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Sumariya, Shrey, Shreyas Rami, Shubham Revadekar, Vidhan Shah i Sudhir Bagul. "Hospital Recommender System". BOHR International Journal of Engineering 2, nr 1 (2023): 1–6. http://dx.doi.org/10.54646/bije.011.
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Elderly patients require more medical effort. It is clear that early-stage disease diagnosis can support timely and appropriate treatment. But if you don’t pay attention in a timely manner, it can lead to different kinds of health problems that can lead to death. Take advantage of our recommendation system to recommend hospitals. A recommender system uses algorithms to provide product or service recommendations to users. By combining blockchain technology and machine learning models, we provide users with highly accurate recommendations. This whitepaper describes how sophisticated machine learning models and blockchain can be connected to improve recommendations, providing hospitals with higher performance and more accurate recommendations. An optimized model for recommending hospitals in a better manner is the main goal behind this paper.
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Sumariya, Shrey, Shreyas Rami Rami, Shubham Revadekar, Vidhan Shah i Sudhir Bagul. "Hospital Recommender System". BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning 1, nr 1 (2022): 99–103. http://dx.doi.org/10.54646/bijiam.016.
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Elderly patients require more medical effort. It is clear that early-stage disease diagnosis can support timely and appropriate treatment. But if you don’t pay attention in a timely manner, it can lead to different kinds of health problems that can lead to death. Take advantage of our recommendation system to recommend hospitals. A recommender system uses algorithms to provide product or service recommendations to users. By combining blockchain technology and machine learning models, we provide users with highly accurate recommendations. This whitepaper describes how sophisticated machine learning models and blockchain can be connected to improve recommendations, providing hospitals with higher performance and more accurate recommendations. An optimized model for recommending hospitals in a better manner is the main goal behind this paper.
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Yuan, Weiwei, i Donghai Guan. "OPTIMIZED TRUST-AWARE RECOMMENDER SYSTEM USING GENETIC ALGORITHM". Neural Network World 27, nr 1 (2017): 77–94. http://dx.doi.org/10.14311/nnw.2017.27.004.
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Verma, Sandhya, i Amit Kumar Manjhvar. "Optimized Ranking Based Recommender System for Various Application Based Fields". International Journal of Database Theory and Application 9, nr 1 (28.02.2016): 137–44. http://dx.doi.org/10.14257/ijdta.2016.9.2.15.
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Li, Jian Yang, Xiao Ping Liu i Rui Li. "Optimized RBF for CBR-Recommendation System". Applied Mechanics and Materials 214 (listopad 2012): 568–72. http://dx.doi.org/10.4028/www.scientific.net/amm.214.568.
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Recommendation systems are widely used in E-commerce to help their customers find products to purchase, with which an important problem is to efficiently search the contents with their demands, and have been attracting attention from quite a few researchers and practitioners from different fields. This paper proposes the CBR-recommender (Case-Based Reasoning) which is a comprehensive expression of human sense, logics and creativity, and can automatically acquire the user’s preferences from the process of adaptation or revision to satisfy the personalized needs; and we deploy radial basis function network (RBF) to control the system scale caused by the large amounts of data with high dimensions, whose performance is also superior with respect to the total time for satisfying a query Our experiments indicate that our mechanism is efficient since it is bounded by the number of neighbors and scalable because no global knowledge is required to be maintained.
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Loukili, Manal, Fayçal Messaoudi i Mohammed El Ghazi. "Machine learning based recommender system for e-commerce". IAES International Journal of Artificial Intelligence (IJ-AI) 12, nr 4 (1.12.2023): 1803. http://dx.doi.org/10.11591/ijai.v12.i4.pp1803-1811.
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<span>Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.</span>
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Herce-Zelaya, Julio, Carlos Porcel, Álvaro Tejeda-Lorente, Juan Bernabé-Moreno i Enrique Herrera-Viedma. "Introducing CSP Dataset: A Dataset Optimized for the Study of the Cold Start Problem in Recommender Systems". Information 14, nr 1 (29.12.2022): 19. http://dx.doi.org/10.3390/info14010019.
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Recommender systems are tools that help users in the decision-making process of choosing items that may be relevant for them among a vast amount of other items. One of the main problems of recommender systems is the cold start problem, which occurs when either new items or new users are added to the system and, therefore, there is no previous information about them. This article presents a multi-source dataset optimized for the study and the alleviation of the cold start problem. This dataset contains info about the users, the items (movies), and ratings with some contextual information. The article also presents an example user behavior-driven algorithm using the introduced dataset for creating recommendations under the cold start situation. In order to create these recommendations, a mixed method using collaborative filtering and user-item classification has been proposed. The results show recommendations with high accuracy and prove the dataset to be a very good asset for future research in the field of recommender systems in general and with the cold start problem in particular.
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Bahrami, N., M. Argany, N. N. Samani i A. R. Vafaeinejad. "DESIGNING A CONTEXT-AWARE RECOMMENDER SYSTEM IN THE OPTIMIZATION OF THE RELIEF AND RESCUE". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (18.10.2019): 171–77. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-171-2019.
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Abstract. The context-aware is the knowledge that leads to better cognition and recognition of the environment, objects and factors, and the way of communication and interactions between them. As a result, it can have a great impact in providing appropriate solutions to various problems. It is possible to integrate consciousness into relief and rescue discussions and to take steps to improve and make realistic solutions. In this study, this issue was addressed in the earthquake crisis, due to a large number of seismic faults in Iran, is one of the major crises in Iran and many parts of the world. Hence, the contexts of rescuers, teams, and environment as the main textures in the above-mentioned issue are investigated and their relationship with each other and the priorities of activities and locations by identifying specialties and the physical and situational conditions of the relief workers, and an algorithm was designed and optimized to optimize the allocation of the relief workers to the affected areas and the necessary activities. Finally, the improvement of the 2.4 fold results of the algorithm and the proposed structure of this research resulted in the ratio of non-use of this algorithm.
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Gupta, Shalini, i Veer Sain Dixit. "A Meta-Heuristic Algorithm Approximating Optimized Recommendations for E-Commerce Business Promotions". International Journal of Information Technology Project Management 11, nr 2 (kwiecień 2020): 23–49. http://dx.doi.org/10.4018/ijitpm.2020040103.
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To provide personalized services such as online-product recommendations, it is usually necessary to model clickstream behavior of users if implicit preferences are taken into account. To accomplish this, web log mining is a promising approach that mines clickstream sessions and depicts frequent sequential paths that a customer follows while browsing e-commerce websites. Strong attributes are identified from the navigation behavior of users. These attributes reflect absolute preference (AP) of the customer towards a product viewed. The preferences are obtained only for the products clicked. These preferences are further refined by calculating the sequential preference (SP) of the user for the products. This paper proposes an intelligent recommender system known as SAPRS (sequential absolute preference-based recommender system) that embed these two approaches that are integrated to improve the quality of recommendation. The performance is evaluated using information retrieval methods. Extensive experiments were carried out to evaluate the proposed approach against state-of-the-art methods.
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Muruganandam, Kishore, i Shaphan Manipaul S. "A Real Time Tourism Recommender System using KNN and RBM Approach". International Journal for Research in Applied Science and Engineering Technology 11, nr 5 (31.05.2023): 357–62. http://dx.doi.org/10.22214/ijraset.2023.51527.
