Journal articles on the topic 'Recommender System (RS)'
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1
Walia, Prof Ranjanroop. "Online Recommender System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 30, 2021): 2569–77. http://dx.doi.org/10.22214/ijraset.2021.36424.
Abstract:
As the size of the e-commerce market grows, the consequences of it are appearing throughout society.The business Environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the users subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user. Recommender system (RS) has emerged as a major research interest that Aims to help users to find items online by providing suggestions that Closely match their interest. This paper provides a comprehensive study on the RS covering the different recommendation approaches, associated issues, and techniques used for information retrieval.
2
Lahlou, Fatima Zahra, Houda Benbrahim, and Ismail Kassou. "Review Aware Recommender System." International Journal of Distributed Artificial Intelligence 10, no. 2 (July 2018): 28–50. http://dx.doi.org/10.4018/ijdai.2018070102.
Abstract:
Context aware recommender systems (CARS) are recommender systems (RS) that provide recommendations according to user contexts. The first challenge for building such a system is to get the contextual information. Some works tried to get this information from reviews provided by users in addition to their ratings. However, all of these works perform important feature engineering in order to infer the context. In this article, the authors present a new CARS architecture that allows to automatically use contextual information from reviews without requiring any feature engineering. Moreover, they develop a new CARS algorithm that is tailored to textual contexts, that they call Textual Context Aware Factorization Machines (TCAFM). An empirical evaluation shows that the proposed architecture allows to significantly improve recommendation accuracy using state of the art RS and CARS algorithms, whereas TCAFM leads to additional improvements.
3
Kumar Sahni, Dheeraj. "Recommender System (RS): Challenges, Issues & Extensions." Mapana Journal of Sciences 21, no. 1 (January 1, 2022): 73–92. http://dx.doi.org/10.12723/mjs.60.6.
Abstract:
Recommendations are long chains followed from traditional life to today’s life. In everyday life, the chain of recommendation augments the social process via some physical media and digital applications. The issues and challenges of recommendation are still in the infancy due to the growth of technology. This article identifies the uncovered areas of concern and links them to novel solutions. We also provide an extensive literature with different dimension for the newbie to work with the subject. We observed the study with different taxonomy provided by the prevalent researcher of the recommender system. This article gives the remedial solution of the recommendation problems
4
Kang, Li Ting, and Yong Wang. "Seven Factors in Evaluating Recommender System." Applied Mechanics and Materials 472 (January 2014): 443–49. http://dx.doi.org/10.4028/www.scientific.net/amm.472.443.
Abstract:
Recommender system (RS) has been evaluated in many but incomparable ways beyond accuracy and thus proposing an evaluation framework to synthesize the existing strategies seems a solution. However, few scholars did it so far. Through literature review, user interview and expert assessment, this study proposed a theoretical evaluation model of RS and then formed the assessment tool, RS Evaluation Questionnaire (RSE). The results showed that RSE was an effective tool to evaluate a recommender system, with its reliability (Cronbachs α=0.803) and validity meeting the requirements of psychometrics. Seven factors such as Perceived Quality and Perceived Ease of Use were generated by factor analysis, accounting for 63.126% of the variance. Furthermore, regression analysis indicated that different combinations of RSE factors could significantly predict User Satisfaction, Reuse Intention and positive Word-Of-Mouth (WOM) spreading willingness. Enlightenments for future research and practice were discussed as well in the end.
5
Bajenaru, Victor, Steven Lavoie, Brett Benyo, Christopher Riker, Mitchell Colby, and James Vaccaro. "Recommender System Metaheuristic for Optimizing Decision-Making Computation." Electronics 12, no. 12 (June 14, 2023): 2661. http://dx.doi.org/10.3390/electronics12122661.
Abstract:
We implement a novel recommender system (RS) metaheuristic framework within a nonlinear NP-hard decision-making problem, for reducing the solution search space before high-burden computational steps are performed. Our RS-based metaheuristic supports consideration of comprehensive evaluation criteria, including estimations of the potential solution set’s optimality, diversity, and feedback/preference of the end-user, while also being fully compatible with additional established RS evaluation metrics. Compared to prior Operations Research metaheuristics, our RS-based metaheuristic allows for (1) achieving near-optimal solution scores through comprehensive deep learning training, (2) fast metaheuristic parameter inference during solution instantiation trials, and (3) the ability to reuse this trained RS module for traditional RS ranking of final solution options for the end-user. When implementing this RS metaheuristic within an experimental high-dimensionality simulation environment, we see an average 91.7% reduction in computation time against a baseline approach, and solution scores within 9.1% of theoretical optimal scores. A simplified RS metaheuristic technique was also developed in a more realistic decision-making environment dealing with multidomain command and control scenarios, where a significant computation time reduction of 87.5% is also achieved compared with a baseline approach, while maintaining solution scores within 9.5% of theoretical optimal scores.
6
Vaidhehi, V., and R. Suchithra. "A Systematic Review of Recommender Systems in Education." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 188. http://dx.doi.org/10.14419/ijet.v7i3.4.16771.
Abstract:
Recommender system (RS)s are widely used in different walks of life. This research work is to explore the usage of RS in the field of education. This review is performed in five dimensions which includes, Purpose of RS in Education, various techniques to build RS, input parameters used in design of RS, type of students involved in design of RS and Modelling strategies for RS to represent the data. The outcome of the research work is to facilitate the efficient design of the recommender system in education which will help the students by generating the appropriate recommendations.
7
Usman, Abdulgafar, Abubakar Roko, Aminu B. Muhammad, and Abba Almu. "Enhancing Personalized Book Recommender System." International Journal of Advanced Networking and Applications 14, no. 03 (2022): 5486–92. http://dx.doi.org/10.35444/ijana.2022.14311.
Abstract:
Recommender systems (Rs) are widely used to provide recommendations for a collection of items or products that may be of interest to user or a group of users. Because of its superior performance, Content-Based Filtering (CBF) is one of the approaches that are commonly utilized in real-world Rs using Time-Frequency and Inverse Document Frequency (TF-IDF) to calculate document similarities. However, it computes document similarity directly in the word-count space. We propose a user-based collaborative filtering (UBCF) method to solve the problem of limited in content analysis which leads to a low prediction rate for large vocabulary. In this study, we present an algorithm that utilises Euclidean distance similarity function, to solve the identified problem. The performance of the proposed scheme was evaluated against the benchmark scheme using different performance metrics. The proposed scheme was implemented and an experimentally tested by using the benchmark datasets (Amazon review datasets). The results revealed that, the proposed scheme achieved better performance than the existing recommender system in terms of Root Mean Square Error (RMSE) which reduces the errors by 29% and also increase the Precision and Recall by 51.4%, and 55.8% respectively in the 1 million datasets.
8
Batra, Priya, Anukriti Singh, and T. S. Mahesh. "Efficient Characterization of Quantum Evolutions via a Recommender System." Quantum 5 (December 6, 2021): 598. http://dx.doi.org/10.22331/q-2021-12-06-598.
