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Recommender Systems - A Review and Recent Research Trends

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Recommender Systems - A Review and Recent Research Trends

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Recommender Systems - A Review and Recent Research Trends

  1. 1. Contents • What is a recommender system? • Year wise distribution of research work on recommender system(2010-2014) • What characterizes a recommender system? • An year wise distribution of papers published between 2010-2014 in the field of Recommender System using the Collaborative Filtering • Why a recommender system? • Applications of recommender system • Outline of the review process • Distribution of Papers collected on the Basis of Publishers • Publications of Top Journals from the collected set of papers • Top-10 cited papers from the collected set of papers • Top-10 cited papers between 2010-2014 & 2013-2014 from the collected set of papers • Datasets • Application & technique based categorization of the collected papers • How to measure the recommendation quality ? • Commercial existence of Recommender System • Recent research trends and future prospects • Reference list I & S E |IIT KGP
  2. 2. Recommender System  Recommender Systems collect information on the preferences of its users for a set of items, say, movies, songs, books, gadgets. [Bobadilla, Ortega et al.(2013)].  They suggest items to the users based on the features of the items or user’s preferences.  The recommender system can acquire information implicitly by monitoring user’s behavior [Cho , Lee et. al.(2010)] and explicitly by collecting user’s rating to any item [Cho , Lee et. al.(2010)]. I & S E |IIT KGP
  3. 3. A few examples: I & S E |IIT KGP - While purchasing a book on Recommender Systems on Amazon.com, some other books are also recommended
  4. 4. I & S E |IIT KGP A few examples: - While using facebook, some new groups or/and friends are suggested
  5. 5. An year wise distribution of papers published between 2010-2014 in the field of Recommender System I & S E |IIT KGP The numbers indicated are the sum of numbers obtained by searching papers using the keywords “recommender system/application of recommender systems” in the libraries of IEEE, INFORMS, ELSEVIER.
  6. 6. What Characterizes a Recommender System? I & S E |IIT KGP Types of filtering algorithms Collaborative filtering Content- based filtering Demographic Filtering Hybrid filtering • RS is characterized by the filtering algorithm used in it [Adomavicius, Tuzhilin et.al.(2005), Candillier, Meye et.al.(2007)], the different filtering algorithms are cited below:
  7. 7. An year wise distribution of papers published between 2010-2014 in the field of Recommender System using the Collaborative Filtering I & S E |IIT KGP The numbers indicated are the sum of numbers obtained by searching papers using the keywords “recommender system/application of recommender systems” in the libraries of IEEE, INFORMS, ELSEVIER.
  8. 8. Why a Recommender System? I & S E |IIT KGP NEED OF RECOMMENDER SYSTEM For Service Provider Helps in deciding what kind of offerings should be made to the user For the User Helps in choosing among a large number of articles or products
  9. 9. I & S E |IIT KGP APPLICATION OF RECOMMENDER SYSTEM Movie and Music Recommendation Books Search E-Commerce Travel/Tourism Recommendation Web Search
  10. 10. Outline of the Review Process I & S E |IIT KGP Step 1 • We collected a Primary Set of 152 research papers published in the field of Recommender system in between 2014-15 form reputed journals of the publishers like IEEE, Science Direct, INFORMS, SPRINGER, ACM, WILEY. Step 2 • All the references of the papers of Primary Set are merged and the irrelevant ( the papers not required to understand the growing research trend in RS or which were published before 2000 )and repetitive references are omitted to form a Secondary Set of 135 papers. These papers are also from the above mentioned publishers. Step 3 • All the papers from both sets are tabulated using a 2-D criteria. • The first dimension is the Name of the Publisher and then the next is the name of the Journal.
