Hybrid Collaborative Recommendations: Practical Considerations and Tools to Develop a Recommender

[1]  Bradley N. Miller,et al.  Social Information Filtering : Algorithms for Automating “ Word of Mouth , ” , 2017 .

[2]  Yehuda Koren,et al.  The BellKor Solution to the Netflix Grand Prize , 2009 .

[3]  Qiong Wu,et al.  Modeling uncertainty driven curiosity for social recommendation , 2017, WI.

[4]  Mingxuan Sun,et al.  PREA: personalized recommendation algorithms toolkit , 2012, J. Mach. Learn. Res..

[5]  Domonkos Tikk,et al.  Scalable Collaborative Filtering Approaches for Large Recommender Systems , 2009, J. Mach. Learn. Res..

[6]  Sean M. McNee,et al.  Improving recommendation lists through topic diversification , 2005, WWW '05.

[7]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[8]  Florian Strub,et al.  Hybrid Recommender System based on Autoencoders , 2018 .

[9]  Michael R. Lyu,et al.  SoRec: social recommendation using probabilistic matrix factorization , 2008, CIKM '08.

[10]  Paolo Avesani,et al.  Trust-aware recommender systems , 2007, RecSys '07.

[11]  Royi Ronen,et al.  Selecting content-based features for collaborative filtering recommenders , 2013, RecSys.

[12]  Yong Liu,et al.  Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction , 2014, SIGIR.

[13]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[14]  Volker Markl,et al.  Distributed matrix factorization with mapreduce using a series of broadcast-joins , 2013, RecSys.

[15]  Luis M. de Campos,et al.  Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks , 2010, Int. J. Approx. Reason..

[16]  Yoram Singer,et al.  Local Low-Rank Matrix Approximation , 2013, ICML.

[17]  Anton van den Hengel,et al.  Image-Based Recommendations on Styles and Substitutes , 2015, SIGIR.

[18]  Charu C. Aggarwal,et al.  Recommender Systems: The Textbook , 2016 .

[19]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[20]  Paolo Cremonesi,et al.  Deriving Item Features Relevance from Past User Interactions , 2017, UMAP.

[21]  Lars Schmidt-Thieme,et al.  MyMediaLite: a free recommender system library , 2011, RecSys '11.

[22]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[23]  Francesco Ricci,et al.  Personality-Based Active Learning for Collaborative Filtering Recommender Systems , 2013, AI*IA.

[24]  Matthias Jarke,et al.  A Clustering Approach for Collaborative Filtering Recommendation Using Social Network Analysis , 2011, J. Univers. Comput. Sci..

[25]  Georg Lausen,et al.  Evaluating Hybrid Music Recommender Systems , 2013, 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[26]  Fabio Airoldi,et al.  Hybrid algorithms for recommending new items , 2011, HetRec '11.

[27]  Nicolas Hug,et al.  Surprise: A Python library for recommender systems , 2020, J. Open Source Softw..

[28]  Alan Said,et al.  WrapRec: an easy extension of recommender system libraries , 2014, RecSys '14.

[29]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[30]  Chun-Hua Tsai.  A Fuzzy-Based Personalized Recommender System for Local Businesses , 2016, HT.

[31]  Tobias Höllerer,et al.  TasteWeights: a visual interactive hybrid recommender system , 2012, RecSys.

[32]  Jurij F. Tasic,et al.  Predicting and Detecting the Relevant Contextual Information in a Movie-Recommender System , 2013, Interact. Comput..

[33]  Jason Weston,et al.  Large scale image annotation: learning to rank with joint word-image embeddings , 2010, Machine Learning.

[34]  Chao Liu,et al.  Recommender systems with social regularization , 2011, WSDM '11.

[35]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[36]  Martin Ester,et al.  A matrix factorization technique with trust propagation for recommendation in social networks , 2010, RecSys '10.

[37]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[38]  Maciej Kula,et al.  Metadata Embeddings for User and Item Cold-start Recommendations , 2015, CBRecSys@RecSys.

[39]  Maryam Ramezani,et al.  Matching Recommendation Technologies and Domains , 2011, Recommender Systems Handbook.

[40]  Neil Yorke-Smith,et al.  A Novel Evidence-Based Bayesian Similarity Measure for Recommender Systems , 2016, ACM Trans. Web.

[41]  Thore Graepel,et al.  WWW 2009 MADRID! Track: Data Mining / Session: Statistical Methods Matchbox: Large Scale Online Bayesian Recommendations , 2022 .

[42]  Qiang Yang,et al.  Rating Prediction with Informative Ensemble of Multi-Resolution Dynamic Models , 2012, KDD Cup.

[43]  Markus Zanker,et al.  Collaborative Feature-Combination Recommender Exploiting Explicit and Implicit User Feedback , 2009, 2009 IEEE Conference on Commerce and Enterprise Computing.

[44]  Alan Said,et al.  Comparative recommender system evaluation: benchmarking recommendation frameworks , 2014, RecSys '14.

[45]  CARLOS A. GOMEZ-URIBE,et al.  The Netflix Recommender System , 2015, ACM Trans. Manag. Inf. Syst..

[46]  Yi-Cheng Zhang,et al.  Solving the apparent diversity-accuracy dilemma of recommender systems , 2008, Proceedings of the National Academy of Sciences.

[47]  Yoon Ho Cho,et al.  A hybrid recommendation procedure for new items using preference boundary , 2009, ICEC.

[48]  Michael Hahsler.  recommenderlab: An R Framework for Developing and Testing Recommendation Algorithms , 2022, ArXiv.

[49]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[50]  Francesco Ricci,et al.  Switching hybrid for cold-starting context-aware recommender systems , 2014, RecSys '14.

[51]  John Riedl,et al.  Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit , 2011, RecSys '11.

[52]  Masataka Goto,et al.  An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[53]  Markus Zanker,et al.  A collaborative constraint-based meta-level recommender , 2008, RecSys '08.

[54]  George A. Tsihrintzis,et al.  Evaluation of a Cascade Hybrid Recommendation as a Combination of One-Class Classification and Collaborative Filtering , 2012, 2012 IEEE 24th International Conference on Tools with Artificial Intelligence.

[55]  John Riedl,et al.  When recommenders fail: predicting recommender failure for algorithm selection and combination , 2012, RecSys.

[56]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

[57]  Chan-Soo Park,et al.  A Hybrid Recommendation System Using Trust Scores in a Social Network , 2012 .

[58]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[59]  Bamshad Mobasher,et al.  Hybrid Recommendation in Heterogeneous Networks , 2014, UMAP.

[60]  Liang Wang,et al.  DeepStyle: Learning User Preferences for Visual Recommendation , 2017, SIGIR.

[61]  Juan C. Burguillo,et al.  A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition , 2010, Inf. Sci..

[62]  Diyi Yang,et al.  Local implicit feedback mining for music recommendation , 2012, RecSys.

[63]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[64]  Xu Chen,et al.  Learning to Rank Features for Recommendation over Multiple Categories , 2016, SIGIR.

[65]  Bernd Ludwig,et al.  InCarMusic: Context-Aware Music Recommendations in a Car , 2011, EC-Web.

[66]  Jiahui Liu,et al.  Personalized news recommendation based on click behavior , 2010, IUI '10.

[67]  Yehuda Koren,et al.  Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy , 2011, RecSys '11.

[68]  Adam Prügel-Bennett,et al.  Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems , 2014, Expert Syst. Appl..