Abstract:
We demonstrate characterizing quantum evolutions via matrix factorization algorithm, a particular type of the recommender system (RS). A system undergoing a quantum evolution can be characterized in several ways. Here we choose (i) quantum correlations quantified by measures such as entropy, negativity, or discord, and (ii) state-fidelity. Using quantum registers with up to 10 qubits, we demonstrate that an RS can efficiently characterize both unitary and nonunitary evolutions. After carrying out a detailed performance analysis of the RS in two qubits, we show that it can be used to distinguish a clean database of quantum correlations from a noisy or a fake one. Moreover, we find that the RS brings about a significant computational advantage for building a large database of quantum discord, for which no simple closed-form expression exists. Also, RS can efficiently characterize systems undergoing nonunitary evolutions in terms of quantum discord reduction as well as state-fidelity. Finally, we utilize RS for the construction of discord phase space in a nonlinear quantum system.
9
Yadav, Dharminder, Himani Maheshwari, and Umesh Chandra. "An Approach Towards Hotel Recommender System." Journal of Computational and Theoretical Nanoscience 17, no. 6 (June 1, 2020): 2605–12. http://dx.doi.org/10.1166/jctn.2020.8936.
Abstract:
Recommendation Systems (RS) suggest the right item to the right user. It predicts the user’s rating to an item and based on this rating RS provides the suggestion to users. In today’s world many online applications are already using the Recommendation system that provides a recommendation for a particular item like books, movies, music etc. in an automated fashion. This paper proposed a system that helps to find the best suitable hotel in a given geographical area according to the user query by using library “recommenderlab” in R. This study proposed a system that gives the best hotel available according to the user rating available in database. User makes their decision according to their recommendation provides by the proposed system for finding best suitable hotel from available database and shows on the map by using a leaflet map package.
10
Nugroho, Arseto Satriyo, Igi Ardiyanto, and Teguh Bharata Adji. "User Curiosity Factor in Determining Serendipity of Recommender System." IJITEE (International Journal of Information Technology and Electrical Engineering) 5, no. 3 (September 30, 2021): 75. http://dx.doi.org/10.22146/ijitee.67553.
Abstract:
Recommender rystem (RS) is created to solve the problem by recommending some items among a huge selection of items that will be useful for the e-commerce users. RS prevents the users from being flooded by information that is irrelevant for them.Unlike information retrieval (IR) systems, the RS system's goal is to present information to the users that is accurate and preferably useful to them. Too much focus on accuracy in RS may lead to an overspecialization problem, which will decrease its effectiveness. Therefore, the trend in RS research is focusing beyond accuracy methods, such as serendipity. Serendipity can be described as an unexpected discovery that is useful. Since the concept of a recommendation system is still evolving today, formalizing the definition of serendipity in a recommendation system is very challenging.One known subjective factor of serendipity is curiosity. While some researchers already addressed curiosity factor, it is found that the relationships between various serendipity component as perceived by the users and their curiosity levels is still yet to be researched. In this paper, the method to determine user curiosity model by considering the variation of rated items was presented, then relation to serendipity components using existing user feedback data was validated. The finding showed that the curiosity model was related to some user-perceived values of serendipity, but not all. Moreover, it also had positive effect on broadening the user preference.
11
Hdioud, Ferdaous, Bouchra Frikh, Brahim Ouhbi, and Ismail Khalil. "Multi-Criteria Recommender Systems." International Journal of Mobile Computing and Multimedia Communications 8, no. 4 (October 2017): 20–48. http://dx.doi.org/10.4018/ijmcmc.2017100102.
Abstract:
A Recommender System (RS) works much better for users when it has more information. In Collaborative Filtering, where users' preferences are expressed as ratings, the more ratings elicited, the more accurate the recommendations. New users present a big challenge for a RS, which has to providing content fitting their preferences. Generally speaking, such problems are tackled by applying Active Learning (AL) strategies that consist on a brief interview with the new user, during which she is asked to give feedback about a set selected items. This article presents a comprehensive study of the most important techniques used to handle this issue focusing on AL techniques. The authors then propose a novel item selection approach, based on Multi-Criteria ratings and a method of computing weights of criteria inspired by a multi-criteria decision making approach. This selection method is deployed to learn new users' profiles, to identify the reasons behind which items are deemed to be relevant compared to the rest items in the dataset.
12
Geng, Bingrui, Lingling Li, Licheng Jiao, Maoguo Gong, Qing Cai, and Yue Wu. "NNIA-RS: A multi-objective optimization based recommender system." Physica A: Statistical Mechanics and its Applications 424 (April 2015): 383–97. http://dx.doi.org/10.1016/j.physa.2015.01.007.
13
Travada, Eko. "TEKNIK POLLING DI RECOMMENDER SYSTEM COLLABORATIVE FILTERING UNTUK PEMBELAJARAN DARING." Jurnal Teknologi dan Komunikasi Pemerintahan 2, no. 1 (June 25, 2020): 43–51. http://dx.doi.org/10.33701/jtkp.v2i1.2299.
Abstract:
Currently, the Recommender System (RS) is a method that is widely used to help sort out information, which is currently very large. Without a Recommender System it will be very difficult to sort out the information one by one as needed. Sorting information in a RS is not the same as searching for information, as we do a search for files on storage media by simply writing a few keywords to find the files needed. RS sorting is by looking at the magnitude of a value obtained from drawing conclusions after analyzing the available data, either the user data itself or other user data. Information separation in online learning is also very much needed. Because online learning will be more effective if learners can be provided with the right material. Online learning that is currently available generally provides learning material content such as textbooks in hardcopy-like form. In this study, the online learning system was added with RS technology in order to help students choose the material that the user or students should study so that they can achieve the expected learning objectives. The method in the Recommender System that is widely researched in online learning is Collaborative Filtering. RS with collaborative filtering in order to provide accurate recommendations requires large amounts of data. Big data raises a problem, namely the spread of data occurs in many locations so that it requires complex computation in providing recommendations. To overcome the computational complexity, this paper will discuss the polling technique as a novelty in this study. The research shows that there is an increase in recommendation precision by 20%, when compared to data without polling.
Keywords: Recommender System, Collaborative Filtering, Polling
14
Sun, Jinyang, Baisong Liu, Hao Ren, and Weiming Huang. "NCGAN:A neural adversarial collaborative filtering for recommender system." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 2915–23. http://dx.doi.org/10.3233/jifs-210123.
Abstract:
The major challenge of recommendation system (RS) based on implict feedback is to accurately model users’ preferences from their historical feedback. Nowadays, researchers has tried to apply adversarial technique in RS, which had presented successful results in various domains. To a certain extent, the use of adversarial technique improves the modeling of users’ preferences. Nonetheless, there are still many problems to be solved, such as insufficient representation and low-level interaction. In this paper, we propose a recommendation algorithm NCGAN which combines neural collaborative filtering and generative adversarial network (GAN). We use the neural networks to extract users’ non-linear characteristics. At the same time, we integrate the GAN framework to guide the recommendation model training. Among them, the generator aims to make user recommendations and the discriminator is equivalent to a measurement tool which could measure the distance between the generated distribution and users’ ground distribution. Through comparison with other existing recommendation algorithms, our algorithm show better experimental performance in all indicators.
