Journal articles: 'RECOMMENDATION TECHNIQUES'

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Dondekar, Anupama D., and Balwant A. Sonkamble. "Tag Recommendation Techniques for Images: A Survey." International Journal of Signal Processing Systems 5, no. 4 (December 2017): 116–22. http://dx.doi.org/10.18178/ijsps.5.4.116-122.

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Gorli, Ravi, and Bagusetty Ajay Ram. "MRML-Movie Recommendation Model with Machine Learning Techniques." International Journal of Science and Research (IJSR) 12, no. 5 (May 5, 2023): 298–302. http://dx.doi.org/10.21275/sr23322101301.

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TR, Mahesh, and V Vinoth Kumar. "Clustering Techniques for Recommendation of Movies." International Journal of Data Informatics and Intelligent Computing 1, no. 2 (December 21, 2022): 16–22. http://dx.doi.org/10.59461/ijdiic.v1i2.17.

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A recommendation system employs a variety of algorithms to provide users with recommendations of any kind. The most well-known technique, collaborative filtering, involves users with similar preferences although it is not always as effective when dealing with large amounts of data. Improvements to this approach are required as the dataset size increases. Here, in our suggested method, we combine a hierarchical clustering methodology with a collaborative filtering algorithm for making recommendations. Additionally, the Principle Component Analysis (PCA) method is used to condense the dimensions of the data to improve the accuracy of the outcomes. The dataset will receive additional benefits from the clustering technique when using hierarchical clustering, and the PCA will help redefine the dataset by reducing its dimensionality as needed. The primary elements utilized for recommendations can be enhanced by applying the key elements of these two strategies to the conventional collaborative filtering recommendation algorithm. The suggested method will unquestionably improve the precision of the findings received from the conventional CFRA and significantly increase the effectiveness of the recommendation system. The total findings will be applied to the combined dataset of TMDB and Movie Lens, which is utilized to suggest movies to the user in accordance with the rating patterns that each individual user has generated.

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Tewari, Anand Shanker, and Asim Gopal Barman. "Sequencing of items in personalized recommendations using multiple recommendation techniques." Expert Systems with Applications 97 (May 2018): 70–82. http://dx.doi.org/10.1016/j.eswa.2017.12.019.

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Al-Absi, Mohammed Abdulhakim, and Hind R’bigui. "Process Discovery Techniques Recommendation Framework." Electronics 12, no. 14 (July 17, 2023): 3108. http://dx.doi.org/10.3390/electronics12143108.

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In a competitive environment, organizations need to continuously understand, analyze and improve the behavior of processes to maintain their position in the market. Process mining is a set of techniques that allows organizations to have an X-ray view of their processes by extracting process related knowledge from the information recorded in today’s process aware information systems such as ‘Enterprise Resource Planning’ systems, ‘Business Process Management’ systems, ‘Supply Chain Management’ systems, etc. One of the major categories of process mining techniques is the process of discovery. This later allows for automatically constructing process models just from the information stored in the system representing the real behavior of the process discovered. Many process discovery algorithms have been proposed today which made users and businesses, in front of many techniques, unable to choose or decide the appropriate mining algorithm for their business processes. Moreover, existing evaluation and recommendation frameworks have several important drawbacks. This paper proposes a new framework for recommending the most suitable process discovery technique to a given process taking into consideration the limitations of existing frameworks.

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Gaurkhede, Miss Pratiksha P. "Review Paper on various Recommendation Techniques of Friends Recommendation System." International Journal for Research in Applied Science and Engineering Technology 9, no. 4 (April 30, 2021): 894–97. http://dx.doi.org/10.22214/ijraset.2021.33770.

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Nazema, Syeda. "A Survey on Feature Recommendation Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 3 (2015): 1662–68. http://dx.doi.org/10.17762/ijritcc2321-8169.1503167.

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Ko, Hyeyoung, Suyeon Lee, Yoonseo Park, and Anna Choi. "A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields." Electronics 11, no. 1 (January 3, 2022): 141. http://dx.doi.org/10.3390/electronics11010141.

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This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied.

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Das, Joydeep, Subhashis Majumder, and Kalyani Mali. "Clustering Techniques to Improve Scalability and Accuracy of Recommender Systems." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 04 (August 2021): 621–51. http://dx.doi.org/10.1142/s0218488521500276.

