In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.
Zanjani M and Hosseinzadeh Aghdam M (2024). The explainable structure of deep neural network for recommendation systems, Future Generation Computer Systems , 159 :C , (459-473), Online publication date: 1-Oct-2024 .
Vy H, Pham-Nguyen C and Nam L (2024). Integrating textual reviews into neighbor-based recommender systems, Expert Systems with Applications: An International Journal , 249 :PB , Online publication date: 1-Sep-2024 .
De Biasio A, Jannach D and Navarin N (2024). Model-based approaches to profit-aware recommendation, Expert Systems with Applications: An International Journal , 249 :PB , Online publication date: 1-Sep-2024 .
Starke A, Bremnes A, Knudsen E, Trilling D and Trattner C Perception versus Reality: Evaluating User Awareness of Political Selective Exposure in News Recommender Systems Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, (286-291)
Sukiennik N, Gao C and Li N Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation Proceedings of the ACM Web Conference 2024, (4727-4735)
Moreschini S, Lenarduzzi V and Coba L Towards a Technical Debt for AI-based Recommender System Proceedings of the 7th ACM/IEEE International Conference on Technical Debt, (36-39)
Ochoa W, Legaristi J, Larrinaga F and Pérez A (2024). Dynamic context-aware workflow management architecture for efficient manufacturing, Future Generation Computer Systems , 153 :C , (505-520), Online publication date: 1-Apr-2024 .
Starke A, Musto C, Rapp A, Semeraro G and Trattner C (2024). “Tell Me Why”: using natural language justifications in a recipe recommender system to support healthier food choices, User Modeling and User-Adapted Interaction , 34 :2 , (407-440), Online publication date: 1-Apr-2024 .
Iftikhar A, Ghazanfar M, Ayub M, Ali Alahmari S, Qazi N and Wall J (2024). A reinforcement learning recommender system using bi-clustering and Markov Decision Process, Expert Systems with Applications: An International Journal , 237 :PB , Online publication date: 1-Mar-2024 .
Barile F, Draws T, Inel O, Rieger A, Najafian S, Ebrahimi Fard A, Hada R and Tintarev N (2024). Evaluating explainable social choice-based aggregation strategies for group recommendation, User Modeling and User-Adapted Interaction , 34 :1 , (1-58), Online publication date: 1-Mar-2024 .
C V, Oberoi H, Goyal A and Sikka N RE-RecSys: An End-to-End system for recommending properties in Real-Estate domain Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD), (558-562)
Ou J, Jin H, Wang X, Jiang H, Wang X and Zhou C (2023). STA-TCN: Spatial-temporal Attention over Temporal Convolutional Network for Next Point-of-interest Recommendation, ACM Transactions on Knowledge Discovery from Data , 17 :9 , (1-19), Online publication date: 30-Nov-2023 .
Hua W, Xu S, Ge Y and Zhang Y How to Index Item IDs for Recommendation Foundation Models Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, (195-204)
Lops P, Polignano M, Musto C, Silletti A and Semeraro G (2023). ClayRS, Information Systems , 119 :C , Online publication date: 1-Oct-2023 .
Mpia H, Mburu L and Mwendia S (2023). CoBERT, Engineering Applications of Artificial Intelligence , 125 :C , Online publication date: 1-Oct-2023 .
Cavenaghi E, Sottocornola G, Stella F and Zanker M (2023). A Systematic Study on Reproducibility of Reinforcement Learning in Recommendation Systems, ACM Transactions on Recommender Systems , 1 :3 , (1-23), Online publication date: 30-Sep-2023 .