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Abstract: Tourism has become a significant contributor to both the industry and national economy. Over the years, the desire to travel and explore new places has led to a substantial increase in the number of tourists. This boom in tourism has resulted in many businesses, both global and local, as well as governments investing heavily in the industry. However, while governments invest in maintaining and promoting tourist attractions, there is often no emphasis on improving the overall experience of tourists.To address this issue, a real-time recommender system can be implemented. This system will provide tourists with recommendations based on their preferences, rather than just providing them with information. As the environment for recommender systems has become increasingly complex and dynamic, with diverse information available in real-time, it is necessary to develop an effective touring recommender system based on real-time characteristics. This system will provide tourists with real-time recommendations, thus enhancing their experience.To achieve this, a recommendation framework that provides comprehensive information for tourists is needed. This framework will guide tourists from the initial stage of exploring which country to visit, to providing a complete comprehensive guide for them once they arrive at their vacation destination. The goal of this solution is to give tourists the best possible experience, eliminating the need to depend on the help of others.Several features will be implemented in this application, such as comprehensive information on places to stay, dine, and visit, using the current location and budget, popularity, and other characteristics. The system will also create a timetable for tourists based on their duration of stay and provide real-time assistance. Overall, the system will provide a personalized and optimized touring experience for the tourist.With the implementation of this recommender system, tourists will be able to make more informed decisions about where to go, what to do, and where to stay. By using real-time data and personalized recommendations, tourists can enjoy a more engaging and satisfying vacation experience. This system will also benefit the tourism industry as a whole, by increasing customer satisfaction and promoting repeat visits
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Zuo, Fang, Uladzislau Siniauski, Haochen Yang i Guanghui Wang. "OP-K-Means: Optimized Algorithm for Recommendation System Based on User Preferences". Journal of Physics: Conference Series 2171, nr 1 (1.01.2022): 012002. http://dx.doi.org/10.1088/1742-6596/2171/1/012002.
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Abstract For a recommender system (RS), it is difficult to capture all the user’s interest lists simultaneously, which leads to the problem of insufficient performance of the existing joint RS based on the K-Means clustering algorithm. In this paper (1), we introduce a cluster optimization method OP -K-means for user preference data. This method starts with propagation from the center of the user preference data. By selecting relatively distant positions between each initial center, the distance between them is increased as much as possible. (2) Finally, we validate the effectiveness of our algorithm on a dataset from Facebook and compare our algorithm with original K-means. Our experimental results justify the validity of our OP -K-means algorithm.
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Al-Asadi, Ammar Abdulsalam, i Mahdi Nsaif Jasim. "Cluster-based denoising autoencoders for rate prediction recommender systems". Indonesian Journal of Electrical Engineering and Computer Science 30, nr 3 (1.06.2023): 1805. http://dx.doi.org/10.11591/ijeecs.v30.i3.pp1805-1812.
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Recommender system (RS) is a suitable tool for filtering out items and providing the most relevant and suitable items to each user, based on their individual preferences. Deep learning algorithms achieve great success in several fields including RS. The issue with deep learning-based RS models is that, they ignore the differences of users’ preferences, and they build a model based on all the users’ rates. This paper proposed an optimized clustering-based denoising autoencoder model (OCB-DAE) which trains multiple models instead of one, based on users’ preferences using k-means algorithm combined with a nature-inspired algorithm (NIA) such as artificial fish swarm algorithm to determine the optimal initial centroids to cluster the users based on their similar preferences, and each cluster trains its own denoising autoencoder (DAE) model. The results proved that combining NIA with k-means gives better clustering results comparing with using k-means alone. OCB-DAE was trained and tested with MovieLens 1M dataset where 80% of it is used for training and 20% for testing. Root mean squared error (RMSE) score was used to evaluate the performance of the proposed model which was 0.618. It outperformed the other models that use autoencoder and denoising autoencoder without clustering with 38.5% and 29.5% respectively.
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Alabduljabbar, Reham. "Matrix Factorization Collaborative-Based Recommender System for Riyadh Restaurants: Leveraging Machine Learning to Enhance Consumer Choice". Applied Sciences 13, nr 17 (24.08.2023): 9574. http://dx.doi.org/10.3390/app13179574.
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Saudi Arabia’s tourism sector has recently started to play a significant role as an economic driver. The restaurant industry in Riyadh has experienced rapid growth in recent years, making it increasingly challenging for customers to choose from the large number of restaurants available. This paper proposes a matrix factorization collaborative-based recommender system for Riyadh city restaurants. The system leverages user reviews and ratings to predict users’ preferences and recommend restaurants likely to be of interest to them. The system incorporates three different approaches, namely, non-negative matrix factorization (NMF), singular value decomposition (SVD), and optimized singular value decomposition (SVD++). To the best of our knowledge, this is the first recommender system specifically designed for Riyadh restaurants. A comprehensive dataset of restaurants in Riyadh was collected, scraped from Foursquare.com, which includes a wide range of restaurant features and attributes. The dataset is publicly available, enabling other researchers to replicate the experiments and build upon the work. The performance of the system was evaluated using a real-world dataset, and its effectiveness was demonstrated by comparing it to a state-of-the-art recommender system. The evaluation results showed that SVD and NMF are effective methods for generating recommendations, with SVD performing slightly better in terms of RMSE and NMF performing slightly better in terms of MAE. Overall, the findings suggest that the collaborative-based approach using matrix factorization algorithms is an effective way to capture the complex relationships between users and restaurants.
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Ahmed, Esmael, i Adane Letta. "Book Recommendation Using Collaborative Filtering Algorithm". Applied Computational Intelligence and Soft Computing 2023 (11.03.2023): 1–12. http://dx.doi.org/10.1155/2023/1514801.
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The explosive growth in the amount of available digital information in higher education has created a potential challenge of information overload, which hampers timely access to items of interest. The recommender systems are applied in different domains such as recommendations film, tourist advising, webpages, news, songs, and products. But the recommender systems pay less attention to university library services. The most users of university library are students. These users have a lack of ability to search and select the appropriate materials from the large repository that meet for their needs. A lot of work has been done on recommender system, but there are technical gaps observed in existing works such as the problem of constant item list in using web usage mining, decision tree induction, and association rule mining. Besides, it is observed that there is cold start problem in case-based reasoning approach. Therefore, this research work presents matrix factorization collaborative filtering with some performance enhancement to overcome cold start problem. In addition, it presents a comparative study among memory-based and model-based approaches. In this study, researchers used design science research method. The study dataset, 5189 records and 76,888 ratings, was collected from the University of Gondar student information system and online catalogue system. To develop the proposed model, memory-based and model-based approaches have been tested. In memory-based approach, matrix factorization collaborative filtering with some performance enhancements has been implemented. In model-based approach, K-nearest neighbour (KNN) and singular value decomposition (SVD) algorithms are also assessed experimentally. The SVD model is trained on our dataset optimized with a scored RMSE 0.1623 compared to RMSE 0.1991 before the optimization. The RMSE for a KNN model trained using the same dataset was 1.0535. This indicates that the matrix factorization performs better than KNN models in building collaborative filtering recommenders. The proposed SVD-based model accuracy score is 85%. The accuracy score of KNN model is 53%. So, the comparative study indicates that matrix factorization technique, specifically SVD algorithm, outperforms over neighbourhood-based recommenders. Moreover, using hyperparameter tuning with SVD also has an improvement on model performance compared with the existing SVD algorithm.
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Su, Xinjie, Peng Li i Xinru Zhu. "The Influence of Herd Mentality on Rating Bias and Popularity Bias: A Bi-Process Debiasing Recommendation Model Based on Matrix Factorization". Behavioral Sciences 13, nr 1 (10.01.2023): 63. http://dx.doi.org/10.3390/bs13010063.
