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Tag-Aware Recommender Systems: A State-of-the-Art Survey

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Abstract

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.

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References

  1. Zhang G Q, Zhang G Q, Yang Q F, Cheng S Q, Zhou T. Evolution of the Internet and its cores. New J. Phys., 2008, 10(12): 123027.

    Article  Google Scholar 

  2. Fensel D, Hendler J, Lieberman H. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. Cambridge, US, 2003.

  3. Brin S, Page L. The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst., 1998, 30(1–7): 107–117.

    Article  Google Scholar 

  4. Resnick P, Varian H R. Recommender systems. Commun. ACM, 1997, 40(3): 56.

    Article  Google Scholar 

  5. Schafer J B, Konstan J, Riedl J. E-commerce recommendation applications. IEEE Internet Comput., 2001, 5(1–2): 115–153.

    MATH  Google Scholar 

  6. Linden G, Smith B, York J. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput., 2003, 7(1): 76–80.

    Article  Google Scholar 

  7. Bennett J, Lanning S. The netflix prize. In Proc. KDD Cup and Workshop, San Jose, USA, Aug. 12, 2007, pp.35-38.

  8. Ali K, van Stam W. TiVo: Making show recommendations using a distributed collaborative filtering architecture. In Proc. 10th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., Las Vegas, USA, Aug. 24–27, 2008, pp.394-401.

  9. Huang Z, Chen H, Zeng D. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst., 2004, 22(1): 116–142.

    Article  Google Scholar 

  10. Zhou T, Ren J, Medo M, Zhang Y C. Bipartite network projection and personal recommendation. Phys. Rev. E, 2007, 76(4): 046115.

    Article  Google Scholar 

  11. Herlocker J L, Konstan J A, Terveen L G, Riedl J T. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 2004, 22(1): 5–53.

    Article  Google Scholar 

  12. Resnick P, Kuwabara K, Zeckhauser R, Friedman E. Reputation systems. Commun. ACM, 2000, 43(12): 45–48.

    Article  Google Scholar 

  13. Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng., 2005, 17(6): 734–749.

    Article  Google Scholar 

  14. Kazienko P, Adamski M. AdROSA − Adaptive personalization of Web advertising. Info. Sci., 2007, 177(11): 2269–2295.

    Article  Google Scholar 

  15. Tso K, Schmidt-Thieme L. Attribute-aware collaborative filtering. In Proc. From Data and Information Analysis to Knowledge Engineering, Beijing, China, 2006, pp.614-621.

    Google Scholar 

  16. Pazzani M J, Billsus D. Content-Based Recommendation Systems. The Adaptive Web, Springer-Verlag, Berlin, 2007, pp.325-341.

    Chapter  Google Scholar 

  17. Albert R, Barabási A L. Statistical mechanics of complex networks. Rev. Mod. Phys., 2002, 74(1): 47–97.

    Article  MATH  Google Scholar 

  18. Dorogovtsev S N, Mendes J F F. Evolution of networks. Adv. Phys., 2002, 51: 1079.

    Article  Google Scholar 

  19. Newman M E J. The structure and function of complex networks. SIAM Rev., 2003, 45(2): 167–256.

    Article  MathSciNet  MATH  Google Scholar 

  20. Boccaletti S, Latora V, Moreno Y, Chavez M, Huang D U. Complex networks: Structure and dynamics. Phys. Rep., 2006, 424(4/5): 175–308.

    Article  MathSciNet  Google Scholar 

  21. Costa L da F, Rodrigues F A, Traviesor G, Boas P R U. Characterization of complex networks: A survey of measurements. Adv. Phys., 2007, 56(1): 167–242.

    Article  Google Scholar 

  22. Mathes A. Folksonomies-cooperative classification and communication through shared metadata. Computer Mediated Communication, 2004, LIS590CMC(10):

  23. Cattuto C, Loreto V, Pietronero L. Semiotic dynamics and collaborative tagging. Proc. Natl. Acad. Sci. U.S.A., 2007, 104(5): 1461–1464.