  11. 11. Distribution of Papers collected on the Basis of Publishers I & S E |IIT KGP IEEE - 75(25 Journals) INFORMS - 21(6 Journals) ELSEVIER - 126(22 Journals) SPRINGER - 27(10 Journals) ACM - 29(8 Journals) WILEY - 9(4 Journals)
  12. 12. Papers published in Top Journals of the collected set of papers I & S E |IIT KGP IEEE Journal No. of papers published Impact Factor (2013) Knowledge and Data Engineering 15 1.815 Multimedia 8 1.767 Learning Technologies 7 1.22 Intelligent Systems 7 1.92 Internet Computing 7 2.0 INFORMS Journal No. of papers published Impact Factor (2013) Management Science 6 2.524 Informs Journal on Computing 6 1.12 Operations Research 3 1.5 Marketing Science 3 2.208 Information Systems Research 2 2.322
  13. 13. Papers published in Top Journals of the collected set of papers I & S E |IIT KGP ELSEVIER Journal No. of papers published Impact Factor (2013) Experts Systems with Applications 48 1.965 Knowledge based Systems 19 3.058 Computers in Human Behavior 5 2.273 Information Sciences 10 3.893 Decision Support Systems 13 2.036 SPRINGER Journal No. of papers published Impact Factor (2013) Multimedia Tools Application 3 1.058 Information Retrieval 3 0.625 Soft Computing 2 1.304 Journal of Intelligent Information 1 0.632 Knowledge & Information Systems 1 2.639
  14. 14. Papers published in Top Journals of the collected set of papers I & S E |IIT KGP ACM Journal No. of papers published Impact Factor (2013) Information Systems 8 1.3 Computing Surveys 6 4.043 Multimedia Computing, Communications and Applications 5 0.904 Internet Technology 4 0.577 Knowledge Discovery from Data 3 1.147 WILEY Journal No. of papers published Impact Factor (2013) Journal of the Association for Information Science & Technology 4 2.23 International Journal of Communication Systems 3 1.106 Software – Practice & Experience 1 1.148 International Journal of Intelligent Systems 1 1.411
  15. 15. Top-10 cited papers from the collected set of papers I & S E |IIT KGP Authors Citations Adomavicius & Tuzhilin (2005) 5532 Herlocker, Konstan et al. (2004) 3484 Greg, Brent et al. (2003) 2952 Josang, Ismail et al. (2007) 2301 Dellarocas (2003) 2083 Deshpande & Karypis ( 2004) 1241 Goldberg, Roeder et al. (2001) 1007 Shim, Warkentin et al. (2002) 977 Adomavicius, Sankaranarayanan et al. (2005) 624 Francois, Alain et al. (2007) 568
  16. 16. Top-10 cited papers between 2010- 2014 from the collected set of papers I & S E |IIT KGP Authors Citations Koren(2010) 242 Feng, Li et al. (2010) 210 Bobadilla, Ortega et al.(2013) 158 Liu & Lee(2010) 132 Lee(2012) 128 Cambria, Schuller et al.(2013) 117 Barragáns, Costa et al.(2010) 112 Bandyopadhyay & Sen(2011) 111 Park, Kim et al.(2012) 105 Verbert, Manouselis et al.(2012) 83
  17. 17. Top-10 cited papers between 2013- 2014 from the collected set of papers I & S E |IIT KGP Authors Citations Bobadilla, Ortega et al.(2013) 158 Cambria, Schuller et al.(2013) 117 Mostafa (2013) 42 Yang, Guo et al.(2013) 40 Tao, Yong et al.(2014) 24 Shi, Larson et al.(2014) 15 Liu, Chen et al.(2014) 9 Bi, Xu et al.(2014) 8 Kaklauskas, Zavadskas et al.(2013) 8 Charu, Yuchen et al.(2014) 6