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To reduce the impact of rating bias and popularity bias in recommender system, and make the recommender system reach a balance between recommendation utility and debias effect at the same time, we propose a bi-process debiasing recommendation model based on matrix factorization. Firstly, considering the problem that the user’s ratings are affected by the herd mentality, which leads to a consistency between the rating and the selection of rating items, resulting in the power-law distribution, the k-times parabolic fuzzy distribution was used to fuse the user’s age to redistribute the ratings. Secondly, the loss function is optimized by the continuously increasing flow and popularity of items. Finally, user emotion and item popularity are combined to construct user psychological tendency, which is divided into three levels: strong, medium and weak, and different levels are given different weights. To verify the performance of the model, the experimental results on real datasets show that the model proposed in this paper not only effectively reduces the recommendation bias but also ensures the recommendation utility.
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Wang, Ruijun. "Spring Festival Holiday Tourism Data Mining Based on the Deep Learning Model". Scientific Programming 2022 (18.06.2022): 1–13. http://dx.doi.org/10.1155/2022/9991794.
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With the advent of the era of big data, people have entered a situation of information overload, how do users filter out the information they need from a large amount of information. When users browse the website, they will record their search or click behavior, and the recommendation system will mine the data based on these data, and recommend the information they need for each user. With the birth of the recommender system, it has indeed changed the way people obtain information. Instead of relying solely on search engines to obtain information, it can obtain the information they want without people’s “consciousness.” This shift has made it easier for people to access information. This paper conducts research on travel recommendation during the Spring Festival holiday. The paper introduces deep learning model and data mining technology, proposes that the recommendation system has three important modules, and obtains the corresponding flowchart. The recommendation system was optimized, and a comparison chart of coverage before and after optimization was obtained. Before optimization, the coverage rate of cities and scenic spots was 45.52% and 21.25%, respectively, and reached 55.65% and 49.81% after optimization.
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Wang, Xixian, Xiaoming Wang, Binrui Huang, Mingzhan Dai i Jianwei Li. "Efficient Personalized Recommendation Based on Federated Learning with Similarity Ciphertext Calculation". Security and Communication Networks 2022 (16.09.2022): 1–15. http://dx.doi.org/10.1155/2022/8607234.
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With the popularity of big data, people get less useful information because of the large amount of data, which makes the Recommender System come into being. However, the privacy and accuracy of the Recommender System still have great challenges. To address these challenges, an efficient personalized recommendation scheme is proposed based on Federated Learning with similarity ciphertext calculation. In this paper, we first design a Similarity calculation algorithm based on Orthogonal Matrix in Ciphertext (SOMC), which can compute the Similarity between users’ demand and Items’ attributes under ciphertext with a low calculation cost. Based on SOMC, we construct an efficient recommendation scheme by employing the Federated Learning framework. The important feature of the proposed approach is improving the accuracy of recommendation while ensuring the privacy of both the users and the Agents. Furthermore, the Agents with good performance are selected according to their Reliability scores to participate in the federal recommendation, so as to further make the accuracy of recommendation better. Under the defined threat model, it is proved that the proposed scheme can meet the privacy requirements of users and Agents. Experiments show that the proposed scheme has optimized accuracy and efficiency compared with existing schemes.
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S, Saranya, i C. Jeyalakshmi. "Collaborative Movie Recommendation System using Enhanced Fuzzy C-Means Clustering with Dove Swarm Optimization Algorithm". ECTI Transactions on Computer and Information Technology (ECTI-CIT) 17, nr 3 (22.07.2023): 308–18. http://dx.doi.org/10.37936/ecti-cit.2023173.251272.
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Recommender Systems (RSs) aid in filtering information seeking to envisage user and item ratings, primarily from huge data to recommend the likes. Movie RSs offer a scheme to help users categorize them based on comparable interests. This enables RSs to be a dominant part of websites and e-commerce applications. This paper proposes an optimized RS for movies, primarily aiming to suggest an RS by clustering data and Computational Intelligence (CI). Unsupervised clustering, a model-based Collaborative Filtering (CF) category, is preferred as it offers simple and practical recommendations. Nevertheless, it involves an increased error rate and consumes more iterations for converging. Enhanced Fuzzy C-Means (EFCM) clustering is proposed to handle these issues. Dove Swarm Optimisation Algorithm (DSOA)-based RS is proposed for optimising Data Points (DPs) in every cluster, providing effcient recommendations. The performance of the proposed EFCM-DSOA-based RS is analysed by performing an experimental study on benchmarked MovieLens Dataset. To ensure the effciency of the proposed EFCM-DSOA-based RS, the outcomes are compared with EFCM-Particle Swarm Optimization (EFCM-PSO) and EFCM-Cuckoo Search (EFCM-CS) based on standard optimization functions. The proposed EFCM-DSOA-based RS offers improved F-measure, Accuracy, and Fitness convergence.
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Vats, Satvik, i B. B. Sagar. "An independent time optimized hybrid infrastructure for big data analytics". Modern Physics Letters B 34, nr 28 (21.07.2020): 2050311. http://dx.doi.org/10.1142/s021798492050311x.
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In Big data domain, platform dependency can alter the behavior of the business. It is because of the different kinds (Structured, Semi-structured and Unstructured) and characteristics of the data. By the traditional infrastructure, different kinds of data cannot be processed simultaneously due to their platform dependency for a particular task. Therefore, the responsibility of selecting suitable tools lies with the user. The variety of data generated by different sources requires the selection of suitable tools without human intervention. Further, these tools also face the limitation of recourses to deal with a large volume of data. This limitation of resources affects the performance of the tools in terms of execution time. Therefore, in this work, we proposed a model in which different data analytics tools share a common infrastructure to provide data independence and resource sharing environment, i.e. the proposed model shares common (Hybrid) Hadoop Distributed File System (HDFS) between three Name-Node (Master Node), three Data-Node and one Client-node, which works under the DeMilitarized zone (DMZ). To realize this model, we have implemented Mahout, R-Hadoop and Splunk sharing a common HDFS. Further using our model, we run [Formula: see text]-means clustering, Naïve Bayes and recommender algorithms on three different datasets, movie rating, newsgroup, and Spam SMS dataset, representing structured, semi-structured and unstructured, respectively. Our model selected the appropriate tool, e.g. Mahout to run on the newsgroup dataset as other tools cannot run on this data. This shows that our model provides data independence. Further results of our proposed model are compared with the legacy (individual) model in terms of execution time and scalability. The improved performance of the proposed model establishes the hypothesis that our model overcomes the limitation of the resources of the legacy model.
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Neupane, Krishna Prasad, Ervine Zheng, Yu Kong i Qi Yu. "A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 7 (28.06.2022): 7868–76. http://dx.doi.org/10.1609/aaai.v36i7.20756.
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We present a novel dynamic recommendation model that focuses on users who have interactions in the past but turn relatively inactive recently. Making effective recommendations to these time-sensitive cold-start users is critical to maintain the user base of a recommender system. Due to the sparse recent interactions, it is challenging to capture these users' current preferences precisely. Solely relying on their historical interactions may also lead to outdated recommendations misaligned with their recent interests. The proposed model leverages historical and current user-item interactions and dynamically factorizes a user's (latent) preference into time-specific and time-evolving representations that jointly affect user behaviors. These latent factors further interact with an optimized item embedding to achieve accurate and timely recommendations. Experiments over real-world data help demonstrate the effectiveness of the proposed time-sensitive cold-start recommendation model.
