Relevant bibliographies by topics / Recommender System (RS)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Recommender System (RS)'

Author: Grafiati
Published: 7 July 2024
Last updated: 31 July 2025

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Journal articles on the topic "Recommender System (RS)"

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Walia, Prof Ranjanroop. "Online Recommender System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 2569–77. http://dx.doi.org/10.22214/ijraset.2021.36424.

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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
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Lahlou, Fatima Zahra, Houda Benbrahim, and Ismail Kassou. "Review Aware Recommender System." International Journal of Distributed Artificial Intelligence 10, no. 2 (2018): 28–50. http://dx.doi.org/10.4018/ijdai.2018070102.

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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 d
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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 (2023): 2661. http://dx.doi.org/10.3390/electronics12122661.

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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)
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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.

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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.
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Kumar Sahni, Dheeraj. "Recommender System (RS): Challenges, Issues & Extensions." Mapana Journal of Sciences 21, no. 1 (2022): 73–92. http://dx.doi.org/10.12723/mjs.60.6.

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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 recommen
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Vaidhehi, V., and R. Suchithra. "A Systematic Review of Recommender Systems in Education." International Journal of Engineering & Technology 7, no. 3.4 (2018): 188. http://dx.doi.org/10.14419/ijet.v7i3.4.16771.

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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.
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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.

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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 lo
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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.

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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 distinguis
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Yadav, Dharminder, Himani Maheshwari, and Umesh Chandra. "An Approach Towards Hotel Recommender System." Journal of Computational and Theoretical Nanoscience 17, no. 6 (2020): 2605–12. http://dx.doi.org/10.1166/jctn.2020.8936.

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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 availabl
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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 (2021): 75. http://dx.doi.org/10.22146/ijitee.67553.

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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 serendip
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Dissertations / Theses on the topic "Recommender System (RS)"

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Sima, Xingyu. "La gestion des connaissances dans les petites et moyennes entreprises : un cadre adapté et complet." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP047.

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La connaissance est essentielle pour les organisations, particulièrement dans le contexte de l'Industrie 4.0. La Gestion des Connaissances (GC) joue un rôle critique dans le succès des organisations. Bien que la GC ait été relativement bien étudiée dans les grandes organisations, les Petites et Moyennes Entreprises (PMEs) reçoivent moins d'attention. Les PMEs font face à des défis uniques en termes de GC, nécessitant un cadre de GC dédié. Notre étude vise à définir un cadre répondant à leurs défis tout en tirant parti de leurs forces inhérentes. Cette thèse présente un cadre de GC dédié et com
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Book chapters on the topic "Recommender System (RS)"

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Zeng, Wanling, Yang Du, Dingqian Zhang, Zhili Ye, and Zhumei Dou. "TUP-RS: Temporal User Profile Based Recommender System." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91262-2_42.

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Magrani, Eduardo, and Paula Guedes Fernandes da Silva. "The Ethical and Legal Challenges of Recommender Systems Driven by Artificial Intelligence." In Multidisciplinary Perspectives on Artificial Intelligence and the Law. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41264-6_8.

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AbstractIn a hyperconnected world, recommendation systems (RS) are one of the most widespread commercial applications of artificial intelligence (AI), initially mostly used for e-commerce, but already widely applied to different areas, for instance, content providers and social media platforms. Due to the current information overload, these systems are designed mainly to help individuals dealing with the infinity of options available, in addition to optimizing companies’ profits by offering products and services that directly meet the needs of their customers. However, despite its benefits, RS
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Cerón-Rios Gineth, López Diego M., and Blobel Bernd. "Architecture and User-Context Models of CoCare: A Context-Aware Mobile Recommender System for Health Promotion." In Studies in Health Technology and Informatics. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-761-0-140.

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Recommender systems (RS) are useful tools for filtering and sorting items and information for users. There is a wide diversity of approaches that help creating personalized recommendations. Context-aware recommender systems (CARS) are a kind of RS which provide adaptation capabilities to the user's environment, e.g., by sensing data through wearable devices or other biomedical sensors. In healthcare and wellbeing, CARS can support health promotion and health education, considering that each individual requires tailored intervention programs. Our research aims at proposing a context-aware mobil
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Roselló Llorenç, Sánchez Mónica, Agell Núria, and Prats Francesc. "Using Qualitative Reasoning for a Recommender System." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-643-0-173.