15
Bin Abubakr Joolfoo, Muhammad, Radhika Dhurmoo, and Rameshwar Ashwin Jugurnauth. "Design of a Recommender System (RS) for Job Searching Using Hybrid System." Internet of Things and Cloud Computing 8, no. 3 (2020): 31. http://dx.doi.org/10.11648/j.iotcc.20200803.11.
16
M. O., Omisore, and Samuel O. W. "Personalized Recommender System for Digital Libraries." International Journal of Web-Based Learning and Teaching Technologies 9, no. 1 (January 2014): 18–32. http://dx.doi.org/10.4018/ijwltt.2014010102.
Abstract:
The huge amount of information available online has given rise to personalization and filtering systems. Recommender systems (RS) constitute a specific type of information filtering technique that present items according to user's interests. In this research, a web-based personalized recommender system capable of providing learners with books that suit their reading abilities was developed. Content-based filtering (CBF) was used to analyze learners' reading abilities while books that are found suitable to learners are recommended with fuzzy matching techniques. The yokefellow cold-start problem inherent to CBF is assuaged by cold start engine. An experimental study was carried out on a database of 10000 books from different categories of computing studies. The outcome tracked over a period of eight months shows that the proposed system induces greater user satisfaction and this attests users' desirability of the system.
17
Bhuskute, Tanmay, Amit Jeve, Nihal Shah, Tejas Shah, and B. A. Patil. "MediaRec: A Hybrid Media Recommender System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2723–28. http://dx.doi.org/10.22214/ijraset.2022.42927.
Abstract:
Abstract: This paper discusses about a hybrid recommendation platform for Movies, Books and Songs in one roof. A recommender system is a subgroup of information filtering systems that helps in predicting the “rating” or “Preference” that a user would give to any item. It also helps users to get media of their choice based on their experiences of self and other users in a productive and efficacious manner without wasting time in useless browsing. Previous approaches in recommender system (RS) include Content based filtering and Collaborative filtering. These approaches have a particular limitation as like the necessity of the user history as they visit. So as to overcome such dependencies, the Hybrid Recommendation System is introduced. It uses both Collaborative based filtering system and Content based filtering system for recommending media. In this way, the system performance will be greatly improved through the integration of these two. Keywords: Media Recommender System, Movies, Books, Songs, Recommender, TFIDF, Cosine Similarity, Pearson Correlation, KNN, K-Means Clustering.
18
Piao, Jinghua, Guozhen Zhang, Fengli Xu, Zhilong Chen, Yu Zheng, Chen Gao, and Yong Li. "Bringing Friends into the Loop of Recommender Systems: An Exploratory Study." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (October 13, 2021): 1–26. http://dx.doi.org/10.1145/3479583.
Abstract:
The recommender system (RS), as a computer-supported information filtering system, is ubiquitous and influences what we eat, watch, or even like. In online RS, interactions between users and the system form a feedback loop: users take actions based on the recommendations provided by RS, and RS updates its recommendations accordingly. As such interactions increase, the issue of recommendation homogeneity intensifies, which significantly impairs user experience. In the face of this long-standing issue, the newly-emerging social e-commerce offers a new solution -- bringing friends' recommendations into the loop (friend-in-the-loop). In this paper, we conduct an exploratory study on the benefits of friend-in-the-loop through mixed methods on a leading social e-commerce platform in China, Beidian. We reveal that friend-in-the-loop provides users with more accurate and diverse recommendations than merely RS, and significantly alleviates algorithmic homogeneity. Moreover, our qualitative results demonstrate that the introduction of friends' external knowledge, consumers' trust, and empathy accounts for these benefits. Overall, we elaborate that friend-in-the-loop comprehensively benefits both users and RS, and it is a promising HCI-based solution to recommendation homogeneity, which offers insightful implications on designing future human-algorithm collaboration models.
19
Rabiu, Idris, Naomie Salim, Aminu Da’u, and Akram Osman. "Recommender System Based on Temporal Models: A Systematic Review." Applied Sciences 10, no. 7 (March 25, 2020): 2204. http://dx.doi.org/10.3390/app10072204.
Abstract:
Over the years, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users’ needs through mapping the available products to users based on their observed interests towards items. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended, a problem known as concept drift or sometimes called temporal dynamics in RS. Although the traditional techniques of RS have attained significant success in providing recommendations, they are insufficient in providing accurate recommendations due to concept drift problems. These issues have triggered a lot of researches on the development of dynamic recommender systems (DRSs) which is focused on the design of temporal models that will account for concept drifts and ensure more accurate recommendations. However, in spite of the several research efforts on the DRSs, only a few secondary studies were carried out in this field. Therefore, this study aims to provide a systematic literature review (SLR) of the DRSs models that can guide researchers and practitioners to better understand the issues and challenges in the field. To achieve the aim of this study, 87 papers were selected for the review out of 875 total papers retrieved between 2010 and 2019, after carefully applying the inclusion/exclusion and the quality assessment criteria. The results of the study show that concept drift is mostly applied in the multimedia domain, then followed by the e-commerce domain. Also, the results showed that time-dependent neighborhood models are the popularly used temporal models for DRS followed by the Time-dependent Matrix Factorization (TMF) and time-aware factors models, specifically Tensor models, respectively. In terms of evaluation strategy, offline metrics such as precision and recalls are the most commonly used approaches to evaluate the performance of DRS.
20
Zhao, Wayne Xin, Gaole He, Kunlin Yang, Hongjian Dou, Jin Huang, Siqi Ouyang, and Ji-Rong Wen. "KB4Rec: A Data Set for Linking Knowledge Bases with Recommender Systems." Data Intelligence 1, no. 2 (May 2019): 121–36. http://dx.doi.org/10.1162/dint_a_00008.
Abstract:
To develop a knowledge-aware recommender system, a key issue is how to obtain rich and structured knowledge base (KB) information for recommender system (RS) items. Existing data sets or methods either use side information from original RSs (containing very few kinds of useful information) or utilize a private KB. In this paper, we present KB4Rec v1.0, a data set linking KB information for RSs. It has linked three widely used RS data sets with two popular KBs, namely Freebase and YAGO. Based on our linked data set, we first preform qualitative analysis experiments, and then we discuss the effect of two important factors (i.e., popularity and recency) on whether a RS item can be linked to a KB entity. Finally, we compare several knowledge-aware recommendation algorithms on our linked data set.
21
Wilkinson, Daricia, Öznur Alkan, Q. Vera Liao, Massimiliano Mattetti, Inge Vejsbjerg, Bart P. Knijnenburg, and Elizabeth Daly. "Why or Why Not? The Effect of Justification Styles on Chatbot Recommendations." ACM Transactions on Information Systems 39, no. 4 (October 31, 2021): 1–21. http://dx.doi.org/10.1145/3441715.