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Recommender systems have emerged as a class of essential tools in the success of modern e-commerce applications. These applications typically handle large datasets and often face challenges like data sparsity and scalability. Clustering techniques help to reduce the computational time needed for recommendation as well as handle the sparsity problem more efficiently. Traditional clustering based recommender systems create partitions (clusters) of the user-item rating matrix and execute the recommendation algorithm in the clusters separately in order to decrease the overall runtime of the system. Each user or item generally belong to at most one cluster. However, it may so happen that some users (boundary users) present in a particular cluster exhibit higher similarity with the preferences of the users residing in the nearby clusters than the ones present in their own cluster. Therefore, we propose a clustering based scalable recommendation algorithm that has a provision for switching a user from its original cluster to another cluster in order to provide more accurate recommendations. For a user belonging to multiple clusters, we aggregate recommendations from those clusters to which the user belongs in order to produce the final set of recommendations to that user. In this work, we propose two types of clustering, one on the basis of rating and the other on the basis of frequency and then compare their performances. Finally, we explore the applicability of cluster ensembles techniques in the proposed method. Our aim is to develop a recommendation framework that can scale well to handle large datasets without much affecting the recommendation quality. The outcomes of our experiments clearly demonstrate the scalability as well as efficacy of our method. It reduces the runtime of the baseline CF algorithm by a minimum of 58% and a maximum of 90% for MovieLens-10M dataset, and a minimum of 42% and a maximum of 84% for MovieLens-20M dataset. The accuracies of recommendations in terms of F1, MAP and NDCG metrics are also better than the existing clustering based recommender systems.

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Kumar, Praveen, Mukesh Kumar Gupta, Channapragada Rama Seshagiri Rao, M. Bhavsingh, and M. Srilakshmi. "A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (March 11, 2023): 184–92. http://dx.doi.org/10.17762/ijritcc.v11i3s.6180.

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Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recommendation systems, hybrid algorithms that combine CF and content-based filtering techniques have been developed. These hybrid systems leverage the strengths of both approaches to provide more accurate and personalized recommendations. In conclusion, collaborative filtering is an essential technique in recommendation systems, and the use of various similarity metrics and hybrid techniques can enhance the quality of recommendations.

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Shargunam, S., and G. Rajakumar. "Filtering Techniques in Recommendation Systems: A Review." Asian Journal of Science and Applied Technology 10, no. 2 (November 5, 2021): 22–25. http://dx.doi.org/10.51983/ajsat-2021.10.2.3059.

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Recommendation systems are not new to the world, they have rapidly become prevalent, appearing in almost every type of technology on a daily basis. As a result, recommendation systems were necessary to reduce the amount of time spent looking for the best and most essential items. Information filtering, user personalization, collaborative filtering, and hybrid filtering are just some of the ways used by recommendation systems in diversion, streaming, software, and other areas to present users and customers with customized content and products. The various filtering methods are compared and analyzed in order to improve the accuracy and quality of the recommendation system.

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Sofikitis, Evangelos, and Christos Makris. "Development of recommendation systems using game theoretic techniques." Computer Science and Information Systems, no. 00 (2022): 18. http://dx.doi.org/10.2298/csis210925018s.

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In the present work, we inquire the use of game theoretic techniques for the development of recommender systems. Initially, the interaction of the two aspects of the systems, query reformulation and relevance estimation, is modelled as a cooperative game where the two players have a common utility, to supply optimal recommendations, which they try to maximize. Based on this modelling, three basic recommendation methods are developed, namely collaborative filtering, content based filtering and demographic filtering. The different methods are then combined to create hybrid systems. In the weighted combination, the use of game theoretic techniques is extended, as it is modelled as a cooperative game. Finally, the methods are combined with the use of a genetic algorithm where game theory is used for the parent selection process. Our work offers a baseline for the efficient combination of recommendation methods through game theory and in addition the novelty method, Choice by Game, for the parent selection process in genetic algorithms which offers consistent performance improvements.

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Jajoo, Palika, and Dolly Mittal. "A Review on Techniques of Recommendation System." SKIT Research Journal 11, no. 2 (January 11, 2021): 31. http://dx.doi.org/10.47904/ijskit.11.2.2021.31-36.

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Lu, Jinliang. "A Survey of Online Course Recommendation Techniques." Open Journal of Applied Sciences 12, no. 01 (2022): 134–54. http://dx.doi.org/10.4236/ojapps.2022.121010.

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Behl, Rachna, and Indu Kashyap. "Locus recommendation using probabilistic matrix factorization techniques." Ingeniería Solidaria 17, no. 1 (January 11, 2021): 1–25. http://dx.doi.org/10.16925/2357-6014.2021.01.10.

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Introduction: The present paper is the outcome of the research “Locus Recommendation using Probabilistic Matrix Factorization Techniques” carried out in Manav Rachna International Institute of Research and Studies, India in the year 2019-20. Methodology: Matrix factorization is a model-based collaborative technique for recommending new items to the users. Results: Experimental results on two real-world LBSNs showed that PFM consistently outperforms PMF. This is because the technique is based on gamma distribution to the model user and item matrix. Using gamma distribution is reasonable for check-in frequencies which are all positive in real datasets. However, PMF is based on Gaussian distribution that can allow negative frequency values as well. Conclusion: The motive of the work is to identify the best technique for recommending locations with the highest accuracy and allow users to choose from a plethora of available locations; the best and interesting location based on the individual’s profile. Originality: A rigorous analysis of Probabilistic Matrix Factorization techniques has been performed on popular LBSNs and the best technique for location recommendation has been identified by comparing the accuracy viz RMSE, Precision@N, Recall@N, F1@N of different models. Limitations: User’s contextual information like demographics, social and geographical preferences have not been considered while evaluating the efficiency of probabilistic matrix factorization techniques for POI Recommendations.