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This paper presents the foundation for a new methodology for a collaborative recommender system (RS). This methodology is based on the degree of consensus of a group of users stating their preferences via qualitative orders-of-magnitude. The structure of distributive lattice is considered in defining the distance between users and the RSs new users. This proposed methodology incorporates incomplete or partial knowledge into the recommendation process using qualitative reasoning techniques to obtain consensus of its users for recommendations.
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Dhabliya, Dharmesh, Kshipra Jain, Manju Bargavi, et al. "Item Selection Using K-Means and Cosine Similarity." In AI-Driven Marketing Research and Data Analytics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2165-2.ch013.

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In today's digital world, recommender systems (RS) are crucial since they provide tailored suggestions depending on user preferences. In order to get beyond the constraints of RS, this chapter presents a revolutionary machine learning technique that uses cosine similarity, embeddings, and k-means clustering. The difficulties and solutions associated with using k-means clustering in RS are covered in the first part. Various approaches are investigated to provide an all-encompassing perspective on recommendation systems. The next part discusses using cosine similarity and embeddings to improve t
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Varaprasad Rao M and Vishnu Murthy G. "DSS for Web Mining Using Recommendation System." In Advances in Data Mining and Database Management. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1877-8.ch003.

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Decision Supports Systems (DSS) are computer-based information systems designed to help managers to select one of the many alternative solutions to a problem. A DSS is an interactive computer based information system with an organized collection of models, people, procedures, software, databases, telecommunication, and devices, which helps decision makers to solve unstructured or semi-structured business problems. Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web mining can be divided into three different types – Web usage mining, Web con
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Sesagiri Raamkumar Aravind, Foo Schubert, and Pang Natalie. "Rec4LRW – Scientific Paper Recommender System for Literature Review and Writing." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-503-6-106.

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In this paper, we introduce Rec4LRW, a recommender system (RS) for assisting researchers in finding research papers for their literature review and writing purposes. This system focuses on three researcher tasks – (1) Building a reading list of research papers, (2) Finding similar papers based on a set of papers, and (3) Shortlisting papers from the final reading list for inclusion in manuscript based on article type. A set of intermediate criteria are proposed to capture the relations between a research paper and its bibliography. The recommendation techniques for the three tasks in
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Shelke, P. M., Suruchi Dedgaonkar, and R. N. Bhimanpallewar. "Powering User Interface Design of Tourism Recommendation System with AI and ML." In Artificial Intelligence, Machine Learning and User Interface Design. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815179606124010008.

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The term “User Experience” (UX) refers to all elements of a customer's relationship with a company, including its services, products, and overall customer experience. Meeting the specific consumer demands and knowing their behavioral patterns are the most important criteria for an efficient UX. The backend that selects what to recommend and the frontend that gives the recommendation are the two essential components of recommendation systems (RS). An RS's user interface must deliver recommendations in a way that allows users to anticipate taking action on them. A user interface is required to p
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Kumar, Sumit, Dr Vishal Shrivastava, and Dr Vibhakar Pathak. "A BRIEF OVERVIEW ON SENTIMENT ANALYSIS BASED RECOMMENDATION SYSTEM." In Futuristic Trends in Computing Technologies and Data Sciences Volume 3 Book 4. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bict4p2ch1.

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Many industries, including e-commerce, media, finance, and utilities, have embraced recommender systems. To maximize customer happiness, this type of technology uses a vast quantity of data. These recommendations assist customers in selecting items, while companies can enhance product use. When it comes to analyzing social data, sentiment analysis may be used to acquire a better knowledge of users' thoughts & feelings, which is useful for enhancing the dependability of recommendation systems. However, this data may also be utilized to supplement user ratings of items. According to some, SA
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Sielis, George A., Aimilia Tzanavari, and George A. Papadopoulos. "Recommender Systems Review of Types, Techniques, and Applications." In Encyclopedia of Information Science and Technology, Third Edition. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-5888-2.ch714.