Heuer H and Glassman E Accessible Text Tools for People with Cognitive Impairments and Non-Native Readers: Challenges and Opportunities Proceedings of Mensch und Computer 2023, (250-266)
Xu S, Ge Y, Li Y, Fu Z, Chen X and Zhang Y Causal Collaborative Filtering Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval, (235-245)
Tomasi F, Cauteruccio J, Kanoria S, Ciosek K, Rinaldi M and Dai Z Automatic Music Playlist Generation via Simulation-based Reinforcement Learning Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (4948-4957)
Fraoua K and David A The Autonomous Platform Using the Markov Chain HCI International 2023 – Late Breaking Papers, (47-57)
van Kuijk K, Mahmoudi S, Wen Y, Barile F and Rienstra T An Argumentative Framework for Generating Explainable Group Recommendations Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, (266-274)
Marigowda C, Moldovan A, Siddig A, Muntean C, Pathak P and Stynes P A Novel Hybrid Machine Learning Framework to Recommend E-Commerce Products Proceedings of the 2023 5th International Conference on Information Technology and Computer Communications, (59-67)
Silva N, Silva T, Werneck H, Rocha L and Pereira A (2023). User Cold-start Problem in Multi-armed Bandits: When the First Recommendations Guide the User’s Experience, ACM Transactions on Recommender Systems , 1 :1 , (1-24), Online publication date: 31-Mar-2023 .
Ou C, Mayer S and Butz A The Impact of Expertise in the Loop for Exploring Machine Rationality Proceedings of the 28th International Conference on Intelligent User Interfaces, (307-321)
Malchik C and Feigenbaum J Toward User Control over Information Access: A Sociotechnical Approach Proceedings of the 2022 New Security Paradigms Workshop, (117-129)
Spillo G, Musto C, De Gemmis M, Lops P and Semeraro G Knowledge-aware Recommendations Based on Neuro-Symbolic Graph Embeddings and First-Order Logical Rules Proceedings of the 16th ACM Conference on Recommender Systems, (616-621)
Chu H and Shen Y User Feedback Design in AI-Driven Mood Tracker Mobile Apps Human-Computer Interaction. User Experience and Behavior, (346-358)
Mu C, Chen W, Liu Y, Lei D and Liu R (2022). Virtual information core optimization for collaborative filtering recommendation based on clustering and evolutionary algorithms, Applied Soft Computing , 116 :C , Online publication date: 1-Feb-2022 .
Zhang X, Li M, Seng D, Chen X, Chen X and Farouk A (2022). A Novel Precise Personalized Learning Recommendation Model Regularized with Trust and Influence, Scientific Programming , 2022 , Online publication date: 1-Jan-2022 .
Atas M, Felfernig A, Polat-Erdeniz S, Popescu A, Tran T and Uta M (2021). Towards psychology-aware preference construction in recommender systems: Overview and research issues, Journal of Intelligent Information Systems , 57 :3 , (467-489), Online publication date: 1-Dec-2021 .
Makhlouf K, Zhioua S and Palamidessi C (2021). Machine learning fairness notions, Information Processing and Management: an International Journal , 58 :5 , Online publication date: 1-Sep-2021 .
Fortes R, de Sousa D, Coelho D, Lacerda A and Gonçalves M (2021). Individualized extreme dominance (IndED), Information Sciences: an International Journal , 572 :C , (558-573), Online publication date: 1-Sep-2021 .
Tran T, Felfernig A and Tintarev N (2021). Humanized Recommender Systems: State-of-the-art and Research Issues, ACM Transactions on Interactive Intelligent Systems , 11 :2 , (1-41), Online publication date: 30-Jun-2021 .
Starke A, Willemsen M and Snijders C Using Explanations as Energy-Saving Frames: A User-Centric Recommender Study Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, (229-237)
Musto C, Starke A, Trattner C, Rapp A and Semeraro G Exploring the Effects of Natural Language Justifications in Food Recommender Systems Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, (147-157)
Makhlouf K, Zhioua S and Palamidessi C (2021). On the Applicability of Machine Learning Fairness Notions, ACM SIGKDD Explorations Newsletter , 23 :1 , (14-23), Online publication date: 26-May-2021 .
Machado G, Maran V, Lunardi G, Wives L and de Oliveira J (2021). AwARE: a framework for adaptive recommendation of educational resources, Computing , 103 :4 , (675-705), Online publication date: 1-Apr-2021 .