Abstract:
Chatbots or conversational recommenders have gained increasing popularity as a new paradigm for Recommender Systems (RS). Prior work on RS showed that providing explanations can improve transparency and trust, which are critical for the adoption of RS. Their interactive and engaging nature makes conversational recommenders a natural platform to not only provide recommendations but also justify the recommendations through explanations. The recent surge of interest inexplainable AI enables diverse styles of justification, and also invites questions on how styles of justification impact user perception. In this article, we explore the effect of “why” justifications and “why not” justifications on users’ perceptions of explainability and trust. We developed and tested a movie-recommendation chatbot that provides users with different types of justifications for the recommended items. Our online experiment ( n = 310) demonstrates that the “why” justifications (but not the “why not” justifications) have a significant impact on users’ perception of the conversational recommender. Particularly, “why” justifications increase users’ perception of system transparency, which impacts perceived control, trusting beliefs and in turn influences users’ willingness to depend on the system’s advice. Finally, we discuss the design implications for decision-assisting chatbots.
22
Haruna, Khalid, Aminu Musa, Zayyanu Yunusa, Yakubu Ibrahim, Fa’iz Ibrahim Jibia, and Nur Bala Rabiu. "Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes." Science in Information Technology Letters 2, no. 1 (March 4, 2022): 28–42. http://dx.doi.org/10.31763/sitech.v2i1.610.
Abstract:
Recommender systems (RS) have been widely used to extract relevant and meaningful information from a vast body of data, to make appropriate suggestions to users with different preferences in various domains of applications. However, despite the success of the early recommendation systems, they suffer from two major challenges of cold start and data sparsity. Traditional RS consider an interaction between user and item (2D), neglecting contextual information such as location, until fairly recently. The contexts extend traditional RS to multi-dimension interaction and provides a useful information that allow recommendations to be more personalized. Surprisingly, taking these contexts such as location, into consideration eliminates the challenges of traditional RS. Location-Aware Recommender System (LARS) takes user's location into account as an additional context. The combination allows the prediction of spatial items, items closest to the users, to reduce information overload and was proved to be more effective than earlier RS. In this research, we provide a systematic literature of the existing literature in LARS from 2010 to 2021, focusing on the state-of-the-art methodologies, the domain of applications, and trends of publications in LARS. The paper proposed several models of LARS based on the traditional RS methodologies, providing future directions to researchers. Despite numerous reviews available on LARS, a review that proposed several LARS techniques were not found in the literature. The results indicated that the trend of publication in LARS is growing exponentially and that the field is getting attention rapidly with the number of publications on the rise every year.
23
Mali, Mahesh, Dhirendra Mishra, and M. Vijayalaxmi. "Benchmarking for Recommender System (MFRISE)." 3C TIC: Cuadernos de desarrollo aplicados a las TIC 11, no. 2 (December 29, 2022): 146–56. http://dx.doi.org/10.17993/3ctic.2022.112.146-156.
Abstract:
The advent of the internet age offers overwhelming choices of movies and shows to viewers which create need of comprehensive Recommendation Systems (RS). Recommendation System will suggest best content to viewers based on their choice using the methods of Information Retrieval, Data Mining and Machine Learning algorithms. The novel Multifaceted Recommendation System Engine (MFRISE) algorithm proposed in this paper will help the users to get personalized movie recommendations based on multi-clustering approach using user cluster and Movie cluster along with their interaction effect. This will add value to our existing parameters like user ratings and reviews. In real-world scenarios, recommenders have many non-functional requirements of technical nature. Evaluation of Multifaceted Recommendation System Engine must take these issues into account in order to produce good recommendations. The paper will show various technical evaluation parameters like RMSE, MAE and timings, which can be used to measure accuracy and speed of Recommender system. The benchmarking results also helpful for new recommendation algorithms. The paper has used MovieLens dataset for purpose of experimentation. The studied evaluation methods consider both quantitative and qualitative aspects of algorithm with many evaluation parameters like mean squared error (MSE), root mean squared error (RMSE), Test Time and Fit Time are calculated for each popular recommender algorithm (NMF, SVD, SVD++, SlopeOne, Co- Clustering) implementation. The study identifies the gaps and challenges faced by each above recommender algorithm. This study will also help researchers to propose new recommendation algorithms by overcoming identified research gaps and challenges of existing algorithms.
24
Chopra, Akanksha Bansal, and Veer Sain Dixit. "An adaptive RNN algorithm to detect shilling attacks for online products in hybrid recommender system." Journal of Intelligent Systems 31, no. 1 (January 1, 2022): 1133–49. http://dx.doi.org/10.1515/jisys-2022-1023.
Abstract:
Abstract Recommender system (RS) depends on the thoughts of numerous users to predict the favourites of potential consumers. RS is vulnerable to malicious information. Unsuitable products can be offered to the user by injecting a few unscrupulous “shilling” profiles like push and nuke attacks into the RS. Injection of these attacks results in the wrong recommendation for a product. The aim of this research is to develop a framework that can be widely utilized to make excellent recommendations for sales growth. This study uses the methodology that presents an enhanced clustering algorithm named as modified density peak clustering algorithm on the consumer review dataset to ensure a well-formed cluster. An improved recurrent neural network algorithm is proposed to detect these attacks in hybrid RS, which uses the content-based RS and collaborative filtering RS. The results are compared with other state of the art algorithms. The proposed method is more suitable for E-commerce applications where the number of customers and products grows rapidly.
25
Esheiba, Laila, Amal Elgammal, Iman M. A. Helal, and Mohamed E. El-Sharkawi. "A Hybrid Knowledge-Based Recommender for Product-Service Systems Mass Customization." Information 12, no. 8 (July 26, 2021): 296. http://dx.doi.org/10.3390/info12080296.
Abstract:
Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of individual customers with near-mass-production efficiency. In the context of the PSS mass customization environment, customers are overwhelmed by a plethora of previously customized PSS variants. As a result, finding a PSS variant that is precisely aligned with the customer’s needs is a cognitive task that customers will be unable to manage effectively. In this paper, we propose a hybrid knowledge-based recommender system that assists customers in selecting previously customized PSS variants from a wide range of available ones. The recommender system (RS) utilizes ontologies for capturing customer requirements, as well as product-service and production-related knowledge. The RS follows a hybrid recommendation approach, in which the problem of selecting previously customized PSS variants is encoded as a constraint satisfaction problem (CSP), to filter out PSS variants that do not satisfy customer needs, and then uses a weighted utility function to rank the remaining PSS variants. Finally, the RS offers a list of ranked PSS variants that can be scrutinized by the customer. In this study, the proposed recommendation approach was applied to a real-life large-scale case study in the domain of laser machines. To ensure the applicability of the proposed RS, a web-based prototype system has been developed, realizing all the modules of the proposed RS.
26
Vijayakumar, V., Subramaniyaswamy Vairavasundaram, R. Logesh, and A. Sivapathi. "Effective Knowledge Based Recommender System for Tailored Multiple Point of Interest Recommendation." International Journal of Web Portals 11, no. 1 (January 2019): 1–18. http://dx.doi.org/10.4018/ijwp.2019010101.