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Lujak, Marin, Holger Billhardt, Jürgen Dunkel, Alberto Fernández, Ramón Hermoso i Sascha Ossowski. "A distributed architecture for real-time evacuation guidance in large smart buildings". Computer Science and Information Systems 14, nr 1 (2017): 257–82. http://dx.doi.org/10.2298/csis161014002l.
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In this paper, we consider the route coordination problem in emergency evacuation of large smart buildings. The building evacuation time is crucial in saving lives in emergency situations caused by imminent natural or man-made threats and disasters. Conventional approaches to evacuation route coordination are static and predefined. They rely on evacuation plans present only at a limited number of building locations and possibly a trained evacuation personnel to resolve unexpected contingencies. Smart buildings today are equipped with sensory infrastructure that can be used for an autonomous situation-aware evacuation guidance optimized in real time. A system providing such a guidance can help in avoiding additional evacuation casualties due to the flaws of the conventional evacuation approaches. Such a system should be robust and scalable to dynamically adapt to the number of evacuees and the size and safety conditions of a building. In this respect, we propose a distributed route recommender architecture for situation-aware evacuation guidance in smart buildings and describe its key modules in detail. We give an example of its functioning dynamics on a use case.
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Vats, Satvik, Bharat Bhushan Sagar, Karan Singh, Ali Ahmadian i Bruno A. Pansera. "Performance Evaluation of an Independent Time Optimized Infrastructure for Big Data Analytics that Maintains Symmetry". Symmetry 12, nr 8 (2.08.2020): 1274. http://dx.doi.org/10.3390/sym12081274.
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Traditional data analytics tools are designed to deal with the asymmetrical type of data i.e., structured, semi-structured, and unstructured. The diverse behavior of data produced by different sources requires the selection of suitable tools. The restriction of recourses to deal with a huge volume of data is a challenge for these tools, which affects the performances of the tool’s execution time. Therefore, in the present paper, we proposed a time optimization model, shares common HDFS (Hadoop Distributed File System) between three Name-node (Master Node), three Data-node, and one Client-node. These nodes work under the DeMilitarized zone (DMZ) to maintain symmetry. Machine learning jobs are explored from an independent platform to realize this model. In the first node (Name-node 1), Mahout is installed with all machine learning libraries through the maven repositories. The second node (Name-node 2), R connected to Hadoop, is running through the shiny-server. Splunk is configured in the third node (Name-node 3) and is used to analyze the logs. Experiments are performed between the proposed and legacy model to evaluate the response time, execution time, and throughput. K-means clustering, Navies Bayes, and recommender algorithms are run on three different data sets, i.e., movie rating, newsgroup, and Spam SMS data set, representing structured, semi-structured, and unstructured data, respectively. The selection of tools defines data independence, e.g., Newsgroup data set to run on Mahout as others cannot be compatible with this data. It is evident from the outcome of the data that the performance of the proposed model establishes the hypothesis that our model overcomes the limitation of the resources of the legacy model. In addition, the proposed model can process any kind of algorithm on different sets of data, which resides in its native formats.
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Della Corte, Dennis, Wolfgang Colsman, Ben Welker i Brian Rennick. "Library eArchiving with ZONTAL Space and the Allotrope Data Format". Digital Library Perspectives 36, nr 1 (15.01.2020): 69–77. http://dx.doi.org/10.1108/dlp-09-2019-0036.
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Purpose The purpose of this technical paper is to evaluate the emerging standard “Allotrope Data Format (ADF)” in the context of digital preservation at a major US academic library hosted at Brigham Young University. In combination with the new information management system ZONTAL Space (ZS), archiving with the ADF is compared with currently used systems CONTENTdm and ROSETTA. Design/methodology/approach The approach is a workflow-based comparison in terms of usability, functionality and reliability of the systems. Current workflows are replaced by optimized target processes, which limit the number of involved parties and process steps. The connectors or manual solutions between the current workflow steps are replaced with automatic functions inside of ZS. Reporting functionalities inside of ZS are used to track system and file lifecycle to ensure stability and data preservation. Findings The authors find that the target processes leveraging ZS drastically reduce complexity compared to current workflows. Archiving with the ADF is found to decrease integration complexity and provide a more robust data migration path for the future. The possibility to enrich data automatically with metadata and to store this information alongside the content in the same information package increases reusability of the data. Research limitations/implications The practical implications of this work suggest the arrival of a new information management system that can potentially revolutionize the archiving landscape within libraries. Beyond the scope of the initial proof of concept, the potential for the system can be seen to replace existing data management tools and provide access to new data analytics applications, like smart recommender systems. Originality/value The value of this study is a systematic introduction of ZS and the ADF, two emerging solutions from the Pharmaceutical Industry, to the broader audience of digital preservation experts within US libraries. The authors consider the exchange of best practices and solutions between industries to be of high value to the communities.
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Katarya, Rahul, i Om Prakash Verma. "Recommender system with grey wolf optimizer and FCM". Neural Computing and Applications 30, nr 5 (27.12.2016): 1679–87. http://dx.doi.org/10.1007/s00521-016-2817-3.
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Gelvez Garcia, Nancy Yaneth, Jesús Gil-Ruíz i Jhon Fredy Bayona-Navarro. "Optimization of Recommender Systems Using Particle Swarms". Ingeniería 28, Suppl (28.02.2023): e19925. http://dx.doi.org/10.14483/23448393.19925.
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Background: Recommender systems are one of the most widely used technologies by electronic businesses and internet applications as part of their strategies to improve customer experiences and boost sales. Recommender systems aim to suggest content based on its characteristics and on user preferences. The best recommender systems are able to deliver recommendations in the shortest possible time and with the least possible number of errors, which is challenging when working with large volumes of data. Method: This article presents a novel technique to optimize recommender systems using particle swarm algorithms. The objective of the selected genetic algorithm is to find the best hyperparameters that minimize the difference between the expected values and those obtained by the recommender system. Results: The algorithm demonstrates viability given the results obtained, highlighting its simple implementation and the minimal and easily attainable computational resources necessary for its execution. Conclusions: It was possible to develop an algorithm using the most convenient properties of particle swarms in order to optimize recommender systems, thus achieving the ideal behavior for its implementation in the proposed scenario.
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Pajuelo-Holguera, Francisco, Juan A. Gómez-Pulido i Fernando Ortega. "Performance of Two Approaches of Embedded Recommender Systems". Electronics 9, nr 4 (25.03.2020): 546. http://dx.doi.org/10.3390/electronics9040546.
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Nowadays, highly portable and low-energy computing environments require programming applications able to satisfy computing time and energy constraints. Furthermore, collaborative filtering based recommender systems are intelligent systems that use large databases and perform extensive matrix arithmetic calculations. In this research, we present an optimized algorithm and a parallel hardware implementation as good approach for running embedded collaborative filtering applications. To this end, we have considered high-level synthesis programming for reconfigurable hardware technology. The design was tested under environments where usual parameters and real-world datasets were applied, and compared to usual microprocessors running similar implementations. The performance results obtained by the different implementations were analyzed in computing time and energy consumption terms. The main conclusion is that the optimized algorithm is competitive in embedded applications when considering large datasets and parallel implementations based on reconfigurable hardware.