Albalawi R, Yeap T and Benyoucef M Evaluating the Effectiveness of A Suggested Architecture for The Real-Time Social Recommendation System Proceedings of the 2021 4th International Conference on Software Engineering and Information Management, (145-151)
Yan H, Yang C, Yu D, Li Y, Jin D and Chiu D (2020). Multi-Site User Behavior Modeling and Its Application in Video Recommendation, IEEE Transactions on Knowledge and Data Engineering , 33 :1 , (180-193), Online publication date: 1-Jan-2021 .
Alam M, Ubaid S, Shakil , Sohail S, Nadeem M, Hussain S and Siddiqui J (2022). Comparative Analysis of Machine Learning based Filtering Techniques using MovieLens dataset, Procedia Computer Science , 194 :C , (210-217), Online publication date: 1-Jan-2021 .
Zhang Y, Wang H, Jia M, Wang J, Li D, Xue G and Tan K (2020). TopoX: Topology Refactorization for Minimizing Network Communication in Graph Computations, IEEE/ACM Transactions on Networking , 28 :6 , (2768-2782), Online publication date: 1-Dec-2020 .
Zanon A, Souza L, Pressato D and Manzato M WordRecommender Proceedings of the Brazilian Symposium on Multimedia and the Web, (181-184)
Jenkin T, Skillicorn D and Chan Y (2020). Novel Information Discovery and Collaborative Filtering to Support Group Creativity, ACM SIGMIS Database: the DATABASE for Advances in Information Systems , 51 :4 , (40-67), Online publication date: 2-Nov-2020 .
Teppan E and Zanker M Exploiting Answer Set Programming for Building explainable Recommendations Foundations of Intelligent Systems, (395-404)
El Majjodi A, Elahi M, El Ioini N and Trattner C Towards Generating Personalized Country Recommendation Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, (71-76)
Elahi M, El Ioini N, Alexander Lambrix A and Ge M Exploring Personalized University Ranking and Recommendation Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, (6-10)
Luef J, Ohrfandl C, Sacharidis D and Werthner H A recommender system for investing in early-stage enterprises Proceedings of the 35th Annual ACM Symposium on Applied Computing, (1453-1460)
Wang S, Gong M, Wu Y and Zhang M (2020). Multi-objective optimization for location-based and preferences-aware recommendation, Information Sciences: an International Journal , 513 :C , (614-626), Online publication date: 1-Mar-2020 .
Lopes R, Assunção R and Santos R (2019). Graph-based Recommendation Meets Bayes and Similarity Measures, ACM Transactions on Intelligent Systems and Technology , 11 :1 , (1-26), Online publication date: 29-Feb-2020 .
Jiang P, Hong C and Agrawal G A novel data transformation and execution strategy for accelerating sparse matrix multiplication on GPUs Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, (376-388)
Haas R, Niedermayr R, Roehm T and Apel S (2020). Is Static Analysis Able to Identify Unnecessary Source Code?, ACM Transactions on Software Engineering and Methodology , 29 :1 , (1-23), Online publication date: 31-Jan-2020 .
Zhang S, Yao L, Sun A and Tay Y (2019). Deep Learning Based Recommender System, ACM Computing Surveys , 52 :1 , (1-38), Online publication date: 31-Jan-2020 .
Manoharan S, Senthilkumar R and Hernández-Pérez J (2020). An Intelligent Fuzzy Rule-Based Personalized News Recommendation Using Social Media Mining, Computational Intelligence and Neuroscience , 2020 , Online publication date: 1-Jan-2020 .
Huang L, Zhao Z, Wang C, Huang D and Chao H (2019). LSCD, Neurocomputing , 366 :C , (86-96), Online publication date: 13-Nov-2019 .
Zanker M, Rook L and Jannach D (2019). Measuring the impact of online personalisation, International Journal of Human-Computer Studies , 131 :C , (160-168), Online publication date: 1-Nov-2019 .