  18. 18. Datasets The various datasets used while researching the recommenders systems are cited here: • Netflix- The dataset provided by Netflix Inc. which is an online video service provider • MovieLens- The dataset provided by MovieLens. It is website that recommends movies to its users (http://grouplens.org/datasets/movielens/) • Sushi- This dataset stores responses of questionnaire survey and the demographic data of the respondents (http://www.kamishima.net/sushi) • Wikilens- It is dataset of Wikilens recommender system which facilitated its community to define item types and categories to be rated (http://grouplens.org/datasets/wikilens) • The TAQ dataset- It provides historical data of trades and quotes(TAQ) for all issues traded on NYSE, NASDAQ and Regional stock exchanges (http://www.nyxdata.com/Data-Products/Daily-TAQ) • Jester- The dataset used while online joke recommendation. (http://goldberg.berkeley.edu/jesterdata) I & S E |IIT KGP
  19. 19. Datasets • Delicious Bookmarks- This dataset contains social networking, bookmarking, and tagging information. (http://grouplens.org/datasets/hetrec-2011/) • Choose4Greece- This dataset has social voting features . It was launched during the Greek national elections of 2012 . (http://www.preferencematcher.com/datasets/) • Art of the Mix(AotM)- This dataset is used in researches in music recommendation and contains the playlist retrieved from the Art of the Music website. (http://labrosa.ee.columbia.edu/projects/musicsim/) • Last.fm- This is a song recommendation dataset. It is created using the last.fm API. (http://labrosa.ee.columbia.edu/millionsong/lastfm#getting) I & S E |IIT KGP
  20. 20. Application based categorization of the collected papers I & S E |IIT KGP Area Authors Music recommendation Turnbull, Barrington et al. (2008), Cornelis, Lesaffre et al.(2010), Jacobson, Fields et al. (2011), Chen, Jang et al. (2011), Bonnin & Jannach (2014), Lee & Lee(2014), Horsburgh, Craw et al.(2015) . Movie recommendation Ricci(2002),Hosanagar & Fleder(2009),Yu, Liu et al.(2012), Carrer, Hernández et al.(2012), Choi, Ko et al.(2012), Uysal & Gunal(2012), Eliashberg, Hui et al.(2014), Briguez, Budán et al.(2014), Mendoza, Garcia et al.(2015). Travel/Tourism Shih,Yen et al.(2011), Batet, Moreno et al.(2012), Liu, Xu et al.(2014), Tan, Liu et al.(2014), Liu, Chen et al.(2014), Borràs, Moreno et al. (2014).
  21. 21. Application based categorization of the collected papers I & S E |IIT KGP Area Authors Text analysis Fung, Yu et al. (2005), Louloudis, Gatos et al.(2008), Aghdam, Ghasem et al. (2009),Yin, Liu(2009), Liu, Loh et al.(2009), Lee (2012), Maks & Vossen(2012), Mostafa (2013), Zhao, Aggarwal et al.(2014), Dnyanesh & Singh(2014), Ryu, Hyung et al.(2014), Hao, Cao et al. (2014), Hashimi, Hafez et al.(2015). Sentiment Analysis Duric & Song(2012), Maks & Vossen (2012), Uysal & Gunal (2012), Desmet & Hoste (2013), Cambria, Schuller et al. (2013), Mostafa(2013), Moraes & Valiat(2013), Xuan & Stieglitz(2013), Hendrikx, Bubendorfer et al.(2015).
  22. 22. Technique based categorization of the collected papers I & S E |IIT KGP Technique Authors Collaborative Filtering Linden, Smith et al.(2003), Greg, Brent et al.(2003), Huang & Zang(2004), Hofmann T (2004), Herlocker, Konstan et.al.(2004), Jin, Si et al.(2006), Bridge & Ryan (2006), Fouss, Pirotte et al.(2007), Ahn(2008), Serradilla, Bobadilla et al.(2009), Chen, Shtykh et al.(2009), Liu & Lee(2010), Barranquero, Labra et al.(2010), Koren (2010), Zhan, Hsieh et al.(2010), Anand & Bharadwaj (2011), ), Huang, Zeng et al.(2011), Bobadilla, Ortega et al. (2012), Cai, Leung et al.(2014), Hsiao, Kulesza et al.(2014), Javari, Gharibshah et al.(2014), Yang, Guo et al.(2014), Rivera, Ruiz et al.(2014), Pereira, Lopes et al.(2014), Li, Chena et al.(2014).