    Article  Google Scholar 

  24. Cattuto C, Barrat A, Baldassarri A, Schehr G, Loreto V. Collective dynamics of social annotation. Proc. Natl. Acad. Sci. U.S.A., 2009, 106(26): 10511–10515.

    Google Scholar 

  25. Zhang Z K, Lü L, Liu J G, Zhou T. Empirical analysis on a keyword-based semantic system. Eur. Phys. J. B, 2008, 66(4): 557–561.

    Article  MATH  Google Scholar 

  26. Lü L, Zhang Z K, Zhou T. Zipf's law leads to heaps' law: Analyzing their relation in finite-size systems. PLoS ONE, 2010, 5(12): e14139.

    Article  Google Scholar 

  27. Sen S, Lam S K, Rashid A M, Cosley D, Frankowski D, Osterhouse J, Harper F M, Riedl J. Tagging, communities, vocabulary, evolution. In Proc. 20th Anniversary Conf. Computer Supported Cooperative Work, Bantt, Canada, Nov. 4–8, 2006, pp.181-190.

  28. Nov O. What motivates wikipedians?. Commun. ACM, 2007, 50(11): 60–64.

    Article  Google Scholar 

  29. Ding Y, Jacob E K, Zhang Z, Foo S, Yan E, George N L, Guo L. Perspectives on social tagging. J. Am. Soc. Inf. Sci. Technol., 2009, 60(12): 2388–2401.

    Article  Google Scholar 

  30. Zhang Z K, Liu C, Zhang Y C, Zhou T. Solving the cold-start problem in recommender systems with social tags. EPL, 2010, 92(2): 28002.

    Article  MathSciNet  Google Scholar 

  31. Hotho A, Jäschke R, Schmitz C, Stumme G. Information retrieval in folksonomies: Search and ranking. In Proc. ESWC, Budva, Montenegro, Jun. 11–14, 2006, pp.411-426.

  32. Kleinberg J M. Authoritative sources in a hyperlinked environment. Journal of the ACM, 1999, 46(5): 604–632.

    Article  MathSciNet  MATH  Google Scholar 

  33. Jäschke R, Marinho L, Hotho A, Schmidt-Thieme L, Stumme G. Tag recommendations in folksonomies. In Proc. PKDD2007, Warsaw, Poland, Sept. 17–21, 2007, pp.506-514.

  34. Xu S, Bao S, Fei B et al. Exploring folksonomy for personalized search. In Proc. the 31st Annual Int. ACM SIGIR Conf. Res. Dev. Info. Retr., Singapore, Jul. 20–24, 2008, pp.155-162.

  35. Bogers T. Recommender systems for social bookmarking [Ph.D. Dissertation]. Tilburg University, Dec. 2009.

  36. Bischoff K, Firan C S, Nejdl W, Paiu R. Can all tags be used for search?. In Proc. 17th ACM Conf. Info. Knowl. Manag., Napa Valley, USA, Oct. 26–30, 2008, pp.193-202.

  37. Wu H, Zubair M, Maly K. Harvesting social knowledge from folksonomies. In Proc. 7th Conf. Hypertext Hypermedia, Odense, Denmark, Aug. 22–25, 2006, pp.111-114.

  38. Spiteri L. Structure and form of folksonomy tags: The road to the public library catalogue. Webology, June 2007, 4(2): 459–468.

    Google Scholar 

  39. Lee S S, Yong H S. Component based approach to handle synonym and polysemy in folksonomy. In Proc. Int. Conf. Computer and Information Technology, Aizu-Wakamatsu, Japan, Oct. 16–19, 2007, pp.200-205.

  40. Shepitsen A, Gemmell J, Mobasher B, Burke R. Personalized recommendation in social tagging systems using hierarchical clustering. In Proc. the 2008 ACM Conf. Recomm. Syst., Lausanne, Switzerland, Oct. 23–25, 2008, pp.259-266.