Cetin M and Ayvaz S A Negative Similarity Based Hybrid Recommender System Using Apache Spark Proceedings of the 3rd International Conference on Advances in Artificial Intelligence, (166-172)
Samer R, Felfernig A and Stettinger M Towards Issue Recommendation for Open Source Communities IEEE/WIC/ACM International Conference on Web Intelligence, (164-171)
Brandner K and Weinreich R A recommender system for software architecture decision making Proceedings of the 13th European Conference on Software Architecture - Volume 2, (22-25)
Rodler P, Jannach D, Schekotihin K and Fleiss P (2019). Are query-based ontology debuggers really helping knowledge engineers?, Knowledge-Based Systems , 179 :C , (92-107), Online publication date: 1-Sep-2019 .
van Capelleveen G, Amrit C, Yazan D and Zijm H (2022). The recommender canvas, Expert Systems with Applications: An International Journal , 129 :C , (97-117), Online publication date: 1-Sep-2019 .
Pavlidis G (2020). On the End-to-End Development of a Cultural Tourism Recommender, International Journal of Computational Methods in Heritage Science , 3 :2 , (73-90), Online publication date: 1-Jul-2019 .
Tran T, Atas M, Felfernig A, Le V, Samer R and Stettinger M Towards Social Choice-based Explanations in Group Recommender Systems Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, (13-21)
Atas M, Samer R, Felfernig A, Tran T, Erdeniz S and Stettinger M Socially-Aware Diagnosis for Constraint-Based Recommendation Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, (121-129)
Weng Y, Wu L and Hong W (2019). Bayesian Inference via Variational Approximation for Collaborative Filtering, Neural Processing Letters , 49 :3 , (1041-1054), Online publication date: 1-Jun-2019 .
Cai W, Zheng J, Pan W, Lin J, Li L, Chen L, Peng X and Ming Z (2019). Neighborhood-enhanced transfer learning for one-class collaborative filtering, Neurocomputing , 341 :C , (80-87), Online publication date: 14-May-2019 .
Chen J, Wang C, Zhou S, Shi Q, Feng Y and Chen C SamWalker: Social Recommendation with Informative Sampling Strategy The World Wide Web Conference, (228-239)
Banihashemi S, Li J and Abhari A Scalable machine learning algorithms for a Twitter followee recommender system Proceedings of the Communications & Networking Symposium, (1-8)
Bai P, Ge Y, Liu F and Lu H (2022). Joint interaction with context operation for collaborative filtering, Pattern Recognition , 88 :C , (729-738), Online publication date: 1-Apr-2019 .
Mizgajski J and Morzy M (2019). Affective recommender systems in online news industry, User Modeling and User-Adapted Interaction , 29 :2 , (345-379), Online publication date: 1-Apr-2019 .
Felfernig A, Polat-Erdeniz S, Uran C, Reiterer S, Atas M, Tran T, Azzoni P, Kiraly C and Dolui K (2019). An overview of recommender systems in the internet of things, Journal of Intelligent Information Systems , 52 :2 , (285-309), Online publication date: 1-Apr-2019 .
Dias A and Wives L (2019). Recommender system for learning objects based in the fusion of social signals, interests, and preferences of learner users in ubiquitous e-learning systems, Personal and Ubiquitous Computing , 23 :2 , (249-268), Online publication date: 1-Apr-2019 .
Loni B, Pagano R, Larson M and Hanjalic A (2019). Top-N Recommendation with Multi-Channel Positive Feedback using Factorization Machines, ACM Transactions on Information Systems , 37 :2 , (1-23), Online publication date: 20-Mar-2019 .
Zhong S and Xu H Intelligently recommending key bindings on physical keyboards with demonstrations in Emacs Proceedings of the 24th International Conference on Intelligent User Interfaces, (12-17)
Chow K, He S, Tan J and Chan S (2019). Efficient Locality Classification for Indoor Fingerprint-Based Systems, IEEE Transactions on Mobile Computing , 18 :2 , (290-304), Online publication date: 1-Feb-2019 .
Srikanth T and Shashi M (2019). An effective preprocessing algorithm for model building in collaborative filtering-based recommender system, International Journal of Business Intelligence and Data Mining , 14 :4 , (489-503), Online publication date: 1-Jan-2019 .