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Adomavicius, Gediminas, and YoungOk Kwon. "New Recommendation Techniques for Multicriteria Rating Systems." IEEE Intelligent Systems 22, no. 3 (May 2007): 48–55. http://dx.doi.org/10.1109/mis.2007.58.

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Mahmood, Wisam Alnadem, LaythKamil Almajmaie, Ahmed Raad Raheem, and Saad Albawi. "A hybrid approach towards movie recommendation system with collaborative filtering and association rule mining." Acta Scientiarum. Technology 44 (March 11, 2022): e58925. http://dx.doi.org/10.4025/actascitechnol.v44i1.58925.

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There is a huge information stockpile available on the internet. But the available information still throws a stiff challenge to users while selecting the needed information. Such an issue can be solved by applying information filtering for locating the required information through a Recommender System. While using a RS, the users find it easy to curate and collect relevant information out of massive databanks. Though various types of RS are currently available, yet the RS developed by Collaborative Filtering techniques has proven to be the most suitable for many problems. Among the various Recommended Systems available, movie recommendation system is the most widely used one. In this system, the recommendations will be made based on the similarities in the characteristics as exhibited by users / items. The movie recommendation system contains a huge list of user objects and item objects. This paper combines Collaborative Filtering Technique with association rules mining for better compatibility and assurance while delivering better recommendations. Hence, in the process, the produced recommendations can be considered as strong recommendations. The hybridization involving both collaborative filtering and association rules mining can provide strong, high-quality recommendations, even when enough data is unavailable. This article combines various recommendations for creating a movie recommendation system by using common filtering techniques and data mining techniques

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O'DONOVAN, JOHN, and BARRY SMYTH. "MINING TRUST VALUES FROM RECOMMENDATION ERRORS." International Journal on Artificial Intelligence Tools 15, no. 06 (December 2006): 945–62. http://dx.doi.org/10.1142/s0218213006003053.

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Increasing availability of information has furthered the need for recommender systems across a variety of domains. These systems are designed to tailor each user's information space to suit their particular information needs. Collaborative filtering is a successful and popular technique for producing recommendations based on similarities in users' tastes and opinions. Our work focusses on these similarities and the fact that current techniques for defining which users contribute to recommendation are in need of improvement. In this paper we propose the use of trustworthiness as an improvement to this situation. In particular, we define and empirically test a technique for eliciting trust values for each producer of a recommendation based on that user's history of contributions to recommendations. We compute a recommendation range to present to a target user. This is done by leveraging under/overestimate errors in users' past contributions in the recommendation process. We present three different models to compute this range. Our evaluation shows how this trust-based technique can be easily incorporated into a standard collaborative filtering algorithm and we define a fair comparison in which our technique outperforms a benchmark algorithm in predictive accuracy. We aim to show that the presentation of absolute rating predictions to users is more likely to reduce user trust in the recommendation system than presentation of a range of rating predictions. To evaluate the trust benefits resulting from the transparency of our recommendation range techniques, we carry out user-satisfaction trials on BoozerChoozer, a pub recommendation system. Our user-satisfaction results show that the recommendation range techniques perform up to twice as well as the benchmark.

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Huang, Xiao, Pengjie Ren, Zhaochun Ren, Fei Sun, Xiangnan He, Dawei Yin, and Maarten de Rijke. "Report on the international workshop on natural language processing for recommendations (NLP4REC 2020) workshop held at WSDM 2020." ACM SIGIR Forum 54, no. 1 (June 2020): 1–5. http://dx.doi.org/10.1145/3451964.3451970.

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This paper summarizes the outcomes of the International Workshop on Natural Language Processing for Recommendations (NLP4REC 2020), held in Houston, USA, on February 7, 2020, during WSDM 2020. The purpose of this workshop was to explore the potential research topics and industrial applications in leveraging natural language processing techniques to tackle the challenges in constructing more intelligent recommender systems. Specific topics included, but were not limited to knowledge-aware recommendation, explainable recommendation, conversational recommendation, and sequential recommendation.

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N. Thangarasu, R. Rajalakshmi, G. Manivasagam, and V. Vijayalakshmi. "Performance of re-ranking techniques used for recommendation method to the user CF- Model." International Journal of Data Informatics and Intelligent Computing 1, no. 1 (September 23, 2022): 30–38. http://dx.doi.org/10.59461/ijdiic.v1i1.9.

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The recent research work for addressed to the aims at a spectrum of item ranking techniques that would generate recommendations with far more aggregate variability across all users while retaining comparable levels of recommendation accuracy. Individual users and companies are increasingly relying on recommender systems to provide information on individual suggestions. The recommended technologies are becoming increasingly efficient because they are focusing on scalable sorting-based heuristics that make decisions based solely on "local" data (i.e., only on the candidate items of each user) rather than having to keep track of "national" data, such as items have been all user recommended at the time. The real-world rating datasets and various assessments to be the prediction techniques and comprehensive empirical research consistently demonstrate the proposed techniques' diversity gains. Although the suggested approaches have primarily concentrated on improving recommendation accuracy, other critical aspects of recommendation quality, such as recommendation delivery, have often been ignored.