  23. 23. Technique based categorization of the collected papers I & S E |IIT KGP Technique Authors Collaborative Filtering Peng, Zhang et al.(2014), Zhanga, Yua et al. (2014), Zhua, Renb et al. (2014), Liu, Hu et al.(2014), Toledo, Mota et al. (2015), Nilashi, Jannach et al.(2015) , Krzywicki, Wobcke et el.(2015), Ghazarian & Nematbakhsh (2015), Braida, Mello et al. (2015), Valdez, Lovelle et al. (2015). Fuzzy approaches Yager(2003), Cao & Li (2007), Norcio & Zenebe(2009), Castellano, Fanelli et al.(2011), Nilashi, Ibrahim et al.(2014), Son (2014), Zhang, Dianshuang et al.(2015), Wang, Zeng et al.(2015), Gupta, Saini et al. (2015), Thong, Son et al. (2015)A, Son & Thong (2015)B.
  24. 24. Technique based categorization of the collected papers I & S E |IIT KGP Technique Authors Hybrid Filtering Liu, Lai et al.(2009), Barragáns, Costa et al. (2010) , Park, Lee et al.(2013), Liu(2014), Son (2014) , Thong & Son(2015)A ,Horsburgh, Craw et al.(2015). Content-based filtering Norcio & Zenebe(2009), Barragáns, Costa et al. (2010), Cornelis, Lesaffre et al.(2010), Chen, Jang et al. (2011), Meng, Chia et al. (2014). Bayesian classifications Yang, Guo et al.(2013), Liu &Wu et al.(2013), Tan, Liu et al.(2014), Baoxing, Enhong et al.(2014), Wei, Barry et al.(2014).
  25. 25. Survey papers in the set of collected papers I & S E |IIT KGP Authors Surveys Josang, Ismail et al.(2007), Carpineto & Romano (2012), Kim, Park et al. (2012), Verbert,Manouselis et al.(2012), Bobadilla, Ortega et al.(2013), Natalia, Cue´ Llar et al. (2014), Bonnin & Jannach(2014), Huai, Chen et al.(2014), Woz´Niak , Grana et al.(2014), Nassirtoussi, Aghabozorgi et al.(2014), Champiri, Shahamiri et al. (2015).
  26. 26. How to measure the recommendation quality ? I & S E |IIT KGP • A recommender system can be evaluated using quality measures [Gunawardana & Shani(2009)] and evaluation metrics [Hernandez , Gaudioso et.al.(2008)] and can be categorized as: Quality Measures Prediction evaluations Evaluations for recommendation as sets Evaluations for recommendation as ranked lists
  27. 27. I & S E |IIT KGP Evaluation Metrics Prediction Metrics MAE, RMSE, Coverage Set Recommendation Metrics Precision, Recall, F1 Rank Recommendation Metrics HL, DCGK Diversity Metrics Diversity and Novelty
  28. 28. Commercial existence of Recommender System I & S E |IIT KGP • A large number of recommender systems currently available on the Internet. • Commercial websites devised tailored solution to help their user find items and increase sale • The following table describes special features of a few commercial systems: System Feature MovieLens •Collaborative filtering •Builds a profile by asking the user to rate the movie •Searches for similar profile •Stochastic and heuristic models to improve profile matching
  29. 29. I & S E |IIT KGP System Feature Pandora •Deep item analysis (Music Genome Project theory) •User preference represented in term of a collection of items Amazon •Combined approach (personalized, social and item based) •Recommendation based on matching of: actual items, related items, items other user purchased, new release, related items to new release Google •Customize search result based on location and recent search activity(“when possible”) •Customize results based on account history •Uses pages link structure(social recommendation) •Recommendation to closest match (The “Did You Mean” feature)