Chan G, Xu P, Dai Z and Ren L (2018). V i B r : Visualizing Bipartite Relations at Scale with the Minimum Description Length Principle, IEEE Transactions on Visualization and Computer Graphics , 25 :1 , (321-330), Online publication date: 1-Jan-2019 .
Chantamunee S, Wong K and Fung C Collaborative Filtering for Personalised Facet Selection Proceedings of the 10th International Conference on Advances in Information Technology, (1-5)
Teodorescu O, Popescu P and Mihaescu M Taking e-Assessment Quizzes - A Case Study with an SVD Based Recommender System Intelligent Data Engineering and Automated Learning – IDEAL 2018, (829-837)
Vahidi Ferdousi Z, Colazzo D and Negre E CBPF: Leveraging Context and Content Information for Better Recommendations Advanced Data Mining and Applications, (381-391)
Jasberg K and Sizov S Neuroscientific User Models: The Source of Uncertain User Feedback and Potentials for Improving Web Personalisation Web Information Systems Engineering – WISE 2018, (422-437)
D'Addio R, Fressato E, da Costa A and Manzato M Incorporating Semantic Item Representations to Soften the Cold Start Problem Proceedings of the 24th Brazilian Symposium on Multimedia and the Web, (157-164)
de Souza P and Durão F RecTwitter Proceedings of the 24th Brazilian Symposium on Multimedia and the Web, (371-378)
Feng Z and Favier L Objective Evaluation or Subjective Evaluation in Digital Social Media Proceedings of the 1st International Conference on Digital Tools & Uses Congress, (1-4)
Lu X, Wen Z and Kveton B Efficient online recommendation via low-rank ensemble sampling Proceedings of the 12th ACM Conference on Recommender Systems, (460-464)
Pessemier T and Martens L (2018). Heart rate monitoring, activity recognition, and recommendation for e-coaching, Multimedia Tools and Applications , 77 :18 , (23317-23334), Online publication date: 1-Sep-2018 .
Wang D and Wang C A New Asymmetric User Similarity Model Based on Rational Inference for Collaborative Filtering to Alleviate Cold Start Problem Intelligent Computing Theories and Application, (467-478)
Tramontin A, Gasparini I and Pereira R Using Social Elements to Recommend Sessions in Academic Events Human Interface and the Management of Information. Information in Applications and Services, (200-210)
Atas M, Reiterer S, Felfernig A, Tran T and Stettinger M Polarization Effects in Group Decisions Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, (305-310)
Pecli A, Cavalcanti M and Goldschmidt R (2018). Automatic feature selection for supervised learning in link prediction applications, Knowledge and Information Systems , 56 :1 , (85-121), Online publication date: 1-Jul-2018 .
Cardoso P, Guerreiro P, Pereira J and Veiga R A Route Planner Supported on Recommender Systems Suggestions Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion, (144-151)
Tahmasebi M, Ghazvini F and Esmaeili M (2018). Implementation and evaluation of a resource-based learning recommender based on learning style and web page features, Journal of Web Engineering , 17 :3-4 , (284-304), Online publication date: 1-Jun-2018 .
Huang H, Zhao B, Zhao H, Zhuang Z, Wang Z, Yao X, Wang X, Jin H and Fu X A Cross-Platform Consumer Behavior Analysis of Large-Scale Mobile Shopping Data Proceedings of the 2018 World Wide Web Conference, (1785-1794)
Jasberg K and Sizov S Human uncertainty and ranking error Proceedings of the 33rd Annual ACM Symposium on Applied Computing, (1358-1365)
Horowitz D, Contreras D and Salam M (2018). EventAware, Pattern Recognition Letters , 105 :C , (121-134), Online publication date: 1-Apr-2018 .
Liu R, Liang J, Gao W and Yu R (2018). Privacy-based recommendation mechanism in mobile participatory sensing systems using crowdsourced users preferences, Future Generation Computer Systems , 80 :C , (76-88), Online publication date: 1-Mar-2018 .