Abstract:
With the massive growth of the internet, a new paradigm of recommender systems (RS's) is introduced in various real time applications. In the research for better RS's, especially in the travel domain, the evolution of location-based social networks have helped RS's to understand the changing interests of users. In this article, the authors present a new travel RS employed on the mobile device to generate personalized travel planning comprising of multiple Point of Interests (POIs). The recommended personalized list of travel locations will be predicted by generating a heat map of already visited POIs and the highly relevant POIs will be selected for recommendation as destinations. To enhance the recommendation quality, this article exploits the temporal features for increased user visits. A personalized travel plan is recommended to the user based on the user selected POIs and the proposed travel RS is experimentally evaluated with the real-time large-scale dataset. The obtained results of the developed RS are found to be proficient by means of improved diversity and accuracy of generated recommendations.
27
Idakwo, John, Joshua Babatunde Agbogun, and Taiwo Kolajo. "A Survey on Recommendation System Techniques." UMYU Scientifica 2, no. 2 (June 30, 2023): 112–19. http://dx.doi.org/10.56919/usci.2322.012.
Abstract:
The primary objective of recommender systems (RS) is to analyze user behavior and propose relevant items or services that users would find appealing. Recommender systems have gained significant prominence in various domains such as information technology and e-commerce. They achieve this by customizing recommendations based on individual preferences, efficiently filtering options from a vast pool, and enabling users to discover content that matches their interests. Numerous recommendation techniques have been developed to generate personalized suggestions, including collaborative filtering, content-based filtering, knowledge-based recommendation systems, and other approaches. Furthermore, hybrid recommendation systems have been proposed to address the limitations of individual methods by combining different techniques. This paper presents an overview of diverse recommendation methods, their fundamental approaches, challenges, solution and have equally looked at different solutions to these challenges faced by modern recommender systems. It also recommends promising avenues for future directions.
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Karabila, Ikram, Nossayba Darraz, Anas El-Ansari, Nabil Alami, and Mostafa El Mallahi. "Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis." Future Internet 15, no. 7 (July 5, 2023): 235. http://dx.doi.org/10.3390/fi15070235.
Abstract:
Recommendation systems (RSs) are widely used in e-commerce to improve conversion rates by aligning product offerings with customer preferences and interests. While traditional RSs rely solely on numerical ratings to generate recommendations, these ratings alone may not be sufficient to offer personalized and accurate suggestions. To overcome this limitation, additional sources of information, such as reviews, can be utilized. However, analyzing and understanding the information contained within reviews, which are often unstructured data, is a challenging task. To address this issue, sentiment analysis (SA) has attracted considerable attention as a tool to better comprehend a user’s opinions, emotions, and attitudes. In this study, we propose a novel RS that leverages ensemble learning by integrating sentiment analysis of textual data with collaborative filtering techniques to provide users with more precise and individualized recommendations. Our system was developed in three main steps. Firstly, we used unsupervised “GloVe” vectorization for better classification performance and built a sentiment model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Secondly, we developed a recommendation model based on collaborative filtering techniques. Lastly, we integrated our sentiment analysis model into the RS. Our proposed model of SA achieved an accuracy score of 93%, which is superior to other models. The results of our study indicate that our approach enhances the accuracy of the recommendation system. Overall, our proposed system offers customers a more reliable and personalized recommendation service in e-commerce.
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A. Almohsen, Khadija, and Huda Al-Jobori. "Recommender Systems in Light of Big Data." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 6 (December 1, 2015): 1553. http://dx.doi.org/10.11591/ijece.v5i6.pp1553-1563.
Abstract:
The growth in the usage of the web, especially e-commerce website, has led to the development of recommender system (RS) which aims in personalizing the web content for each user and reducing the cognitive load of information on the user. However, as the world enters Big Data era and lives through the contemporary data explosion, the main goal of a RS becomes to provide millions of high quality recommendations in few seconds for the increasing number of users and items. One of the successful techniques of RSs is collaborative filtering (CF) which makes recommendations for users based on what other like-mind users had preferred. Despite its success, CF is facing some challenges posed by Big Data, such as: scalability, sparsity and cold start. As a consequence, new approaches of CF that overcome the existing problems have been studied such as Singular value decomposition (SVD). This paper surveys the literature of RSs and reviews the current state of RSs with the main concerns surrounding them due to Big Data. Furthermore, it investigates thoroughly SVD, one of the promising approaches expected to perform well in tackling Big Data challenges, and provides an implementation to it using some of the successful Big Data tools (i.e. Apache Hadoop and Spark). This implementation is intended to validate the applicability of, existing contributions to the field of, SVD-based RSs as well as validated the effectiveness of Hadoop and spark in developing large-scale systems. The implementation has been evaluated empirically by measuring mean absolute error which gave comparable results with other experiments conducted, previously by other researchers, on a relatively smaller data set and non-distributed environment. This proved the scalability of SVD-based RS and its applicability to Big Data.
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Kuanr, Madhusree, and Puspanjali Mohapatra. "Assessment Methods for Evaluation of Recommender Systems: A Survey." Foundations of Computing and Decision Sciences 46, no. 4 (December 1, 2021): 393–421. http://dx.doi.org/10.2478/fcds-2021-0023.
Abstract:
Abstract The recommender system (RS) filters out important information from a large pool of dynamically generated information to set some important decisions in terms of some recommendations according to the user’s past behavior, preferences, and interests. A recommender system is the subclass of information filtering systems that can anticipate the needs of the user before the needs are recognized by the user in the near future. But an evaluation of the recommender system is an important factor as it involves the trust of the user in the system. Various incompatible assessment methods are used for the evaluation of recommender systems, but the proper evaluation of a recommender system needs a particular objective set by the recommender system. This paper surveys and organizes the concepts and definitions of various metrics to assess recommender systems. Also, this survey tries to find out the relationship between the assessment methods and their categorization by type.
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Al-Asadi, Ammar Abdulsalam, and Mahdi Nsaif Jasim. "Cluster-based denoising autoencoders for rate prediction recommender systems." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 3 (June 1, 2023): 1805. http://dx.doi.org/10.11591/ijeecs.v30.i3.pp1805-1812.
Abstract:
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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Mat Amin, Maizan, Jannifer Yep Ai Lan, Mokhairi Makhtar, and Abd Rasid Mamat. "A Decision Tree Based Recommender System for Backpackers Accommodations." International Journal of Engineering & Technology 7, no. 2.15 (April 6, 2018): 45. http://dx.doi.org/10.14419/ijet.v7i2.15.11210.
Abstract:
Backpackers often travel for a longer period of time, have their own budgets and requirements on accommodations. The existing systems do not offer personalized recommendation criteria and some proposed inefficient recommender system (RS) for users. Moreover, other than information searching from websites and bloggers, only limited systems were specifically designed for backpackers’ accommodations recommender system. An observation and online survey was conducted to get the information from backpackers regarding their preferences while looking for the accommodations. Fifty (50) respondents were involved in the survey and the data have been analyzed and were classified to build a decision tree. The decision tree model then implemented in the Backpackers’ accommodations Recommender System (BRS). BRS offers a convenient way and solution for backpackers by including decision tree technique in the system to suggest best accommodations suit to backpacker’s preferences.