  30. 30. Recent Research Trends & Future Prospects I & S E |IIT KGP  Online reputation and polling systems[Krishnamurthy and Holies(2014)]  Social voting advice applications[Katakis, Tsapatsoulis et. al. (2014)]  The most valuable collaborators recommendation[Xia, Chen et. al. (2014)]  The concept of Internet of Things [Bi, Xu et. al.(2014)]
  31. 31. Recent Research Trends & Future Prospects I & S E |IIT KGP  Dynamically promoting experts for recommendation [Lee and Lee(2014)]  Tree-based recommendation using fuzzy preferences [Wu, Zhang et al.(2014)]  Cloud computing and recommendation[Campo, Pegueroles et al. (2014)]  Security and privacy in filtering algorithms[Casino, Ferrer et al.(2014)]  Diversity improvement in recommendations[Adomavicius & Kwon et al.(2014)
  32. 32. Recent Research Trends & Future Prospects I & S E |IIT KGP  Evaluation of filtering algorithms using novel methods[ Banda & Bhardwaj (2014)]  Detection and correction of natural noise in filtering techniques [Toledo, Mota et. al.(2015)]  Fashion recommendation [Weng and Zeng,(2015)]  Recommendation in medical and health[Thong, Son et. al.(2015)]  Recommendation system in bibliometrics and scientometrics [Lorente, Porcel et. al.(2015)]
  33. 33. I & S E |IIT KGP Reference List • Guangtao Wang, Qinbao Song, Xueying Zhang, And Kaiyuan Zhang, A Generic Multilabel Learning-Based Classification Algorithm Recommendation Method, ACM Transactions On Knowledge Discovery From Data, Vol. 9, No. 1, Article 7, Publication Date: October 2014. • Natalia D´Iaz Rodr´Iguez, M. P. Cue´ Llar, Johan Lilius, Miguel Delgado Calvo-Floresa, Survey On Ontologies For Human Behavior Recognition, ACM Computing Surveys, Vol. 46, No. 4, Article 43, Publication Date: March 2014. • Amin Javari And Mahdi Jalili, Accurate And Novel Recommendations: An Algorithm Based On Popularity Forecasting, ACM Transactions On Intelligent Systems And Technology, Vol. 5, No. 4, Article 56, Publication Date: December (2014)A. • Geoffray Bonnin And Dietmar Jannach, Automated Generation Of Music Playlists: Survey And Experiments, ACM Computing Surveys, Vol. 47, No. 2, Article 26, Publication Date: November 2014 • Peter Christen, Dinusha Vatsalan, Vassilios S. Verykios, Challenges For Privacy Preservation In Data Integration, ACM Journal Of Data And Information Quality, Vol. 5, Nos. 1–2, Article 4, Publication Date: August 2014. • Yue Shi, Martha Larson, And Alan Hanjalic, Collaborative Filtering Beyond The User-Item Matrix: A Survey Of The State Of The Art And Future Challenges, ACM Computing Surveys, Vol. 47, No. 1, Article 3, Publication Date: April 2014. • Tao Mei, Yong Rui, Shipeng Li, & Qi Tian, Multimedia Search Re-ranking: A Literature Survey, ACM Computing Surveys, Vol. 46, No. 3, Article 38, Publication Date: January 2014 • Chang Tan, Qi Liu, Enhong Chen, Hui Xiong & Xiang Wu, Object-Oriented Travel Package Recommendation, ACM Transactions On Intelligent Systems And Technology, Vol. 5, No. 3, Article 43, Publication Date: September 2014. • Panagiotis Adamopoulos And Alexander Tuzhilin, On Unexpectedness In Recommender Systems: Or How To Better Expect The Unexpected, ACM Transactions On Intelligent Systems And Technology, Vol. 5, No. 4, Article 54, Publication Date: December 2014. • Qinghua Huang, Bisheng Chen, Jingdong Wang And Tao Mei, Personalized Video Recommendation Through Graph Propagation, ACM Transactions On Multimedia Computing, Communications And