Phan L, Huynh H and Huynh H Hybrid recommendation based on implicative rating measures Proceedings of the 2nd International Conference on Machine Learning and Soft Computing, (50-56)
(2018). Hierarchical classification of web search results to detect users, International Journal of Artificial Intelligence and Soft Computing , 6 :4 , (287-305), Online publication date: 1-Jan-2018 .
Hamidi H and Mousavi R (2018). Analysis and Evaluation of a Framework for Sampling Database in Recommenders, Journal of Global Information Management , 26 :1 , (41-57), Online publication date: 1-Jan-2018 .
Hu Q, Zhao Z, Wang C and Lai J (2017). An item orientated recommendation algorithm from the multi-view perspective, Neurocomputing , 269 :C , (261-272), Online publication date: 20-Dec-2017 .
Zheng Y, Wang Y, Zhang L, Wang J and Qi Q A Tag-Based Integrated Diffusion Model for Personalized Location Recommendation Neural Information Processing, (327-337)
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Hamidi H and Hashemzadeh E (2017). An Approach to Improve Generation of Association Rules in Order to Be Used in Recommenders, International Journal of Data Warehousing and Mining , 13 :4 , (1-18), Online publication date: 1-Oct-2017 .
Orso V, Varotto A, Rodaro S, Spagnolli A, Jacucci G, Andolina S, Leino J and Gamberini L A two-step, user-centered approach to personalized tourist recommendations Proceedings of the 12th Biannual Conference on Italian SIGCHI Chapter, (1-5)
Verstrepen K, Bhaduriy K, Cule B and Goethals B (2017). Collaborative Filtering for Binary, Positiveonly Data, ACM SIGKDD Explorations Newsletter , 19 :1 , (1-21), Online publication date: 1-Sep-2017 .
Jasberg K and Sizov S The Magic Barrier Revisited Proceedings of the Eleventh ACM Conference on Recommender Systems, (56-64)
Sottocornola G, Stella F, Zanker M and Canonaco F Towards a deep learning model for hybrid recommendation Proceedings of the International Conference on Web Intelligence, (1260-1264)
Amami M, Faiz R, Stella F and Pasi G A graph based approach to scientific paper recommendation Proceedings of the International Conference on Web Intelligence, (777-782)
Morawski J, Stepan T, Dick S and Miller J (2017). A Fuzzy Recommender System for Public Library Catalogs, International Journal of Intelligent Systems , 32 :10 , (1062-1084), Online publication date: 3-Aug-2017 .
Aghdam M, Analoui M and Kabiri P (2017). Collaborative filtering using non-negative matrix factorisation, Journal of Information Science , 43 :4 , (567-579), Online publication date: 1-Aug-2017 .
Vahedian F, Burke R and Mobasher B (2017). Multirelational Recommendation in Heterogeneous Networks, ACM Transactions on the Web , 11 :3 , (1-34), Online publication date: 12-Jul-2017 .
Delic A and Neidhardt J A Comprehensive Approach to Group Recommendations in the Travel and Tourism Domain Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, (11-16)
Quijano-Sanchez L, Sauer C, Recio-Garcia J and Diaz-Agudo B (2017). Make it personal, Expert Systems with Applications: An International Journal , 76 :C , (36-48), Online publication date: 15-Jun-2017 .
Campelo D, Silva T and Ferraz de Abreu J Recommending Personalized Informative Contents on iTV Adjunct Publication of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video, (99-103)
Guerraoui R, Kermarrec A, Lin T and Patra R (2017). Heterogeneous recommendations, Proceedings of the VLDB Endowment , 10 :10 , (1070-1081), Online publication date: 1-Jun-2017 .
Sacha D, Al-Masoudi F, Stein M, Schreck T, Keim D, Andrienko G and Janetzko H (2017). Dynamic Visual Abstraction of Soccer Movement, Computer Graphics Forum , 36 :3 , (305-315), Online publication date: 1-Jun-2017 .