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Parasuraman, Desabandh, and Sathiyamoorthy Elumalai. "Hybrid Recommendation Using Temporal Data for Accuracy Improvement in Item Recommendation." Journal of information and organizational sciences 45, no. 2 (December 15, 2021): 535–51. http://dx.doi.org/10.31341/jios.45.2.10.

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Recommender systems have become a vital entity to the business world in form of software tools to make decisions. It estimates the overloaded information and provides the suitable decisions in any kind of business work through online. Especially in the area of e-commerce, recommender systems provide suggestions to users on the items that are likely based upon user’s true interest. Collaborative Filtering and Content Based Filtering are the main techniques of recommender systems. Collaborative Filtering is considered to be the best in all domains and always outperforms Content Based filtering. But, both the techniques have some limitations like data sparsity, cold start, gray sheep and scalability issues. To overcome these limitations, Hybrid Recommender Systems are used by combining Collaborative Filtering and Content Based Filtering. This paper proposes such kind of hybrid system by combining Collaborative Filtering and Content Based Filtering using time variance and machine learning algorithm.

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Salau, Latifat, Mohamed Hamada, Rajesh Prasad, Mohammed Hassan, Anand Mahendran, and Yutaka Watanobe. "State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning." Applied Sciences 12, no. 23 (November 24, 2022): 11996. http://dx.doi.org/10.3390/app122311996.

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Recommender systems (RSs) are increasingly recognized as intelligent software for predicting users’ opinions on specific items. Various RSs have been developed in different domains, such as e-commerce, e-government, e-resource services, e-business, e-library, e-tourism, and e-learning, to make excellent user recommendations. In e-learning technology, RSs are designed to support and improve the learning practices of a student or an organization. This survey aims to examine the different works of literature on RSs that corroborate e-learning and classify and provide statistics of the reviewed articles based on their recommendation goals, recommendation techniques used, the target user, and the application platforms. The survey makes a prominent contribution to the e-learning RSs field by providing an overview of current research and traditional and nontraditional recommendation techniques to provide different recommendations for future e-learning. One of the most significant findings to emerge from this survey is that a substantial number of works followed either deep learning or context-aware recommendation techniques, which are considered more efficient than any traditional methods. Finally, we provided comprehensive observations from the quantitative assessment of publications, which can guide and support researchers in understanding the current development for potential future trends and the direction of deep learning-based RSs in e-learning.

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Su, Zhan, Haochuan Yang, and Jun Ai. "FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction." PLOS ONE 18, no. 8 (August 28, 2023): e0290622. http://dx.doi.org/10.1371/journal.pone.0290622.

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Rating prediction is crucial in recommender systems as it enables personalized recommendations based on different models and techniques, making it of significant theoretical importance and practical value. However, presenting these recommendations in the form of lists raises the challenge of improving the list’s quality, making it a prominent research topic. This study focuses on enhancing the ranking quality of recommended items in user lists while ensuring interpretability. It introduces fuzzy membership functions to measure user attributes on a multi-dimensional item label vector and calculates user similarity based on these features for prediction and recommendation. Additionally, the user similarity network is modeled to extract community information, leading to the design of a set of corresponding recommendation algorithms. Experimental results on two commonly used datasets demonstrate the effectiveness of the proposed algorithm in enhancing list ranking quality, reducing prediction errors, and maintaining recommendation diversity and accurate user preference classification. This research highlights the potential of integrating heuristic methods with complex network theory and fuzzy techniques to enhance recommendation system performance with interpretability in mind.

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Jain, Gourav, Nishchol Mishra, and Sanjeev Sharma. "A Survey on Recommendation Techniques in Numerous Domains." International Journal of Computer Applications 67, no. 25 (April 18, 2013): 26–30. http://dx.doi.org/10.5120/11745-7379.

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Chung, Young-Mee, and Yong-Gu Lee. "Developing a Book Recommendation System Using Filtering Techniques." Journal of Information Management 33, no. 1 (March 31, 2002): 1–17. http://dx.doi.org/10.1633/jim.2002.33.1.001.

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DENNOUNI, Nassim, Zohra SLAMA, Yvan PETER, and Luigi LANCIERI. "Recommendation Techniques in Mobile Learning Context: A Review." International Journal of Modern Education and Computer Science 9, no. 10 (October 8, 2017): 37–46. http://dx.doi.org/10.5815/ijmecs.2017.10.05.

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Lei, Jian Lan, Jin Wang, Guo Dong Lu, and Shao Mei Fei. "Applying Collaborative Filtering Techniques for Individual Fashion Recommendation." Advanced Materials Research 102-104 (March 2010): 31–35. http://dx.doi.org/10.4028/www.scientific.net/amr.102-104.31.