Applications, Vol. 10, No. 4, Article 32, Publication Date: June 2014. • Y. Song, L. Zhang, and C. L. Giles, “Automatic tag recommendation algorithms for social recommender systems,” ACM Trans. Web, vol. 5, no. 1, 2011. • S.Lee,W.D.Neve, K. N. Plataniotis, and Y. M. Ro, “Map-based image tag recommendation using a visual folksonomy,” Pattern Recognit. Lett., vol. 31, no. 9, pp. 976–982, 2010. • H. M. Yu, W. H. Tsai, and H. M. Wang, “A query-by-singing system for retrieving karaoke music,” IEEE Trans. Multimedia, vol. 10, no. 8, pp. 1626–1637, 2008
  34. 34. I & S E |IIT KGP Reference List • Soudip Roy Chowdhury, Florian Daniel And Fabio Casati Recommendation And Weaving Of Reusable Mashup Model Patterns For Assisted Development, ACM Transactions On Internet Technology, Vol. 14, No. 2-3, Article 21, Publication Date: October 2014. • Al´Ipio M. Jorge, Adam Jatowt, Gae¨ L Dias, Ricardo Campos, Survey Of Temporal Information Retrieval And Related Applications, ACM Computing Surveys, Vol. 47, No. 2, Article 15, Publication Date: July 2014. • Baoxing Huai, Enhong Chen, Hengshu Zhu, Hui Xiong, Tengfei Bao, Qi Liu, Jilei Tian, Toward Personalized Context Recognition For Mobile Users: A Semi-Supervised Bayesian HMM Approach, ACM Transactions On Knowledge Discovery From Data, Vol. 9, No. 2, Article 10, Publication Date: September 2014. • Changsheng Xu, Zhengyu Deng, Ming Yan, Jitao Sang, Twitter Is Faster: Personalized Time-Aware Video Recommendation From Twitter To Youtube, ACM Trans. Multimedia Comput. Commun. Appl., Vol. 11, No. 2, Article 31, Publication Date: December 2014. • Michalis Katsarakis, Georgios Fortetsanakis, Paulos Charonyktakis, Alexandros Kostopoulos, And Maria Papadopouli,On User- Centric Tools For Qoe-Based Recommendaton And Real-Time Analysis Of Large-Scale Markets, IEEE Communications Magazine, September 2014. • Hong Yu And Mark O. Riedl, Personalized Interactive Narratives Via Sequential Recommendation Of Plot Points, IEEE Transactions On Computational Intelligence And AI In Games, Vol. 6, No. 2, June 2014. • Vikram Krishnamurthy, William Hoiles, Online Reputation And Polling Systems: Data Incest, Social Learning, And Revealed Preferences, IEEE Transactions On Computational Social Systems, Vol. 1, No. 3, September 2014. • Ioannis Katakis, Nicolas Tsapatsoulis, Fernando Mendez, Vasiliki Triga, And Constantinos Djouvas, Social Voting Advice Applications -Definitions, Challenges, Datasets And Evaluation, IEEE Transactions On Cybernetics, Vol. 44, No. 7, July 2014. • Daqiang Zhang, Ching-Hsien Hsu, Min Chen, Quan Chen, Naixue Xiong, And Jaime Lloret,Cold-Start Recommendation Using Bi- Clustering And Fusion For Large-Scale Social Recommender Systems, Emerging Topics In Computing, Volume 2, No. 2, June 2014 • Feng Xia, Zhen Chen, Wei Wang, Jing Li, And Laurence T. Yang, Mvcwalker: Random Walk-Based Most Valuable Collaborators Recommendation Exploiting Academic Factors, Emerging Topics In Computing, Volume 2, No. 3, September 2014. • M. Hamada, “An Integrated Virtual Environment for Active and Collaborative e-Learning in Theory of Computation,” IEEE Trans . Learning Technologies, vol. 1, no. 2, pp. 117-130, Apr.–June 2008. • R. Kumar and C.P. Rose, “Architecture for Building Conversational Agents that Support Collaborative Learning,” IEEE Trans. Learning Technologies, vol. 4, no. 1, pp. 21-34, Jan.–Mar. 2011.
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