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Anbu, A. "Analyzing the misleading information on Covid-19 using MBCFWS4". Multidisciplinary Science Journal 5, nr 2 (9.04.2023): 2023021. http://dx.doi.org/10.31893/multiscience.2023021.
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The COVID-19 pandemic had a major effect on almost every area of human lives in the majority of the world's countries. Misinformation spreads quickly in the initial phases of the COVID-19 pandemic in different forms, such as fear, distortion, contraindication assumption, and others. False and misleading advertisements harm millions of individuals. In recent research, there are more advanced techniques have been used to address the misinformation about the COVID-19 pandemic. But they are self-reported and probabilistic data during the lockdown period. So, it was difficult to find the respondents' perceptions based on sharing the COVID-19 misinformation. This proposed work analyzed and filtered some optimized factors to analyze the misinformation such as fake reports, fake remedies, conspiracy, susceptibility, vaccine rumors, social media, vitamin D prevents corona, political corona, socio-Economic, and more side effects after getting vaccinated. Collaborative filtering (CF) is the most efficient recommender system, and it is extensively utilized by a broad range of research institutions and enterprises, as well as being used in practice. It consists of two types of CF namely Memory-based CF and Model-based CF. In this work, Memory-based CF recommendation algorithm is combined with a similarity measure called MBCFWS4 to analyze the similarity measure between the factors to conclude the most impact factor. The Primary and secondary dataset helps to identify the respondents' perception based on the COVID-19 misinformation. The efficiency comparison of the proposed work is measured in terms of Precision, Recall & F-measure and found that this analysis using MBCFWS4 is outperforming well than the others as MBCFWS4 predicted accurately and revealed the conclusion based on the COVID-19 misinformation.
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Song, Li Yang, Xiao Ru He i Ji Cheng Zhang. "Optimization of Acid Fracturing Design in Yubei Fractured Reservoir". Applied Mechanics and Materials 580-583 (lipiec 2014): 2495–501. http://dx.doi.org/10.4028/www.scientific.net/amm.580-583.2495.
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This paper recommends the application of acidic fracturing technology for horizontal wells in Yubei region basing on its fractured reservoir’s characteristics, and the numerical simulation method is used to optimize the parameters of acidic fracturing. According to the results of optimization, the proper acid system is selected, and the construction parameters of acidic fracturing are optimized. According to the results of numerical simulation, we recommend the fracture half-length to be 120m, the fracture conductivity to be 30D.cm, and the fracture number to be 5. According to the properties of reservoir in Yubei region and the optimization results of fracture half-length and fracture conductivity, the alternative injection of crosslinked acid and ordinary gelled acid is recommend. The injection rate of crosslinked acid is 5m3/min, with volume 300m3. The injection rate of ordinary gelled acid is 6m3/min, with volume 300m3.
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Zhai, Hai-bin. "Exploiting Post-Click Behaviors for Recommender System". International Journal on Cybernetics & Informatics 11, nr 4 (27.08.2022): 113–23. http://dx.doi.org/10.5121/ijci.2022.110409.
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In recent years, recommender systems become an effective technique to address the information overload problem on the Web, targeting the delivery of most relevant items and information to individual users based on their historical behaviours. To build an effective recommender system, implicit feedback like user click has been harvested. Since click data is usually very noisy, recent works also leverage dwell time as a proxy to optimize user engagement. However, dwell time is just one-dimensional post-click behaviour. The multi-dimensional post-click behaviours, e.g., reading comments, browsing images, and other sub-modules, have not been fully exploited. To address this issue, in this paper, we study leveraging post-click behaviours to improve user engagement in recommendation systems. We first take an E-commerce recommender system as an example to define post-click behaviours and demonstrate their effects with respect to user conversions. Based on the analysis, we then propose a tree-based labelling model, which provides a new perspective to understand user engagement beyond CTR or dwell time. The labelling model can be further incorporated into state-of-the-art recommendation methods. Extensive experiments on the dataset from JD.com, a real-world E-commerce website, demonstrate the competitive performance of the proposed method in both offline and online scenarios.
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Angelis, Sotiris, Konstantinos Kotis i Dimitris Spiliotopoulos. "Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces". Big Data and Cognitive Computing 5, nr 4 (16.12.2021): 80. http://dx.doi.org/10.3390/bdcc5040080.
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Semantic trajectory analytics and personalised recommender systems that enhance user experience are modern research topics that are increasingly getting attention. Semantic trajectories can efficiently model human movement for further analysis and pattern recognition, while personalised recommender systems can adapt to constantly changing user needs and provide meaningful and optimised suggestions. This paper focuses on the investigation of open issues and challenges at the intersection of these two topics, emphasising semantic technologies and machine learning techniques. The goal of this paper is twofold: (a) to critically review related work on semantic trajectories and knowledge-based interactive recommender systems, and (b) to propose a high-level framework, by describing its requirements. The paper presents a system architecture design for the recognition of semantic trajectory patterns and for the inferencing of possible synthesis of visitor trajectories in cultural spaces, such as museums, making suggestions for new trajectories that optimise cultural experiences.
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Song, Yicheng, Nachiketa Sahoo i Elie Ofek. "When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation". Management Science 65, nr 8 (sierpień 2019): 3737–57. http://dx.doi.org/10.1287/mnsc.2018.3127.
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Sometimes we desire change, a break from the same, or an opportunity to fulfill different aspects of our needs. Noting that consumers seek variety, several approaches have been developed to diversify items recommended by personalized recommender systems. However, current diversification strategies operate under a one-shot paradigm without considering the evolution of preferences resulting from recent consumption. Therefore, such methods often sacrifice accuracy. In the context of online media, we show that by recognizing that consumption in a session is the result of a sequence of utility-maximizing selections from various categories, one can increase recommendation accuracy by dynamically tailoring the diversity of suggested items to the diversity sought by the consumer. Our approach is based on a multicategory utility model that captures a consumer’s preference for different categories of content, how quickly the consumer satiates with one category and wishes to substitute it with another, and how the consumer trades off costly search efforts with selecting from a recommended list to discover new content. Taken together, these three elements allow us to characterize how an individual selects a diverse set of items to consume over the course of a session and how likely the individual is to click on recommended content. We estimate the model using a clickstream data set from a large media outlet and apply it to determine the most relevant content to recommend at different stages of an online session. We find that our approach generates recommendations that are on average about 10% more accurate than optimized alternatives and about 25% more accurate than those diversified using existing diversification strategies. Moreover, the proposed method recommends content with diversity that more closely matches the diversity sought by readers, exhibiting lower concentration–diversification bias than other personalized recommender systems. Using a policy simulation, we estimate that recommending content using the proposed approach would result in visitors reading 23% additional articles at the studied website and deriving 35% higher utility. This could lead to immediate gains in revenue for the publisher and longer-term improvements in customer satisfaction and retention at the site. This paper was accepted by Chris Forman, information systems.
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Sun, Ning. "Overview of definition, evaluation, and algorithms of serendipity in recommender systems". Applied and Computational Engineering 6, nr 1 (14.06.2023): 565–71. http://dx.doi.org/10.54254/2755-2721/6/20230861.
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Over time, recommendation systems are playing an important role in an increasingly wide range of areas, such as paper retrieval sites that can recommend papers or books to users, and shopping sites that can recommend products to users. With the development of recommendation systems, there are many different metrics to measure a good recommendation system, including serendipity. This paper summarizes the definition of serendipity, a review of the metrics for measuring serendipity, and several major serendipity-oriented algorithms and presents conjectures for future research on serendipity. Through the research of some papers, for how to delimit and evaluate recommender systems, experts have mostly focused on the unexpected, and most of them use and optimize collaborative filtering algorithms to achieve and improve serendipity.