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Collaborative filtering (CF) technique is the most successful method for recommendation system. In this article, we developed a fashion recommendation system by using CF technique. In order to improve on data sparseness problems in CF technique, firstly we built users’ similarities based on users’ background information which is related with fashion, then the neighbors’ predicting ratings were filled into the U-I rating matrix in advance before the traditional collaborative filtering. While computing the background information similarities, we develop a hybrid similarity model which can deal with different types of properties. The method can solve the data sparseness of U-I rating matrix effectively.

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Aditya, T. S., Karthik Rajaraman, and M. Monica Subashini. "Comparative Analysis of Clustering Techniques for Movie Recommendation." MATEC Web of Conferences 225 (2018): 02004. http://dx.doi.org/10.1051/matecconf/201822502004.

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Movie recommendation is a subject with immense ambiguity. A person might like a movie but not a very similar movie. The present recommending systems focus more on just few parameters such as Director, cast and genre. A lot of Power intensive methods such as Deep Convolutional Neural Network (CNN) has been used which demands the use of Graphics processors that require more energy. We try to accomplish the same task using lesser Energy consuming algorithms such as clustering techniques. In this paper, we try to create a more generalized list of similar movies in order to provide the user with more variety of movies which he/she might like, using clustering algorithms. We will compare how choosing different parameters and number of features affect the cluster's content. Also, compare how different algorithms such as K-mean, Hierarchical, Birch and mean shift clustering algorithms give a varied result and conclude which method will suit for which scenarios of movie recommendations. We also conclude on which algorithm clusters stray data points more efficiently and how different algorithms provide different advantages and disadvantages.

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Adomavicius, G., and YoungOk Kwon. "Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques." IEEE Transactions on Knowledge and Data Engineering 24, no. 5 (May 2012): 896–911. http://dx.doi.org/10.1109/tkde.2011.15.

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Hopfgartner, Frank, and Joemon M. Jose. "Semantic user profiling techniques for personalised multimedia recommendation." Multimedia Systems 16, no. 4-5 (May 14, 2010): 255–74. http://dx.doi.org/10.1007/s00530-010-0189-6.

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Guo, Xuetao, and Jie Lu. "Intelligent e-government services with personalized recommendation techniques." International Journal of Intelligent Systems 22, no. 5 (2007): 401–17. http://dx.doi.org/10.1002/int.20206.

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Kumar, Pushpendra, and Ramjeevan Singh Thakur. "Recommendation system techniques and related issues: a survey." International Journal of Information Technology 10, no. 4 (April 7, 2018): 495–501. http://dx.doi.org/10.1007/s41870-018-0138-8.

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Bansal, Saumya, and Niyati Baliyan. "A Study of Recent Recommender System Techniques." International Journal of Knowledge and Systems Science 10, no. 2 (April 2019): 13–41. http://dx.doi.org/10.4018/ijkss.2019040102.

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The influx of data in most domains is huge and dynamic, leading to big data and hence the need to build a recommender system grows stronger. This work is a comprehensive survey of the current status of different recommendation approaches, their limitations and extension which when applied may eradicate the incessant information overload problem of web entirely. Further, an investigation is conducted on the Google Scholar database, delineating the temporal distribution of different recommendation techniques. Several popular and most-used evaluation metrics, domain-specific applications, and data sets used in the recommendation are reviewed. By summarizing the current state-of-the-art, this work may help researchers in the field of recommendation system techniques and provides future directions highlighting issues that need to be focused on.

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Ikram, Fasiha, and Humera Farooq. "Multimedia Recommendation System for Video Game Based on High-Level Visual Semantic Features." Scientific Programming 2022 (February 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/6084363.

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The increase of multimedia content in e-commerce and entertainment services creates a new research gap in the field of recommendation systems. The main emphasis of the presented work is on increasing the accuracy of multimedia recommendations using visual semantic content. Recent approaches have shown that the inclusion of visual information is helpful to understand the semantic features for a recommendation model. The researchers have contributed to the field of multimedia item recommendations using low-level visual semantic features. Here, we seek to extend this contribution by exploring the high-level visual semantic content using constant visual attributes for video game recommendation systems. With the exponential growth of multimedia content in the video game industry in the last decade, researchers investigate the importance of personalized video game recommendation techniques. Previous methods have not investigated the importance of visual semantic content for video game recommendations. A practical recommendation system for video games is challenging due to the data diversity, level of user interest, and semantic complexity of features involved. This study proposed a novel method named Deep Visual Semantic Multimedia Recommendation Systems (D_VSMR) to deal with high-level visual features for multimedia recommendation systems. A visual semantic-based video game recommendation system utilizing deep learning methods for visual content learning and user profile learning is introduced. The proposed approach employs content-based techniques to expand users’ profiles. The user profile expansion is based on the visual content of games. The required datasets have been obtained from video game e-commerce platforms like Google Play Store and Amazon for evaluation purposes. The evaluation results have shown that the proposed approach’s accuracy and effectiveness have been improved up to 95.87% compared to the other state-of-the-art methods.