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Guesmi, Mouadh, Mohamed Amine Chatti, Shoeb Joarder, Qurat Ul Ain, Clara Siepmann, Hoda Ghanbarzadeh, and Rawaa Alatrash. "Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender System." Information 14, no. 7 (July 14, 2023): 401. http://dx.doi.org/10.3390/info14070401.
Abstract:
Significant attention has been paid to enhancing recommender systems (RS) with explanation facilities to help users make informed decisions and increase trust in and satisfaction with an RS. Justification and transparency represent two crucial goals in explainable recommendations. Different from transparency, which faithfully exposes the reasoning behind the recommendation mechanism, justification conveys a conceptual model that may differ from that of the underlying algorithm. An explanation is an answer to a question. In explainable recommendation, a user would want to ask questions (referred to as intelligibility types) to understand the results given by an RS. In this paper, we identify relationships between Why and How explanation intelligibility types and the explanation goals of justification and transparency. We followed the Human-Centered Design (HCD) approach and leveraged the What–Why–How visualization framework to systematically design and implement Why and How visual explanations in the transparent Recommendation and Interest Modeling Application (RIMA). Furthermore, we conducted a qualitative user study (N = 12) based on a thematic analysis of think-aloud sessions and semi-structured interviews with students and researchers to investigate the potential effects of providing Why and How explanations together in an explainable RS on users’ perceptions regarding transparency, trust, and satisfaction. Our study shows qualitative evidence confirming that the choice of the explanation intelligibility types depends on the explanation goal and user type.
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Ojagh, Soroush, Mohammad Reza Malek, and Sara Saeedi. "A Social–Aware Recommender System Based on User’s Personal Smart Devices." ISPRS International Journal of Geo-Information 9, no. 9 (August 30, 2020): 519. http://dx.doi.org/10.3390/ijgi9090519.
Abstract:
Providing recommendations in cold start situations is one of the most challenging problems for collaborative filtering based recommender systems (RSs). Although user social context information has largely contributed to the cold start problem, most of the RSs still suffer from the lack of initial social links for newcomers. For this study, we are going to address this issue using a proposed user similarity detection engine (USDE). Utilizing users’ personal smart devices enables the proposed USDE to automatically extract real-world social interactions between users. Moreover, the proposed USDE uses user clustering algorithm that includes contextual information for identifying similar users based on their profiles. The dynamically updated contextual information for the user profiles helps with user similarity clustering and provides more personalized recommendations. The proposed RS is evaluated using movie recommendations as a case study. The results show that the proposed RS can improve the accuracy and personalization level of recommendations as compared to two other widely applied collaborative filtering RSs. In addition, the performance of the USDE is evaluated in different scenarios. The conducted experimental results on USDE show that the proposed USDE outperforms widely applied similarity measures in cold start and data sparsity situations.
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Hornik, Jacob, Chezy Ofir, Matti Rachamim, and Sergei Graguer. "Fog Computing-Based Smart Consumer Recommender Systems." Journal of Theoretical and Applied Electronic Commerce Research 19, no. 1 (March 11, 2024): 597–614. http://dx.doi.org/10.3390/jtaer19010032.
Abstract:
The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC “smart” applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the “fog-based recommender systems” opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper.
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Shafik, Wasswa, S. Mojtaba Matinkhah, and Fawad Shokoor. "Recommendation System Comparative Analysis: Internet of Things aided Networks." EAI Endorsed Transactions on Internet of Things 8, no. 29 (May 20, 2022): e5. http://dx.doi.org/10.4108/eetiot.v8i29.1108.
Abstract:
Today, the public is not willing to spend much time identifying their personal needs. Therefore, it needs a system that automatically recommends customized items to customers. The Recommender system has an internet of things (IoT) that entails a subclass of evidenced-based sieving structures that pursues to forecast the assessment of a customer would stretch to an item. Within social networks, numerous categories of RS operate on different recommendation expertise. In this state-of-the-art, we describe and classify current studies from three different aspects by describing different methods of recommender systems. The Friend Recommendation System in social networks is necessary and inevitable, and it is due to this kind of coordination that inevitably recommends latent friends to customers. Making recommendations for friends is an imperative assignment for community networks, as obligating supplementary networks customarily superiors to enhanced customer experience.
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Guo, Shangzhi, Xiaofeng Liao, Gang Li, Kaiyi Xian, Yuhang Li, and Cheng Liang. "A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis." Entropy 25, no. 7 (July 14, 2023): 1062. http://dx.doi.org/10.3390/e25071062.
Abstract:
A recommender system (RS) is highly efficient in extracting valuable information from a deluge of big data. The key issue of implementing an RS lies in uncovering users’ latent preferences on different items. Latent Feature Analysis (LFA) and deep neural networks (DNNs) are two of the most popular and successful approaches to addressing this issue. However, both the LFA-based and the DNNs-based models have their own distinct advantages and disadvantages. Consequently, relying solely on either the LFA or DNN-based models cannot ensure optimal recommendation performance across diverse real-world application scenarios. To address this issue, this paper proposes a novel hybrid recommendation model that combines Autoencoder and LFA techniques, termed AutoLFA. The main idea of AutoLFA is two-fold: (1) It leverages an Autoencoder and an LFA model separately to construct two distinct recommendation models, each residing in a unique metric representation space with its own set of strengths; and (2) it integrates the Autoencoder and LFA model using a customized self-adaptive weighting strategy, thereby capitalizing on the merits of both approaches. To evaluate the proposed AutoLFA model, extensive experiments on five real recommendation datasets are conducted. The results demonstrate that AutoLFA achieves significantly better recommendation performance than the seven related state-of-the-art models.
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Aljukhadar, Muhammad, and Sylvain Senecal. "The Effect of Consumer-Activated Mind-Set and Product Involvement on the Compliance With Recommender System Advice." SAGE Open 11, no. 3 (July 2021): 215824402110315. http://dx.doi.org/10.1177/21582440211031550.
Abstract:
Whereas the research gauging the effectiveness of e-commerce recommender systems (RS) has depended on their design factors, recent work proposes a key role for consumer’s psychological factors. Involvement should reduce the compliance with RS advice because a consumer highly involved with the product perceives high choice risk and assigns low value to the advice. However, a consumer’s activated mind-set captured by implicit theory (fixed vs. growth mind-set) should also shape compliance. It is hypothesized that the two factors interact to jointly mitigate advice taking. Specifically, consumers whose fixed mind-set is primed comply with the RS advice less often when involvement is high. This and other anticipated effects (i.e., consumer’s importance of social approval, positive affect, and need for cognition) on advice compliance are tested in an experiment on 251 Canadian adults. In the experiment, compliance occurred when the participant follows the RS advice, and product involvement was initially measured. The results show that priming a fixed mind-set, which orients shoppers toward a performance goal, motivates them to comply with the RS advice when involvement is low. Priming a growth mind-set, which orients shoppers toward a learning goal, nullifies such effect. Positive affect and the importance of social approval had no significant impact on advice taking. Therefore, the effect of involvement on RS effectiveness is contingent on the shopper’s accessible mind-set.