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Lim, Ying Fei, Su Cheng Haw, Kok Why Ng i Elham Abdulwahab Anaam. "Hybrid-based Recommender System for Online Shopping: A Review". Journal of Engineering Technology and Applied Physics 5, nr 1 (15.03.2023): 12–34. http://dx.doi.org/10.33093/jetap.2023.5.1.3.
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In the era of the digital revolution, online shopping has developed into a remarkably simple and economical option for consumers to make purchases securely and conveniently from their homes. In order for the online merchant to optimize their profit, the online shopping platform must always display a list of potential products that customers may purchase. The recommender system kicks in at this point to assist in finding products that customers would like and recommend a list of product recommendations that match the customer's preferences. This paper reviews the recommender system technology in detail by reviewing the classification technique. Other than that, the related works will be reviewed to understand how each technique works, the strengths and limitations, the datasets and evaluation metrics employed.
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Chen, Shuo, i Min Wu. "Attention Collaborative Autoencoder for Explicit Recommender Systems". Electronics 9, nr 10 (18.10.2020): 1716. http://dx.doi.org/10.3390/electronics9101716.
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Recently, various deep learning-based models have been applied in the study of recommender systems. Some researches have combined the classic collaborative filtering method with deep learning frameworks in order to obtain more accurate recommendations. However, these models either add additional features, but still recommend in the original linear manner, or only extract the global latent factors of the rating matrices in a non-linear way without considering some local special relationships. In this paper, we propose a deep learning framework for explicit recommender systems, named Attention Collaborative Autoencoder (ACAE). Based on the denoising autoencoder, our model can extract the global latent factors in a non-linear fashion from the sparse rating matrices. In ACAE, attention units are introduced during back propagation, enabling discovering potential relationships between users and items in the neighborhood, which makes the model obtain better results in the rating prediction tasks. In addition, we propose how to optimize the training process of the model by proposing a new loss function. Experiments on two public datasets demonstrate the effectiveness of ACAE and its outperformance of competitive baselines.
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Yao, ZiXi. "Review of Movie Recommender Systems Based on Deep Learning". SHS Web of Conferences 159 (2023): 02010. http://dx.doi.org/10.1051/shsconf/202315902010.
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With the development of the network, society has moved into the data era, and the amount of data is exploding, we need a tool to help users find corresponding data collections based on their interests, and recommender systems were born for this purpose. In the movie field, recommender systems suggest items that users may like, improving the efficiency of finding movies and optimizing the user experience thus driving the growth of the movie industry. Machine learning is a multi-disciplinary science that focuses on how to improve the performance of algorithms by continuously reorganizing existing knowledge structures in a way that mimics human learning. Deep learning is a research direction in the field of machine learning that has achieved results in many areas that far surpass previous related techniques. In order to better provide personalized services to users and improve the accuracy of the system’s recommendations, it is necessary to integrate deep learning techniques into the recommender system to optimize the system’s performance. In this paper, we review different approaches in deep learning based recommender systems.
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Chen, Keyu, i Shiliang Sun. "CP-Rec: Contextual Prompting for Conversational Recommender Systems". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 11 (26.06.2023): 12635–43. http://dx.doi.org/10.1609/aaai.v37i11.26487.
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The conversational recommender system (CRS) aims to provide high-quality recommendations through interactive dialogues. However, previous CRS models have no effective mechanisms for task planning and topic elaboration, and thus they hardly maintain coherence in multi-task recommendation dialogues. Inspired by recent advances in prompt-based learning, we propose a novel contextual prompting framework for dialogue management, which optimizes prompts based on context, topics, and user profiles. Specifically, we develop a topic controller to sequentially plan the subtasks, and a prompt search module to construct context-aware prompts. We further adopt external knowledge to enrich user profiles and make knowledge-aware recommendations. Incorporating these techniques, we propose a conversational recommender system with contextual prompting, namely CP-Rec. Experimental results demonstrate that it achieves state-of-the-art recommendation accuracy and generates more coherent and informative conversations.
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Kim, Minseok, Hwanjun Song, Yooju Shin, Dongmin Park, Kijung Shin i Jae-Gil Lee. "Meta-Learning for Online Update of Recommender Systems". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 4 (28.06.2022): 4065–74. http://dx.doi.org/10.1609/aaai.v36i4.20324.
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Online recommender systems should be always aligned with users' current interest to accurately suggest items that each user would like. Since user interest usually evolves over time, the update strategy should be flexible to quickly catch users' current interest from continuously generated new user-item interactions. Existing update strategies focus either on the importance of each user-item interaction or the learning rate for each recommender parameter, but such one-directional flexibility is insufficient to adapt to varying relationships between interactions and parameters. In this paper, we propose MeLON, a meta-learning based novel online recommender update strategy that supports two-directional flexibility. It is featured with an adaptive learning rate for each parameter-interaction pair for inducing a recommender to quickly learn users' up-to-date interest. The procedure of MeLON is optimized following a meta-learning approach: it learns how a recommender learns to generate the optimal learning rates for future updates. Specifically, MeLON first enriches the meaning of each interaction based on previous interactions and identifies the role of each parameter for the interaction; and then combines these two pieces of information to generate an adaptive learning rate. Theoretical analysis and extensive evaluation on three real-world online recommender datasets validate the effectiveness of MeLON.
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Quest, Gemina, Rosalie Arendt, Christian Klemm, Vanessa Bach, Janik Budde, Peter Vennemann i Matthias Finkbeiner. "Integrated Life Cycle Assessment (LCA) of Power and Heat Supply for a Neighborhood: A Case Study of Herne, Germany". Energies 15, nr 16 (15.08.2022): 5900. http://dx.doi.org/10.3390/en15165900.
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(1) The use of renewable energy for power and heat supply is one of the strategies to reduce greenhouse gas emissions. As only 14% of German households are supplied with renewable energy, a shift is necessary. This shift should be realized with the lowest possible environmental impact. This paper assesses the environmental impacts of changes in energy generation and distribution, by integrating the life cycle assessment (LCA) method into energy system models (ESM). (2) The integrated LCA is applied to a case study of the German neighborhood of Herne, (i) to optimize the energy supply, considering different technologies, and (ii) to determine the environmental impacts of the base case (status quo), a cost-optimized scenario, and a CO2-optimized scenario. (3) The use of gas boilers in the base case is substituted with CHPs, surface water heat pumps and PV-systems in the CO2-optimized scenario, and five ground-coupled heat pumps and PV-systems for the cost-optimized scenario. This technology shift led to a reduction in greenhouse gas emissions of almost 40% in the cost-optimized, and more than 50% in the CO2-optimized, scenario. However, technology shifts, e.g., due to oversized battery storage, risk higher impacts in other categories, such as terrestrial eco toxicity, by around 22%. Thus, it can be recommended to use smaller battery storage systems. (4) By combining ESM and LCA, additional environmental impacts beyond GHG emissions can be quantified, and therefore trade-offs between environmental impacts can be identified. Furthermore, only applying ESM leads to an underestimation of greenhouse gas emissions of around 10%. However, combining ESM and LCA required significant effort and is not yet possible using an integrated software.
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Rizkallah, Sandra, Amir F. Atiya i Samir Shaheen. "New Vector-Space Embeddings for Recommender Systems". Applied Sciences 11, nr 14 (13.07.2021): 6477. http://dx.doi.org/10.3390/app11146477.