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Uçar, Tamer, and Adem Karahoca. "Personalizing trip recommendations: A framework proposal." Global Journal of Computer Science 5, no. 1 (November 13, 2015): 24. http://dx.doi.org/10.18844/gjcs.v5i1.30.

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Personalized trip planning is a very common problem in tourism domain. There are several studies in this area each one of all aims to provide recommendations based on user preferences. Recommendation engines mostly use two common methods: content based filtering and collaborative filtering. As a combination of these two methods, hybrid approaches are also popular for recommendation systems. This study provides a deep analysis about recent studies in trip recommendation domain. Applied techniques and mentioned methodologies in literature is discussed at all points. Insights about the proposed systems are provided clearly. Besides a literature survey, this study also proposes a novel travel recommender method based on a tourism datasource. A hybrid approach involving demographic, content-based and collaborative filtering techniques are proposed in order to eliminate drawbacks of each approach. Recommendations will be based on many factors including users’ demographic information, past travel locations and favorite seasons. Based on such inputs, recommender engine predicts possible travel locations along with various flight options. Possible challenges and future trends are concluded as a result of this study. Keywords: Recommender systems, trip recommendation, personalized recommendation, information filtering.

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Ravi, Logesh, and Subramaniyaswamy Vairavasundaram. "A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users." Computational Intelligence and Neuroscience 2016 (2016): 1–28. http://dx.doi.org/10.1155/2016/1291358.

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Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented.

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Verma, Rupal. "Movie Recommendation System by Using Collaborative Filtering." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 888–92. http://dx.doi.org/10.22214/ijraset.2021.38084.

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Abstract: This is the era of modern technology where we are all surrounded and covered by technology. This eases our daily life and saves our time and one of the most important techniques that played a very important role in our day-to-day life is the recommendation system. The recommendation system is used in various fields like it is used to recommend products, books, videos, movies, news, and many more. In this paper, we use a Recommendation system for movies we built or a movie recommendation system. It is based on a collaborative filtering approach that makes use of the information provided by the users, analyzes them and recommends movies according to the taste of users. The recommended movie list sorted according to the ratings given to this system is developed in python by using pycharm IDE and MYSQL for database connectivity. The presented recommendation system generates recommendations using various types of knowledge and data about users. Our Recommendation system recommends movies to each and every user by their previous searching history. Here we use some searching techniques as well. We also tried to overcome the cold start problem we use Movielens database. Keywords: Collaborative-filtering, Content-based filtering, Clustering, Recommendation system searching technique, Movies

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Santosh, Mohit, Viren Rajhauns, Deepankar Bhade, and Prof Debarati Ghosal. "Aaroha: A Music Recommendation System." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 3591–95. http://dx.doi.org/10.22214/ijraset.2023.50908.

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Abstract: The increasing availability of music streaming services has led to a vast collection of music. However, this abundance of music can make it difficult for users to find new songs and artists that match their tastes. Music recommendation applications provide a solution to this problem by using various algorithms and techniques to suggest music based on the user's listening habits and preferences. In this research paper, we review the popular music recommendation applications and the methods they use to recommend music. We also propose a new music recommendation system based on the user's emotional state and implement it using machine learning techniques. Our results show that our proposed system outperforms existing music recommendation systems and provides a more personalized recommendation.

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Vanbiervliet, Geoffroy, Alan Moss, Marianna Arvanitakis, Urban Arnelo, Torsten Beyna, Olivier Busch, Pierre H. Deprez, et al. "Endoscopic management of superficial nonampullary duodenal tumors: European Society of Gastrointestinal Endoscopy (ESGE) Guideline." Endoscopy 53, no. 05 (April 1, 2021): 522–34. http://dx.doi.org/10.1055/a-1442-2395.

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Main recommendations 1 ESGE recommends that all duodenal adenomas should be considered for endoscopic resection as progression to invasive carcinoma is highly likely.Strong recommendation, low quality evidence. 2 ESGE recommends performance of a colonoscopy, if that has not yet been done, in cases of duodenal adenoma.Strong recommendation, low quality evidence. 3 ESGE recommends the use of the cap-assisted method when the location of the minor and/or major papilla and their relationship to a duodenal adenoma is not clearly established during forward-viewing endoscopy.Strong recommendation, moderate quality evidence. 4 ESGE recommends the routine use of a side-viewing endoscope when a laterally spreading adenoma with extension to the minor and/or major papilla is suspected.Strong recommendation, low quality evidence. 5 ESGE suggests cold snare polypectomy for small (< 6 mm in size) nonmalignant duodenal adenomas.Weak recommendation, low quality evidence. 6 ESGE recommends endoscopic mucosal resection (EMR) as the first-line endoscopic resection technique for nonmalignant large nonampullary duodenal adenomas.Strong recommendation, moderate quality evidence. 7 ESGE recommends that endoscopic submucosal dissection (ESD) for duodenal adenomas is an effective resection technique only in expert hands.Strong recommendation, low quality evidence. 8 ESGE recommends using techniques that minimize adverse events such as immediate or delayed bleeding or perforation. These may include piecemeal resection, defect closure techniques, noncontact hemostasis, and other emerging techniques, and these should be considered on a case-by-case basis.Strong recommendation, low quality evidence. 9 ESGE recommends endoscopic surveillance 3 months after the index treatment. In cases of no recurrence, a further follow-up endoscopy should be done 1 year later. Thereafter, surveillance intervals should be adapted to the lesion site, en bloc resection status, and initial histological result. Strong recommendation, low quality evidence.