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Recommender or recommendation systems are software tools that make useful suggestions to users, by taking into account their profile, preferences and/or actions during interaction with an application or website. They are usually personalized and can refer to items to buy, people to connect to or books/ articles to read. Recommender Systems (RS) aim at helping users with their interaction by bringing to surface the information that is relevant to them, their needs, or their tasks. This article's objective is to present a review of the different types of RS, the techniques and methods used for b
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Conference papers on the topic "Recommender System (RS)"

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Lu, Kezhi, Qian Zhang, Guangquan Zhang, and Jie Lu. "BERT-RS: A neural personalized recommender system with BERT." In Conference on Machine learning, Multi Agent and Cyber Physical Systems (FLINS 2022). WORLD SCIENTIFIC, 2023. http://dx.doi.org/10.1142/9789811269264_0046.

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Zheng, Yong, Markus Zanker, Li Chen, and Panagiotis Symeonidis. "Session details: Theme: System software and security: RS - recommender systems track." In SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing. ACM, 2022. http://dx.doi.org/10.1145/3535442.

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Laskoski, Felipe F., and Alfredo Goldman. "CienTec Guide: Application and Online Evaluation of a Context-Based Recommender System in Cultural Heritage." In Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação (SBC), 2022. http://dx.doi.org/10.5753/sbsi_estendido.2022.222608.

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A Recommender System (RS) is best applied in situations where users have to decide to choose among a list of usually many options and visits in cultural heritage sites are an example of that. Visitors may also face problems in finding how to reach their options. This research addresses both problems with a mobile app consisting of a hybrid context-based RS that suggests personalized visiting routes with the goal to maximize user satisfaction and minimize the length of the recommended route. Unlike most published RS papers related to cultural heritage, the system in this research was built for
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"Session details: Theme: System software and security: RS - Recommender systems: Theory and applications track." In the 34th ACM/SIGAPP Symposium, edited by Markus Zanker, Li Chen, Panagiotis Symeonidis, and Yong Zheng. ACM Press, 2019. http://dx.doi.org/10.1145/3297280.3329387.

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"Session details: Theme: System software and security: RS - Recommender systems: Theory and applications track." In SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing, edited by Markus Zanker, Li Chen, Panagiotis Symeonidis, and Yong Zheng. ACM, 2019. http://dx.doi.org/10.1145/3329387.

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Zheng, Yong, Li Chen, Markus Zanker, and Panagiotis Symeonidis. "Session details: Theme: System software and security: RS - Recommender systems: Theory and applications track." In SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing. ACM, 2021. http://dx.doi.org/10.1145/3462430.

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Zanker, Markus, Panagiotis Symeonidis, and Yong Zheng. "Session details: Theme: System software and security: RS - Recommender systems: Theory and applications track." In SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing. ACM, 2020. http://dx.doi.org/10.1145/3389669.

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"Session details: System software and security: RS - recommender systems: theory, user interactions and applications track." In the 33rd Annual ACM Symposium, edited by Yong Zheng, Li Chen, and Markus Zanker. ACM Press, 2018. http://dx.doi.org/10.1145/3167132.3258667.

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"Session details: System software and security: RS - recommender systems: theory, user interactions and applications track." In SAC 2018: Symposium on Applied Computing, edited by Yong Zheng, Li Chen, and Markus Zanker. ACM, 2018. http://dx.doi.org/10.1145/3258667.

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Silva, Thiago, Adriano Pereira, and Leonardo Rocha. "iRec: Um framework para modelos interativos em Sistemas de Recomendação." In Concurso de Teses e Dissertações. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/ctd.2023.229296.

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Nowadays, most e-commerce and entertainment services have adopted interactive Recommender Systems (RS) to guide the entire journey of users into the system. This task has been addressed as a Multi-Armed Bandit problem where systems must continuously learn and recommend at each iteration. However, despite the recent advances, there is still a lack of consensus on the best practices to evaluate such bandit solutions. Several variables might affect the evaluation process, but most of the works have only been concerned with the accuracy of each method. Thus, this master dissertation proposes an in
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