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Martínez-López, Francisco J., Irene Esteban-Millat, Ana Argila, and Francisco Rejón-Guardia. "Consumers’ psychological outcomes linked to the use of an online store’s recommendation system." Internet Research 25, no. 4 (August 3, 2015): 562–88. http://dx.doi.org/10.1108/intr-01-2014-0033.
Abstract:
Purpose – Psychological perspective has been omitted or considered a secondary issue by past studies focused on e-commerce recommendation systems (RS). However, this perspective is key to gaining a better understanding of consumer behaviours when these systems are used to support purchasing processes at online stores. The paper aims to discuss these issues. Design/methodology/approach – The field study consisted of a simulated online shopping process undertaken by a sample of internet users with a recommender system at a real online store (Pixmania). The authors applied rigorous and detailed exploratory and confirmatory factor analyses to assess the empirical validity of the model. Findings – The proposed sequence of psychological outcomes is valid, with the exception of one hypothesized relationship. In particular, satisfaction with an online store’s recommender has a strong influence on a consumer’s willingness to purchase one of the items related to his/her shopping goal. However, this satisfaction has no direct effect on a consumer’s intention to make add-on purchases based on the recommender’s suggestions. On the contrary, the results support the idea that add-on purchases are conditioned by a previous purchase related to the consumer’s initial shopping goal. On the other hand, a consumer’s flow state while shopping improves all his/her psychological outcomes linked to an online store’s recommender. The influence of flow state is particularly interesting when seeking to gain a better understanding of consumers’ unplanned purchases based on the recommender’s suggestions. These findings have important implications for practitioners. Originality/value – This paper discusses in detail and empirically test a set of psychological outcomes that emerge when an e-vendor’s recommender is used to assist a consumer’s shopping process. To the best of the knowledge, this is the first attempt that empirically tests most of the hypothesized relationships within an online store’s RS context.
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Zhu, Hongyun. "RS on video games based on item-based collaborative filtering algorithm." Applied and Computational Engineering 5, no. 1 (June 14, 2023): 11–17. http://dx.doi.org/10.54254/2755-2721/5/20230515.
Abstract:
With the rapid development of the Internet and e-commerce, recommender systems have received great attention and wide application in this environment. Because it is difficult for people to choose the one that they like in the face of the dazzling array of items on the Internet, and these e-commerce sites also need to consider how to improve efficiency, the recommendation system is an excellent solution. This paper mainly reviewed the development of recommender systems, focusing on the research and experiments of a recommender system based on an item-based collaborative filtering algorithm. According to the experimental results and some previous studies, summarizing the advantages and disadvantages of this method, proposing some solutions, and pointing out some problems that will be faced by future researches on recommendation systems.
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Lavanya, R., Ebani Gogia, and Nihal Rai. "Comparison Study on Improved Movie Recommender Systems." Webology 18, Special Issue 04 (December 8, 2021): 1470–78. http://dx.doi.org/10.14704/web/v18si04/web18285.
Abstract:
Recommendation system is a crucial part of offering items especially in services that offer streaming. For streaming movie services on OTT, RS are a helping hand for users in finding new movies for leisure. In this paper, we propose a machine learning an approach based on auto encoders to produce a CF system which outputs movie rating for a user based on a huge DB of ratings from other users. Utilising Movie Lens dataset, we explore the use of deep learning neural network based Stacked Auto encoders to predict user s ratings on new movies, thereby enabling movie recommendations. We consequently implement Singular Value Decomposition (SVD) to recommend movies to users. The experimental result showcase that our R S out performs a user-based neighbourhood baseline in terms of MSE on predicted ratings and in a survey in which user judge between recommendation s from both systems.
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Ahmed, Muqeem, Mohd Dilshad Ansari, Ninni Singh, Vinit Kumar Gunjan, Santhosh Krishna B. V., and Mudassir Khan. "Rating-Based Recommender System Based on Textual Reviews Using IoT Smart Devices." Mobile Information Systems 2022 (July 11, 2022): 1–18. http://dx.doi.org/10.1155/2022/2854741.
Abstract:
Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach.
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Bahramian, Z., and R. Ali Abbaspour. "AN ONTOLOGY-BASED TOURISM RECOMMENDER SYSTEM BASED ON SPREADING ACTIVATION MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 10, 2015): 83–90. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-83-2015.
Abstract:
A tourist has time and budget limitations; hence, he needs to select points of interest (POIs) optimally. Since the available information about POIs is overloading, it is difficult for a tourist to select the most appreciate ones considering preferences. In this paper, a new travel recommender system is proposed to overcome information overload problem. A recommender system (RS) evaluates the overwhelming number of POIs and provides personalized recommendations to users based on their preferences. A content-based recommendation system is proposed, which uses the information about the user’s preferences and POIs and calculates a degree of similarity between them. It selects POIs, which have highest similarity with the user’s preferences. The proposed content-based recommender system is enhanced using the ontological information about tourism domain to represent both the user profile and the recommendable POIs. The proposed ontology-based recommendation process is performed in three steps including: ontology-based content analyzer, ontology-based profile learner, and ontology-based filtering component. User’s feedback adapts the user’s preferences using Spreading Activation (SA) strategy. It shows the proposed recommender system is effective and improves the overall performance of the traditional content-based recommender systems.
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Yin, Hongzhi, Weiqing Wang, Liang Chen, Xingzhong Du, Quoc Viet Hung Nguyen, and Zi Huang. "Mobi-SAGE-RS: A sparse additive generative model-based mobile application recommender system." Knowledge-Based Systems 157 (October 2018): 68–80. http://dx.doi.org/10.1016/j.knosys.2018.05.028.
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Abdelkhalek, Raoua, Imen Boukhris, and Zied Elouedi. "Towards more trustworthy predictions: A hybrid evidential movie recommender system." JUCS - Journal of Universal Computer Science 28, no. 10 (October 28, 2022): 1003–29. http://dx.doi.org/10.3897/jucs.79777.
Abstract:
Recommender Systems (RSs) are considered as popular tools that have revolutionized the e-commerce and digital marketing. Their main goal is predicting the users’ future preferences and providing accessible and personalized recommendations. However, uncertainty can spread at any level throughout the recommendation process, which may affect the results. In fact, the ratings given by the users are often unreliable. The final provided predictions itself may also be pervaded with uncertainty and doubt. Obviously, the reliability of the predictions cannot be fully certain and trustworthy. For the system to be effective, recommendations must inspire trust in the system and provide reliable and credible recommendations. The user may speculate about the uncertainty pervaded behind the given recommendation. He could tend to a reliable recommendation offering him a global overview about his preferences rather than an inappropriate one that contradicts his activities and objectives. While such imperfection cannot be ignored, traditional RSs are rarely able to deal with the uncertainty spreading around the prediction process, which may affect the credibility, the transparency and the trustworthiness of the current RS. Thus, in this paper, we opt for the uncertain framework of the belief function theory (BFT), which allows us to represent, quantify and manage imperfect evidence. By using the BFT, the users’ preferences and the interactions between the neighbors can be represented under uncertainty. Evidence from different information sources can then be combined leading to more reliable results. The proposed approach is a hybrid evidential movie RS that uses different data sources and delivers a personalized user-interface allowing a global overview of the possible future preferences. This representation would increase the users’ confidence towards the system as well as their satisfaction. Experiments are performed on MovieLens and their additional features provided by the Internet Movie Database (IMDb) and Rotten Tomatoes. The new approach achieves promising results compared to traditional approaches in terms of MAE, NMAE and RMSE. It also reaches interesting Precision, Recall and F-measure values of respectively, 0.782, 0.792 and 0.787.
