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Готові списки джерел за темами / Recommender System (RS)

Добірка наукової літератури з теми "Recommender System (RS)"

Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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Статті в журналах з теми "Recommender System (RS)":

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.

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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 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.

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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 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.

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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 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.

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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. 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.

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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) 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.

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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.

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.

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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 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.

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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 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.

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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 (June 1, 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 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.

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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 (September 30, 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 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.

Більше джерел

Дисертації з теми "Recommender System (RS)":

1

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 complet pour les PMEs, offrant des solutions dédiées pour l’ensemble des activités de GC, de l'acquisition et la représentation des connaissances à leur exploitation: (1) un processus d'acquisition de connaissances dédié basé sur le cadre Scrum, une méthodologie agile, (2) un modèle de représentation des connaissances dédié basé sur des graphes de connaissances semi-structurés, et (3) un processus d'exploitation des connaissances dédié basé sur le système de recommandation établi sur les liens entre les connaissances. Cette recherche a été menée en collaboration avec Axsens-bte, une PME spécialisée dans le conseil et la formation. Le partenariat avec Axsens-bte a fourni des retours précieux et des expériences pratiques, contribuant au développement du cadre de GC proposé et soulignant sa pertinence et son applicabilité dans des contextes réels de PME
Knowledge is vital for organizations, particularly in today’s Industry 4.0 context. Knowledge Management (KM) plays a critical role in an organization's success. Although KM has been relatively well-studied in large organizations, Small and Medium-sized Enterprises (SMEs) receive less attention. SMEs face unique challenges in KM, requiring a tailored KM framework. Our study aims to define a framework addressing their challenges while leveraging their inherent strengths. This thesis presents a dedicated and comprehensive SME KM framework, offering dedicated solutions from knowledge acquisition and representation to exploitation: (1) a dedicated knowledge acquisition process based on the Scrum framework, an agile methodology, (2) a dedicated knowledge representation model based on semi-structured KG, and (3) a dedicated knowledge exploitation process based on knowledge-relatedness RS. This research was conducted in collaboration with Axsens-bte, an SME specializing in consultancy and training. The partnership with Axsens-bte has provided invaluable insights and practical experiences, contributing to developing the proposed KM framework and highlighting its relevance and applicability in real-world SME contexts

Частини книг з теми "Recommender System (RS)":

1

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, 463–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91262-2_42.

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2

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, 141–68. Cham: 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 based on AI may also create detrimental effects—sometimes unforeseen—for users and society, especially for vulnerable groups. Constant tracking of users, automated analysis of personal data to predict and infer behaviours, preferences, future actions and characteristic, the creation of behavioural profiles and the microtargeting for personalized recommendations may raise relevant ethical and legal issues, such as discriminatory outcomes, lack of transparency and explanation of algorithmic decisions that impact people’s lives and unfair violations of privacy and data protection. This article aims to address these issues, through a multisectoral, multidisciplinary and human rights’-based approach, including contributions from the Law, ethics, technology, market, and society.

3

Dhabliya, Dharmesh, Kshipra Jain, Manju Bargavi, Deepak, Anishkumar Dhablia, Jambi Ratna Raja Kumar, Ankur Gupta, and Sabyasachi Pramanik. "Item Selection Using K-Means and Cosine Similarity." In AI-Driven Marketing Research and Data Analytics, 228–44. 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 the quality of recommendations. High-dimensional data is made simpler by embeddings, and similarity is precisely measured using cosine similarity. Transparency is ensured by covering dataset selection, analysis, and solutions in this chapter. The system architecture is covered in the concluding section, emphasizing approaches. This chapter provides information about the development of RS.

4

Varaprasad Rao M and Vishnu Murthy G. "DSS for Web Mining Using Recommendation System." In Advances in Data Mining and Database Management, 22–34. 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 content mining and Web structure mining. Recommender systems (RS) aim to capture the user behavior by suggesting/recommending users with relevant items or services that they find interesting in. Recommender systems have gained prominence in the field of information technology, e-commerce, etc., by inferring personalized recommendations by effectively pruning from a universal set of choices that directed users to identify content of interest.

5

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, 108–35. 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 provide the recommendations. When creating a recommender's user interface, the designers must make several decisions. Understandability, transparency, assessability, trust, and timeliness are five elements that the designer must address. When it comes to organizing a trip, people are becoming increasingly accustomed to using modern technology. Users are provided with a large quantity of data, which they must evaluate in order to choose the offerings that are interesting or appropriate for them. A customized tourist attractions recommender system is thought to be the most efficient way for visitors to find tourist attractions. The recommender system compares the acquired data to comparable and dissimilar data from other sources to provide a list of recommended tourist sites. These systems, which assist people in finding what they need on the internet, have been a huge success, and they wouldn't be conceivable without an excellent user interface. Data can now be easily segmented based on demographics, habits, trends, and a variety of other factors, thanks to the application of machine learning and AI. The main concept is to provide each user with better strategic decisions to their preferences based on their prior travel data and behavior. In this way, every facet of human behavior that these systems supply and explore is then fed into algorithms, which develop meaningful patterns. These patterns are then expressed through an interface and then transformed into useful products and services that help businesses improve their user experience. Both AI and machine learning are extremely compatible and friendly with UX; they all follow the same concepts and aims. However, there are many challenges to their implementation. AI/ML engineers and UX designers should collaborate on a shared platform to create a blueprint for a fantastic UX experience. The mix of qualitative and quantitative data is crucial if AI and machine learning connect with UX. There is no other technology that can improve UX as much as AI.