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Yanhong, Shen. "Design of Digital Network Shared Learning Platform Based on SCORM Standard." International Journal of Emerging Technologies in Learning (iJET) 13, no. 07 (June 28, 2018): 214. http://dx.doi.org/10.3991/ijet.v13i07.8602.

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Aiming at the problem of low utilization rate of teaching resources, a personalized recommendation system of digital teaching resources under SCORM standard was put forward. Based on the learner's learning process, standards and personalized recommendation techniques, a SCORM digital teaching resource management model was constructed. According to the rec-ommendation algorithm of collaborative filtering in personalized recommendation technology, a digital teaching resource recommendation model based on SCORM standard was constructed. The SCORM digital teaching resource library system based on collaborative filtering technology was designed and implemented. The system was evaluated. The data of the assessment were analyzed. The results showed that the recommendation system had a good promoting effect on the learners. Therefore, the system can provide personalized recommendations for learners. It improves learning efficiency.

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HN, Manjula, Nivin Srinivas S, and Samuel Raj S. "Survey on Recommendation Engines built using Collaborative Filtering Techniques." IJIREEICE 7, no. 3 (March 30, 2019): 44–46. http://dx.doi.org/10.17148/ijireeice.2019.7309.

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Ugale, Prof Meena, Nimeesha Venkatavelu, Pranay Patil, and Suraj Rane. "Review of Machine Learning Techniques for Crop Recommendation System." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 467–77. http://dx.doi.org/10.22214/ijraset.2022.40559.

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Abstract: The Indian population is highly dependent on agriculture for vegetables, fruits, grains, natural textile fibres like cotton, jute, and many more. Also, the agricultural sector plays a vital role in the economic growth of the country. The agriculture sector is contributing around 19.9 percent since 2020-2021. As a result, agricultural production in India has a significant impact on employment. The soil in India has been in use for thousands of years, resulting in depletion and exhaustion of nutrients and minerals, which leads to a reduction of crop yield. Also, there is a lack of modern applications, which causes a need for precision agriculture. Precision Agriculture, also known as Satellite farming is a series of strategies and tools to manage farms based on observing, measuring, and responding to crop variability both within and between fields. One of the main applications of precision agriculture is the recommendation of accurate crops. It helps in increasing crop yield and gaining profits. This paper aims to review and analyse the implementation and performance of various methodologies on crop recommendation systems. Keywords: Machine Learning, Precision Agriculture, Crop Recommendation System, Classification.

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Sahal, Radhya, Sahar Selim, and Abeer ElKorany. "An Adaptive Framework for Enhancing Recommendation Using Hybrid Techniques." International Journal of Computer Science and Information Technology 6, no. 2 (April 30, 2014): 51–66. http://dx.doi.org/10.5121/ijcsit.2014.6204.

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Nguyen, Thi Thanh Sang, Hai Yan Lu, Tich Phuoc Tran, and Jie Lu. "Investigation of sequential pattern mining techniques for web recommendation." International Journal of Information and Decision Sciences 4, no. 4 (2012): 293. http://dx.doi.org/10.1504/ijids.2012.050378.

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Zhang, Junjie, Kaixuan Liu, Min Dong, Hua Yuan, Chun Zhu, and Xianyi Zeng. "An intelligent garment recommendation system based on fuzzy techniques." Journal of The Textile Institute 111, no. 9 (December 3, 2019): 1324–30. http://dx.doi.org/10.1080/00405000.2019.1694351.

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kumar, Ravi, and Kali raj. "Location Based Service Recommendation System Using Hierarchy Clustering Techniques." International Journal of Computer Trends and Technology 36, no. 2 (June 25, 2016): 81–86. http://dx.doi.org/10.14445/22312803/ijctt-v36p114.

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Cornelis, Chris, Jie Lu, Xuetao Guo, and Guanquang Zhang. "One-and-only item recommendation with fuzzy logic techniques." Information Sciences 177, no. 22 (November 2007): 4906–21. http://dx.doi.org/10.1016/j.ins.2007.07.001.

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Ajmera, Avi, Mudit Bhandari, Harshit Kumar Jain, and Supriya Agarwal. "Crop, Fertilizer, & Irrigation Recommendation using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 29–35. http://dx.doi.org/10.22214/ijraset.2022.47793.