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In this work, we propose a novel recommender system model based on a technology commonly used in natural language processing called word vector embedding. In this technology, a word is represented by a vector that is embedded in an n-dimensional space. The distance between two vectors expresses the level of similarity/dissimilarity of their underlying words. Since item similarities and user similarities are the basis of designing a successful collaborative filtering, vector embedding seems to be a good candidate. As opposed to words, we propose a vector embedding approach for learning vectors for items and users. There have been very few recent applications of vector embeddings in recommender systems, but they have limitations in the type of formulations that are applicable. We propose a novel vector embedding that is versatile, in the sense that it is applicable for the prediction of ratings and for the recommendation of top items that are likely to appeal to users. It could also possibly take into account content-based features and demographic information. The approach is a simple relaxation algorithm that optimizes an objective function, defined based on target users’, items’ or joint user–item’s similarities in their respective vector spaces. The proposed approach is evaluated using real life datasets such as “MovieLens”, “ModCloth”, “Amazon: Magazine_Subscriptions” and “Online Retail”. The obtained results are compared with some of the leading benchmark methods, and they show a competitive performance.
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Gallacher, D. J. "Optimised descriptors recommended for Australian sugarcane germplasm (Saccharum spp. hybrid)". Australian Journal of Agricultural Research 48, nr 6 (1997): 775. http://dx.doi.org/10.1071/a96106.
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No internationally accepted system of describing sugarcane cultivars has ever emerged, despite repeated attempts. An internationally adopted descriptor set would enable cultivar label maintenance both within and among research stations, and enable an international measure of genetic distance. An optimum morphological descriptor set was formulated after an exhaustive study of characters. Characters were selected using both a monothetic and a polythetic discriminant analysis, and from a study of character variance components. Twenty-nine vegetative characters are recommended for adoption and general use. A further 22 reproductive characters are recommended for secondary use. The latter are descriptive, but less practical as flowering rate is often low. These morphological characters are compared with molecular marker studies in the crop.
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Mu, Shanlei, Yaliang Li, Wayne Xin Zhao, Siqing Li i Ji-Rong Wen. "Knowledge-Guided Disentangled Representation Learning for Recommender Systems". ACM Transactions on Information Systems 40, nr 1 (31.01.2022): 1–26. http://dx.doi.org/10.1145/3464304.
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In recommender systems, it is essential to understand the underlying factors that affect user-item interaction. Recently, several studies have utilized disentangled representation learning to discover such hidden factors from user-item interaction data, which shows promising results. However, without any external guidance signal, the learned disentangled representations lack clear meanings, and are easy to suffer from the data sparsity issue. In light of these challenges, we study how to leverage knowledge graph (KG) to guide the disentangled representation learning in recommender systems. The purpose for incorporating KG is twofold, making the disentangled representations interpretable and resolving data sparsity issue. However, it is not straightforward to incorporate KG for improving disentangled representations, because KG has very different data characteristics compared with user-item interactions. We propose a novel K nowledge-guided D isentangled R epresentations approach ( KDR ) to utilizing KG to guide the disentangled representation learning in recommender systems. The basic idea, is to first learn more interpretable disentangled dimensions (explicit disentangled representations) based on structural KG, and then align implicit disentangled representations learned from user-item interaction with the explicit disentangled representations. We design a novel alignment strategy based on mutual information maximization. It enables the KG information to guide the implicit disentangled representation learning, and such learned disentangled representations will correspond to semantic information derived from KG. Finally, the fused disentangled representations are optimized to improve the recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model in terms of both performance and interpretability.
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Liu, Lewis, i Kun Zhao. "Asynchronous Stochastic Gradient Descent for Extreme-Scale Recommender Systems". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 1 (18.05.2021): 328–35. http://dx.doi.org/10.1609/aaai.v35i1.16108.
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Recommender systems are influential for many internet applications. As the size of the dataset provided for a recommendation model grows rapidly, how to utilize such amount of data effectively matters a lot. For a typical Click-Through-Rate(CTR) prediction model, the amount of daily samples can probably be up to hundreds of terabytes, which reaches dozens of petabytes at an extreme-scale when we take several days into consideration. Such data makes it essential to train the model parallelly and continuously. Traditional asynchronous stochastic gradient descent (ASGD) and its variants are proved efficient but often suffer from stale gradients. Hence, the model convergence tends to be worse as more workers are used. Moreover, the existing adaptive optimizers, which are friendly to sparse data, stagger in long-term training due to the significant imbalance between new and accumulated gradients. To address the challenges posed by extreme-scale data, we propose: 1) Staleness normalization and data normalization to eliminate the turbulence of stale gradients when training asynchronously in hundreds and thousands of workers; 2) SWAP, a novel framework for adaptive optimizers to balance the new and historical gradients by taking sampling period into consideration. We implement these approaches in TensorFlow and apply them to CTR tasks in real-world e- commerce scenarios. Experiments show that the number of workers in asynchronous training can be extended to 3000 with guaranteed convergence, and the final AUC is improved by more than 5 percentage.
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Pasdar, Amirmohammad, Young Choon Lee, Tahereh Hassanzadeh i Khaled Almi’ani. "Resource Recommender for Cloud-Edge Engineering". Information 12, nr 6 (25.05.2021): 224. http://dx.doi.org/10.3390/info12060224.
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The interaction between artificial intelligence (AI), edge, and cloud is a fast-evolving realm in which pushing computation close to the data sources is increasingly adopted. Captured data may be processed locally (i.e., on the edge) or remotely in the clouds where abundant resources are available. While many emerging applications are processed in situ due primarily to their data intensiveness and short-latency requirement, the capacity of edge resources remains limited. As a result, the collaborative use of edge and cloud resources is of great practical importance. Such collaborative use should take into account data privacy, high latency and high bandwidth consumption, and the cost of cloud usage. In this paper, we address the problem of resource allocation for data processing jobs in the edge-cloud environment to optimize cost efficiency. To this end, we develop Cost Efficient Cloud Bursting Scheduler and Recommender (CECBS-R) as an AI-assisted resource allocation framework. In particular, CECBS-R incorporates machine learning techniques such as multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks. In addition to preserving privacy due to employing edge resources, the edge utility cost plus public cloud billing cycles are adopted for scheduling, and jobs are profiled in the cloud-edge environment to facilitate scheduling through resource recommendations. These recommendations are outputted by the MLP neural network and LSTM for runtime estimation and resource recommendation, respectively. CECBS-R is trained with the scheduling outputs of Facebook and grid workload traces. The experimental results based on unseen workloads show that CECBS-R recommendations achieve a ∼65% cost saving in comparison to an online cost-efficient scheduler (BOS), resource management service (RMS), and an adaptive scheduling algorithm with QoS satisfaction (AsQ).
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Rockson, Seth Bedu, Madihah Md Rasid, Mohd Shafiq Anuar, Siti Maherah Hussin, Norzanah Rosmin, Norjulia Mohamad Nordin i Michael Gyan. "DESIGNING TECHNO-ECONOMIC OFF-GRID PHOTOVOLTAIC SYSTEM USING AN IMPROVED DIFFERENTIAL EVOLUTION ALGORITHM". Jurnal Teknologi 85, nr 4 (25.06.2023): 153–65. http://dx.doi.org/10.11113/jurnalteknologi.v85.18334.