6

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, 83–94. 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 (Sentiment Analysis) of articles that may be found in online news sources and blogs or even in the recommender systems themselves can provide better recommendations to users. Research trends that connect sophisticated technological components of recommendation systems utilized in many service domains with the commercial aspects of these services are reviewed in this article. We must first conduct an accurate evaluation of recommendations models for RS (Recommendation Systems) using data mining & application service research. Deep learning architectures for breast cancer detection are the topic of this review. The following is a list of current machine-learning-based technologies that will be discussed in this survey. Research into recommendation systems is made possible by this study's examination of the numerous technologies and service trends to which recommendation systems may be applied, which gives a complete overview of the area.

7

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, 7260–70. 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 building such systems, the algorithms used to generate the recommendations and how these systems can be evaluated. Finally, a number of topics are discussed as envisioned future research directions.

8

Soliman, Khaled, Mahmood A. Mahmood, Ahmed El Azab, and Hesham Ahmed Hefny. "A Survey of Recommender Systems and Geographical Recommendation Techniques." In GIS Applications in the Tourism and Hospitality Industry, 249–74. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5088-4.ch011.

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Advancement of location-acquisition technologies with fast development of mobile devices and wireless communication caused a revolution of information. It has been used in location-based social networks (LBSNs), has attracted millions of users to Facebook places, Gowalla, and Foursquare, is an important task to make location recommendations to users, and utilizes user preferences and other information that not only help users explore new places but also make LBSNs more attractive to users. This chapter discusses recommender systems (RS) and its application in different fields like LBSN, big data, and real life. It describes traditional recommendation approaches as well as modern approaches and explains smart community as one of powerful techniques to be used. It also introduces the state-of-art geographical techniques and presents a comparative study of recommendation techniques that can be served as a good guide and a roadmap for research and practice in this area. Finally, the authors discuss measurements and the limitations of RS.

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Karthick, G. S., and M. Sridhar. "Intelligent Healthcare Recommender Systems for Advanced Healthcare Informatics." In Advances in Healthcare Information Systems and Administration, 1–24. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8913-0.ch001.

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Recommender systems (RS) are an important filtering tool for discovering services in a personalized way to guide users from the large space of possible options. Over the past decades, recommendation systems in healthcare application have become popular due to the exponential increase in health data available on various platforms. The chapter begins with an overview of recommender systems, discussing the purpose, functionality, and key components. It highlights the significance of recommender systems in healthcare, where the abundance of data and the complexity of medical decisions necessitate personalized recommendations to enhance the quality of care. Additionally, it introduces the concept of big data and its relevance in healthcare recommender systems, emphasizing the wealth of information available from various sources such as electronic health records, wearable devices, and social media. Moreover, the chapter addresses the challenges associated with the implementation of recommender systems in the healthcare domain.

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Ricci, Francesco, Quang Nhat Nguyen, and Olga Averjanova. "Exploiting a Map-Based Interface in Conversational Recommender Systems for Mobile Travelers." In Tourism Informatics, 73–93. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-818-5.ch005.

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Nowadays travel and tourism Web sites store and offer a large volume of travel related information and services. Furthermore, this huge amount of information can be easily accessed using mobile devices, such as a phone with mobile Internet connection capability. However, this information can easily overwhelm a user because of the large number of information items to be shown and the limited screen size in the mobile device. Recommender systems (RSs) are often used in conjunction with Web tools to effectively help users in accessing this overwhelming amount of information. These recommender systems can support the user in making a decision even when specific knowledge necessary to autonomously evaluate the offerings is not available. Recommender systems cope with the information overload problem by providing a user with personalized recommendations (i.e., a well chosen selection of the items contained in the repository), adapting this selection to the user’s needs and preferences in a particular usage context. In this chapter, the authors present a recommendation approach integrating a conversational preference acquisition technology based on “critiquing” with map visualization technologies to build a new map-based conversational mobile RS that can effectively and intuitively support travelers in finding their desired products and services. The results of the authors’ real-user study show that integrating map-based visualization and critiquing-based interaction in mobile RSs improves the system’s recommendation effectiveness, and increases the user satisfaction.

Тези доповідей конференцій з теми "Recommender System (RS)":

1

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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2

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. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3535442.

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3

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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Анотація:

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 the mobile platform and its effectiveness was evaluated with actual visitors of a museum. The results were consistent in indicating the improved system achieved high user satisfaction, with all the recommender attributes average ratings between 4.3 and 4.7 (in a scale of 1 to 5), and accuracy, with a Mean Average Error (MAE) of 0.69.

4

"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. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3297280.3329387.

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5

"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. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3329387.

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6

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. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3462430.

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7

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. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3389669.

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8

"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. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3167132.3258667.

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9

"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. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3258667.

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10

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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Анотація:

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 interactive RS framework named iRec. It covers the whole experimentation process by following the main RS guidelines. The iRec provides three modules to prepare the dataset, create new recommendation agents, and simulate the interactive scenario. Moreover, it also contains several state-of-the-art algorithms, a hyperparameter tuning module, distinct evaluation metrics, different ways of visualizing the results, and statistical validation.