Liu X, Xu A, Akkiraju R and Sinha V Understanding Purchase Behaviors through Personality-driven Traces Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, (1837-1843)
Park Y Recommending Personalized Tips on New Courses for Guiding Course Selection Proceedings of the 2017 ACM Southeast Conference, (172-174)
Ragone A, Tomeo P, Magarelli C, Di Noia T, Palmonari M, Maurino A and Di Sciascio E Schema-summarization in linked-data-based feature selection for recommender systems Proceedings of the Symposium on Applied Computing, (330-335)
Wang J and Kawagoe K Ukiyo-e Recommendation based on Deep Learning For Learning Japanese Art and Culture Proceedings of the 2017 International Conference on Information System and Data Mining, (119-123)
Polatidis N, Georgiadis C, Pimenidis E and Mouratidis H (2017). Privacy-preserving collaborative recommendations based on random perturbations, Expert Systems with Applications: An International Journal , 71 :C , (18-25), Online publication date: 1-Apr-2017 .
Polatidis N and Georgiadis C (2017). A dynamic multi-level collaborative filtering method for improved recommendations, Computer Standards & Interfaces , 51 :C , (14-21), Online publication date: 1-Mar-2017 .
Xie Y, Ding C, Gong Y and Wu Z Rank ordering constraints elimination with application for kernel learning Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, (2775-2781)
Cremonesi P, Elahi M and Garzotto F (2017). User interface patterns in recommendation-empowered content intensive multimedia applications, Multimedia Tools and Applications , 76 :4 , (5275-5309), Online publication date: 1-Feb-2017 .
Ranjbar Kermany N and Alizadeh S (2017). A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques, Electronic Commerce Research and Applications , 21 :C , (50-64), Online publication date: 1-Jan-2017 .
Mutlu B, Veas E and Trattner C (2016). VizRec, ACM Transactions on Interactive Intelligent Systems , 6 :4 , (1-39), Online publication date: 26-Dec-2016 .
Guo D, Zhu Y, Xu W, Shang S and Ding Z (2016). How to find appropriate automobile exhibition halls, Neurocomputing , 213 :C , (95-101), Online publication date: 12-Nov-2016 .
Karydi E and Margaritis K (2016). Parallel and Distributed Collaborative Filtering, ACM Computing Surveys , 49 :2 , (1-41), Online publication date: 11-Nov-2016 .
Rodriguez-Lozano D, Gomez-Pulido J and Duran-Dominguez A Predicting Access Points Workload in Wi-Fi Infrastructures According to Users' Behavior Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, (1-6)
Zhang M, Wu Y, Chen K, Qian X, Li X and Zheng W Exploring the hidden dimension in graph processing Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, (285-300)
Elmisery A, Rho S and Botvich D (2016). Collaborative privacy framework for minimizing privacy risks in an IPTV social recommender service, Multimedia Tools and Applications , 75 :22 , (14927-14957), Online publication date: 1-Nov-2016 .
Ayachi R, Boukhris I, Mellouli S, Ben Amor N and Elouedi Z (2016). Proactive and reactive e-government services recommendation, Universal Access in the Information Society , 15 :4 , (681-697), Online publication date: 1-Nov-2016 .
Bafna P, Shirwaikar S and Pramod D Semantic Clustering Driven Approaches to Recommender Systems Proceedings of the 9th Annual ACM India Conference, (1-9)
Do L and Lauw H (2016). Probabilistic Models for Contextual Agreement in Preferences, ACM Transactions on Information Systems , 34 :4 , (1-33), Online publication date: 14-Sep-2016 .
He S, Tan J and Chan S Towards area classification for large-scale fingerprint-based system Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, (232-243)
Rossetti M, Stella F and Zanker M Contrasting Offline and Online Results when Evaluating Recommendation Algorithms Proceedings of the 10th ACM Conference on Recommender Systems, (31-34)
Nguyen T and Lauw H Representation Learning for Homophilic Preferences Proceedings of the 10th ACM Conference on Recommender Systems, (317-324)
Marshall J and Wang D Mood-Sensitive Truth Discovery For Reliable Recommendation Systems in Social Sensing Proceedings of the 10th ACM Conference on Recommender Systems, (167-174)
Karim R, Ding C, Miri A and Rahman M (2016). Incorporating service and user information and latent features to predict QoS for selecting and recommending cloud service compositions, Cluster Computing , 19 :3 , (1227-1242), Online publication date: 1-Sep-2016 .