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Conventional power generation is one of the main contributors to the phenomenon of the greenhouse effect. This has led to a diversification of electricity sources including environmentally friendly energy sources such as solar energy. Off-grid PV systems have gained some traction due to their cost-effectiveness for rural communities. However, the intermittent nature of solar is the main challenge to developing the off-grid PV system. Moreover, the high capital cost of PV systems as well as the storage batteries becomes the main concern for all PV users. Thus, this study aims to optimize the size of the PV system and battery simultaneously and design a cost-effective off-grid photovoltaic system considering various aspects such as battery power, solar irradiance, and PV panel selection while ensuring system reliability. The proposed system was optimized using improved Differential Evolution (DE) and its effectiveness was tested by comparing the results with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The Improved DE algorithm provides the highest average cost savings compared to other algorithms, which is $500 per year. It is recommended that this method is very useful in the optimization of off-grid PV systems, considering other uncertainties that affect PV system performance.
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Manikandan, B., P. Rama i S. Chakaravarthi. "An automatic product recommendation system in e-commerce using Flamingo Search Optimizer and Fuzzy Temporal Multi Neural Classifier". Journal of Autonomous Intelligence 6, nr 2 (4.08.2023): 568. http://dx.doi.org/10.32629/jai.v6i2.568.
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<p>In this paper, a new automatic product recommendation system (APRS) is proposed to recommend the suitable products to the customer in e-commerce by analyzing the customers’ reviews. This recommendation system applies semantic aware data preprocessing, feature selection and extraction and classification. The initial level data preprocessing including blank space and stop word removal. Moreover, we use a Flamingo Search Optimizer (FSO) for optimizing the features that are extracted in the initial level data preprocessing. In addition, a new Fuzzy Temporal Multi Neural Classification Algorithm (FTMNCA) is proposed for performing effective classification that is helpful to make effective decision on prediction process. In addition, the proposed automatic product recommendation system recommends the suitable products to the customers according to the classification result. Finally, the proposed system is evaluated by conducting various experiments and proved as superior than the available systems in terms of prediction accuracy, precision, recall and f-measure.</p>
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Mustika, Hani Febri, i Aina Musdholifah. "Book Recommender System Using Genetic Algorithm and Association Rule Mining". Computer Engineering and Applications Journal 8, nr 2 (11.06.2019): 85–92. http://dx.doi.org/10.18495/comengapp.v8i2.305.
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Recommender system aims to provide on something that likely most suitable and attractive for users. Many researches on the book recommender system for library have already been done. One of them used association rule mining. However, the system was not optimal in providing recommendations that appropriate to the user's preferences and achieving the goal of recommender system. This research proposed a book recommender system for the library that optimizes association rule mining using genetic algorithm. Data used in this research has taken from Yogyakarta City Library during 2015 until 2016. The experimental results of the association rule mining study show that 0.01 for the greatest value of minimum support and 0.4359 for the average confidence value due to a lot of data and uneven distribution of data. Furthermore, other results are 0.499471 for the average of Laplace value, 30.7527 for the average of lift value and 1.91534252 for the average of conviction value, which those values indicate that rules have good enough level of confidence, quite interesting and dependent which indicates existing relation between antecedent and consequent. Optimization using genetic algorithm requires longer execution time, but it was able to produce book recommendations better than only using association rule mining. In Addition, the system got 77.5% for achieving the goal of recommender system, namely relevance, novelty, serendipity and increasing recommendation diversity.
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Assad, Ussama, Muhammad Arshad Shehzad Hassan, Umar Farooq, Asif Kabir, Muhammad Zeeshan Khan, S. Sabahat H. Bukhari, Zain ul Abidin Jaffri, Judit Oláh i József Popp. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods". Energies 15, nr 6 (9.03.2022): 2003. http://dx.doi.org/10.3390/en15062003.
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In view of scarcity of traditional energy resources and environmental issues, renewable energy resources (RERs) are introduced to fulfill the electricity requirement of growing world. Moreover, the effective utilization of RERs to fulfill the varying electricity demands of customers can be achieved via demand response (DR). Furthermore, control techniques, decision variables and offered motivations are the ways to introduce DR into distribution network (DN). This categorization needs to be optimized to balance the supply and demand in DN. Therefore, intelligent algorithms are employed to achieve optimized DR. However, these algorithms are computationally restrained to handle the parametric load of uncertainty involved with RERs and power system. Henceforth, this paper focuses on the limitations of intelligent algorithms for DR. Furthermore, a comparative study of different intelligent algorithms for DR is discussed. Based on conclusions, quantum algorithms are recommended to optimize the computational burden for DR in future smart grid.
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Son, Juyeon, Wonyoung Choi i Sang-Min Choi. "Trust information network in social Internet of things using trust-aware recommender systems". International Journal of Distributed Sensor Networks 16, nr 4 (kwiecień 2020): 155014772090877. http://dx.doi.org/10.1177/1550147720908773.
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Social Internet of things is one of the most up-to-date research issues in the applications of Internet of things technologies. In social Internet of things, accuracy and reliability are standard features to discerning decisions. We assume that decision support systems based on social Internet of things could leverage research from recommender systems to achieve more stable performance. Therefore, we propose a trust-aware recommender systems suitable for social Internet of things. Trust-aware recommender systems adapt the concept of social networking service and utilize social interaction information. Trust information not only improves recommender systems from opinion spam problems but also more accurately predicts users’ preferences. We confirm that the performance of a recommender system becomes more improved when implicit trust is able to satisfy the properties of trust in the social Internet of things environment. The structure and amount of social link information are context-sensitive, so applying the concept of trust into social Internet of things environments requires a method to optimize implicit and explicit trust with minimal social link information. Our proposed method configures an asymmetric implicit trust network utilizing user–item rating matrix and transforms trust propagation metrics for a directional and weighted trust network. Through experiments, we confirm that the proposed methods enable higher accuracy and wider coverage compared to the existing recommendation methods.
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Popa, Alina. "DESIGNING A HOLISTIC ADAPTIVE RECOMMENDER SYSTEM (HARS) FOR CUSTOMER RELATIONSHIP DEVELOPMENT: A CONCEPTUAL FRAMEWORK". Journal of Social Sciences IV, nr 2 (maj 2021): 84–97. http://dx.doi.org/10.52326/jss.utm.2021.4(2).09.
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With the recent COVID-19 pandemic, the world we knew changed significantly. The buying behavior shifted as well and is reflected by a growing transition to online interaction, higher media consumption and massive turn to online shopping. Companies that aim to remain top of mind to customers should ensure that their way of interacting with user is both relevant and highly adaptive. Companies should invest in state-of-the-art technologies that help manage and optimize the relationship with the client based on both online and offline data. One of the most popular applications that companies use to develop the client relationship is a Recommender System. The vast majority of traditional recommender systems consider the recommendation as a static procedure and focus either on a specific type of recommendation or on some limited data. In this paper, it is proposed a novel Reinforcement Learning-based recommender system that has an integrative view over data and recommendation landscape, as well as it is highly adaptive to changes in customer behavior, the Holistic Adaptive Recommender System (HARS). From system design to detailed activities, it was attempted to present a comprehensive way of designing and developing a HARS system for an e-commerce company use-case as well as giving a suite of metrics that could be used for its evaluation.
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Zhang, Liang, Quanshen Wei, Lei Zhang, Baojiao Wang i Wen-Hsien Ho. "Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems". Applied Sciences 10, nr 4 (13.02.2020): 1257. http://dx.doi.org/10.3390/app10041257.
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Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.