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Patro, Sunkuru Gopal Krishna, Brojo Kishore Mishra, Sanjaya Kumar Panda, Raghvendra Kumar, Hoang Viet Long, and Tran Manh Tuan. "Knowledge-based preference learning model for recommender system using adaptive neuro-fuzzy inference system." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4651–65. http://dx.doi.org/10.3233/jifs-200595.
Abstract:
A recommender system (RS) delivers personalized suggestions on products based on the interest of a particular user. Content-based filtering (CBF) and collaborative filtering (CF) schemes have been previously used for this task. However, the main challenge in RS is cold start problem (CSP). This originates once a new user joins the system which makes the recommendation task tedious due to the shortage of information (clickstream, dwell time, rating, etc.) regarding the user’s interest. Therefore, CBF and CF are combined together by developing a knowledge-based preference learning (KBPL) system. This system considers the demographic data that includes gender, occupation, and age for the recommendation task. Initially, the dataset is clustered using the self-organizing map (SOM) technique, then the high dimensional data is decomposed by higher-order singular value decomposition (HOSVD) and finally, Adaptive neuro-fuzzy inference system (ANFIS) predicts the output. For the big dataset, SOM is a robust clustering method and the similarities among the users can be easily observed by grid clustering. The HOSVD extracts the required information from the available data set to find the user similarity by decomposing the dataset in lower dimensions. ANFIS uses IF-THEN rules to recommend similar product to the new users. The proposed KBPL system is evaluated with the Black Friday dataset and the obtained error value is compared with the existing CF and CBF techniques. The proposed KBPL system has obtained root mean squared error (RMSE) of 0.71%, mean absolute error (MAE) of 0.54%, and mean absolute percentage error (MAPE) of 37%. Overall, the outcome of the comparative analysis shows minimum error and better performance in terms of precision, recall, and f-measure for the proposed KBPL system compared to the existing techniques and therefore more suitable for accurately recommending the products for the new users.
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R.Sowmya, Dr T. Ananth Kumar, Dr R. Rajmohan, Dr P. Kanimozhi, Dr Christo Ananth, and Sunday A. AJAGBE. "A Brief Survey on Recommendation System for a Gradient Classifier based Inadequate Approach System." Middle East Journal of Applied Science & Technology 06, no. 02 (2023): 01–08. http://dx.doi.org/10.46431/mejast.2023.6201.
Abstract:
Recommender systems are a common and successful feature of modern internet services. (RS). A service that connects users to tasks is known as a recommendation system. Making it simpler for customers and project providers to identify and receive projects and other solutions achieves this. A recommendation system is a strong device that may be advantageous to a business or organisation. This study explores whether recommender systems may be utilised to solve cold-start and data-sparsely issues with recommender systems, as well as delays and business productivity. Recommender systems make it easier and more convenient for people to get information. Over the years, several different methods have been created. We employ a potent predictive regression method known as the slope classifier algorithm, which minimises a loss function by repeatedly choosing a function that points in the direction of the weak hypothesis or the negative gradient. A group that is experiencing trouble handling cold beginnings and data sparsity will send enormous datasets to the suggested systems team. The users have to finish their job by the deadline in order to overcome these challenges.
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Obeid, Charbel, Christine Lahoud, Khoury El, and Pierre-Antoine Champin. "A novel hybrid recommender system approach for student academic advising named COHRS, supported by case-based reasoning and ontology." Computer Science and Information Systems, no. 00 (2022): 11. http://dx.doi.org/10.2298/csis220215011o.
Abstract:
The recent development of the WorldWideWeb, information, and communications technology have transformed the world and moved us into the data era resulting in an overload of data analysis. Students at high school use, most of the time, the internet as a tool to search for universities/colleges, university?s majors, and career paths that match their interests. However, selecting higher education choices such as a university major is a massive decision for students leading them, to surf the internet for long periods in search of needed information. Therefore, the purpose of this study is to assist high school students through a hybrid recommender system (RS) that provides personalized recommendations related to their interests. To reach this purpose we proposed a novel hybrid RS approach named (COHRS) that incorporates the Knowledge base (KB) and Collaborative Filtering (CF) recommender techniques. This hybrid RS approach is supported by the Case based Reasoning (CBR) system and Ontology. Hundreds of queries were processed by our hybrid RS approach. The experiments show the high accuracy of COHRS based on two criteria namely the accuracy of retrieving the most similar cases and the accuracy of generating personalized recommendations. The evaluation results show the percentage of accuracy of COHRS based on many experiments as follows: 98 percent accuracy for retrieving the most similar cases and 95 percent accuracy for generating personalized recommendations.
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Whang, Claire, and Hyunjoo Im. "Does recommendation matter for trusting beliefs and trusting intentions? Focused on different types of recommender system and sponsored recommendation." International Journal of Retail & Distribution Management 46, no. 10 (October 8, 2018): 944–58. http://dx.doi.org/10.1108/ijrdm-06-2017-0122.
Abstract:
Purpose The advance of technology creates new possibilities for enhancing shopper experience. The purpose of this paper is to gain understanding of a recent innovation found in retail environment, a recommender system (RS). Specifically, this study investigated how the retailer’s claims of RS affect consumers’ perception of personalization, and further, trusting beliefs and intentions. Additionally, the effect of sponsored recommendation (SR) on consumers’ perceived trust was explored. Design/methodology/approach A 2 (RS claim: personalized/non-personalized)×2 (SR: present/absent)×2 (involvement: high/low) between subject factorial design was employed. An online experiment was conducted. A total of 273 response collected through Amazon MTurk were used for the analysis. Findings The findings showed retailer’s claims for RS were enough to increase the perception of personalization. The increased perceived personalization of the RS increased trusting beliefs and trusting intention. For SR, mixed results were found. Disclosing SR increased trusting intentions under the low-involvement condition, but the opposite effect was found under high-involvement condition. Practical implications The findings highlight the importance of retailers’ articulating what RS does. This can impact trusting beliefs and trusting intention. Additionally, the findings indicate SRs should be presented in accordance to the decision-making stage. The presence of SRs during the searching stage may positively impact consumer’s perception, but their presence during purchase stage may have a negative impact. Originality/value This study is among the first to examine the effect of different retailer’s claims on how the recommendations are generated on shopper’s perception. Also, this is one of few studies to investigate how SRs in RSs impact a shopper’s perception. This research provides insights into how an RS found in retail environment influence shopping experience.
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S, Saranya, and 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, no. 3 (July 22, 2023): 308–18. http://dx.doi.org/10.37936/ecti-cit.2023173.251272.
Abstract:
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.