Moon H, Yoon J and Kim J The impact of information amount on the performance of recommender systems Proceedings of the 18th Annual International Conference on Electronic Commerce: e-Commerce in Smart connected World, (1-6)
Zhan K, Zukerman I, Moshtaghi M and Rees G Eliciting Users' Attitudes toward Smart Devices Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, (175-184)
Nishioka C and Scherp A Profiling vs. Time vs. Content Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, (171-180)
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Reviewer: Maulik A Dave
Internet users typically search for items such as books and computers. Recommender systems provide not only the result of the search, but also a list of other items that the user may be interested in. This book describes many approaches to building recommender systems, ranging from a simple neighborhood approach to complex knowledge-based approaches. The most modern approaches are also covered. The first part covers the basics of recommender systems, and the second part covers modern challenges facing recommendation systems. After a brief overview of the contents of the book in the first chapter, a detailed description of collaborative recommendation follows in chapter 2. Collaborative approaches use the past behavior of the user community for recommendations. The chapter describes memory-based approaches, including user-based nearest neighbor and item-based nearest neighbor. It discusses model-based approaches, in which the raw data is preprocessed, in detail. The discussion of model-based approaches includes associative rules mining and probabilistic approaches. The chapter ends by describing Slope One predictors. Chapter 3 is on content-based approaches, in which the characteristics of items play a central role in recommendations. The vector space model and text classification methods are described in detail, and decision models are mentioned briefly. Chapter 4 is on knowledge-based approaches, in which the user interacts with the system and receives recommendations based on knowledge incorporated in the system. The knowledge-based approaches are classified as constraint-based and case-based. Both types of approaches are discussed in detail, along with some key algorithms and user interaction considerations. The chapter ends with two practical examples. Combining the various approaches is called hybridization, which is the subject of chapter 5. Various kinds of hybridization designs-monolithic, parallel, and pipelined-are presented. Feature combination hybrids, feature augmentation hybrids, hybrids mixed at the user level, hybrids combining weighted scores, hybrids switching depending on situations, hybrids with a sequenced order of techniques, and meta-level hybrids are all discussed. Chapter 6, on explanations in recommender systems, provides mathematical details of well-founded explanations in constraint-based recommenders. It concludes with a brief explanation of the case-based and collaborative recommendation systems. Chapter 7 focuses on the evaluation of recommender systems. It begins by describing general properties of evaluations. The methodology of evaluations based on historical datasets is described later. The chapter ends by showing some experimental designs for evaluation. Chapter 8 explains the evaluation of an example: personalized game recommendations on the Internet. Measurements, described with their results, are "my recommendation," post-sale recommendations, start page recommendations, and overall effects. Chapter 9 is on possible attacks on collaborative recommender systems. It presents various types of attacks, including random, average, bandwagon, segment, nuke, and clickstream attacks. Techniques such as increasing injection costs, additional information on profiles, and automated attack detection are discussed as countermeasures. The chapter ends with a discussion on the privacy aspects of collaborative filtering. Chapter 10 is on aspects of consumer decision making. The factors likely to affect the decision-making processes are discussed, as are personality-based and social psychology-based factors. Chapter 11 is on the challenges facing recommenders in the next generation. Recommenders using explicit trust networks are discussed, followed by a detailed discussion on tag-based recommendations. The chapter concludes by discussing ontological filtering approaches. Chapter 12 covers recommenders in ubiquitous environments. A brief overview of recommendations for various application domains, such as mobile commerce and tourism guides, is presented. Chapter 13 is a summary, and is followed by a 25-page bibliography. For learning about the basics of recommender systems, this book is sufficient, although knowledge of elementary mathematics is necessary to understand the formulas presented. Online Computing Reviews Service
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WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03
Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based .