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Abstract: Agriculture is the majority source of income for many people not just in the Indian subcontinent but around the world and hence forms the backbone of the economy. Present-day difficulties like unpredictability in weather conditions, water scarcity, and volatility due to demand- supply fluctuations create the need for the farmer to be equipped with modern day techniques. More specifically, topics like less yield of crops due to unpredictable climate, faulty irrigation resources, and soil fertility level depletions needto be communicated. Hence there is a requirement to modify the abundant agriculture data into modern day technologies and make them conveniently accessible to farmers. A technique that can be implemented in crop yield predictionis Machine learning. Numerous machine learning techniqueslike regression, clustering, classification and prediction can be employed in crop yield forecasting. Algorithms like Naïve Bayes, support vector machines, decision trees, linear and logistic regression, and artificial neural networks can be employed in the prediction. The wide array of available algorithms poses a selection dilemma with reference to the selected crop. The purpose of this study is to investigate how different machine learning algorithms may be used to forecast agricultural production and present an approach in the context of big data computing for crop yield prediction and fertilizer recommendation using machine learning techniques

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Kumar, M. A. R., Abdullah Khan Mohammed, Siri Reddy Gundlapally, Tarun Ramavath, and Yadav Sujith. "Automatic car service recommendation system using machine learning techniques." i-manager’s Journal on Image Processing 9, no. 4 (2022): 46. http://dx.doi.org/10.26634/jip.9.4.19241.

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The automobile industry has been growing at a high rate in the past few decades, contributing about 7.5% to India's total Gross Domestic Product (GDP). As the number of vehicle owners are increasing the demand and need for automobile service is also high, but people are busy with their routines, hence failing to perform proper maintenance on their vehicles. This paper uses machine learning algorithms and object detection to come up with the idea to develop a web application that suggests users some offers and timing for their car maintenance by analyzing a car using computer vision without the owner's involvement. This project aims at both the owner's convenience and the growth of the service provider's business. Generally, we do not realize that multiple tasks can be done at a time, which results in incomplete tasks. This paper presents a machine learning-based automated car maintenance system with effective time utilization, by using the Internet of Things (IoT) device that could be installed at the parking's main gate in places where people tend to spend many hours, like offices or malls. This device consists of a camera that is responsible for detecting a car image from the live video. These images are then sent to the device, which uses pre-trained models to detect any damages or dirtiness in the vehicle.

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M C, Prakash, and P. Saravanan. "Crop Insurance Premium Recommendation System Using Artificial Intelligence Techniques." International Journal of Professional Business Review 8, no. 4 (April 14, 2023): e01270. http://dx.doi.org/10.26668/businessreview/2023.v8i4.1270.

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Purpose: The objective of this study is to build a crop insurance premium recommender model which will be fair to both crop insurance policy holders and crop insurance service providers. Theoretical Framework: The Nonparametric Bayesian Model (modified) is the name of the proposed model suggested by Maulidi et al. (2021) and it consists of six variables which are regional risk, cultivation time period, land area, claim frequency, discount eligibility (local variable) and premium. Discount eligibility variable is introduced to encourage right farming practices among farmers. Design/methodology/approach: Descriptive research method is used in this study as it is used to accurately represent the characteristics of a group of items. The population for this study is 943 respondents. The entire dataset is used for in-depth and accurate analysis. Five Artificial Intelligence models (Machine Learning models) are proposed for crop insurance premium prediction and they are Ada Boost Regressor, Gradient Boosting Regressor, Extra Trees Regressor, Support Vector Regressor and K-Neighbors Regressor. Among them Gradient Boosting Regression model has given the highest accuracy. Thus, Gradient Boosting Regression model is the most suitable model to be recommended for crop insurance premium prediction. Findings and Suggestions: Regional risk, land area, claim frequency and cultivation time period is the order of independent variables from highest to least in terms of regression coefficient. This relative importance helps Non-Banking Financial Companies (NBFCs) to suggest farmers that they should concentrate most on the regional risk or chances of crop failure in a particular region in which they are doing agriculture and least on the cultivation time period of a crop or the season in which a crop is cultivated. Two suggestions for future researchers are to extend this research work to other parts of Tamil Nadu and to apply hybrid machine learning techniques to the proposed model. Practical Implication: Unlike the existing formula-based traditional method used for calculating crop insurance premium, artificial intelligence models (machine learning models) can automatically learn the changes that take place with respect to the nature of variables in the proposed model and improve its accuracy based on new data. Hence, the crop insurance premium suggested by the most accurate model among the artificial intelligence models used in this study will be fair to both NBFCs and farmers. Here, fair means moderate. On the other hand, the crop insurance premium suggested by the existing formula-based method may not be fair in the long term as they cannot automatically learn the changes that take place with respect to the nature of variables in the proposed model and improve. Originality/value: In this research article, the relative importance of independent variables in the proposed model is determined and it helps NBFCs to suggest farmers that they should concentrate most on the region they are doing agriculture and least on the cultivation time period of a crop. Additionally, a machine learning model which can automatically learn and improve itself is used and hence the crop insurance premium predicted by it will be fair. Finally, the entire population containing 943 respondents details is analysed.

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Journal articles on the topic 'RECOMMENDATION TECHNIQUES'

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