  41. Capocci A, Caldarelli G. Folksonomies and clustering in the collaborative system CiteULike. J. Phys. A, 2008, 41(22): 224016.

    Article  MathSciNet  Google Scholar 

  42. Mika P. Ontologies are us: A unified model of social networks and semantics. Web Semantics: Science, Services and Agents on the World Wide Web, 2007, 5(1): 5–15.

    Article  MathSciNet  Google Scholar 

  43. Kim H L, Scerri S, Breslin J G, Decker S, Kim H G. The state of the art in tag ontologies: A semantic model for tagging and folksonomies. In Proc. 2008 Int. Conf. Dublin Core Metadata Appl., Berlin, Germany, Sept. 22–26, 2008, pp.128-137.

  44. Symeonidis P, Nanopoulos A, Manolopoulos Y. Tag recommendations based on tensor dimensionality reduction. In Proc. 2008 ACM Conf. Recomme. Syst., Lausanne, Switzerland, Oct. 23–25, 2008, pp.43-50.

  45. Wetzker R, Umbrath W, Said A. A hybrid approach to item recommendation in folksonomies. In Proc. the Workshop Exploiting Semantic Annotation Information Retrieval (WSDM2009), Barcelona, Spain, Feb. 9–11, 2009, pp.25-29.

  46. Zhang Z K, Zhou T, Zhang Y C. Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs. Physica A, 2010, 389(1): 179–186.

    Article  Google Scholar 

  47. Liu Z, Shi C, Sun M. FolkDiffusion: A graph-based tag suggestion method for folksonomies. In Proc. the 6th Asia Info. Retrieval Societies Conf., Taipei, China, Dec. 1–3, 2010, pp.231-240.

  48. Lipczak M. Tag recommendation for folksonomies oriented towards individual users. In Proc. ECML PKDD Discovery Challenge (RSDC2008), Antwerp, Belgium, Sept. 15, 2008, pp.84-95.

  49. Rendle S, Marinho L B, Nanopoulos A, Thieme L S. Learning optimal ranking with tensor factorization for tag recommendation. In Proc. 15th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Pairs, France, Jun. 28-Jul. 1, 2009, pp.727-736.

  50. Ghoshal G, Zlatić V, Caldarelli G et al. Random hypergraphs and their applications. Phys. Rev. E, 2009, 79(6): 066118.

    Article  MathSciNet  Google Scholar 

  51. Zlatić V, Ghoshal G, Caldarelli G. Hypergraph topological quantities for tagged social networks. Phys. Rev. E, 2009, 80(3): 036118.

    Article  Google Scholar 

  52. Zhang Z K, Liu C. A hypergraph model of social tagging networks. J. Stat. Mech., 2010, (10): P10005.

    Article  Google Scholar 

  53. Gunawardana A, Shani G. A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res., 2009, 10: 2935–2962.

    MathSciNet  Google Scholar 

  54. Zhou T, Kuscsik Z, Liu J G et al. Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Natl. Acad. Sci. U.S.A., 2010, 107(10): 4511–4515.

    Article  Google Scholar 

  55. Hanley J A, McNeil B J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 1982, 143(1): 29–36.

    Google Scholar 

  56. Clauset A, Moore C, Newman M E J. Finding community structure in very large networks. Nature, 2008, 453(7191): 98–101.

    Article  Google Scholar 

  57. Zhou T, Su R Q, Liu R R, Jiang L L, Wang B H, Zhang Y C. Effect of initial configuration on network-based recommendation. Europhysics Letters, 2008, 81(5): 58004.

    Article  Google Scholar 

  58. Shang M S, Lü L, Zhang Y C et al. Empirical analysis of web-based user-object bipartite networks. EPL, 2010, 90: 48006.

    Article  Google Scholar 

  59. Zhang Y C, Blattner M, Yu Y K. Heat conduction process on community networks as a recommendation mode. Phys. Rev. Lett., 2007, 99(15): 154301.

    Article  Google Scholar 

  60. Shang M S, Zhang Z K, Zhou T, Zhang Y C. Collaborative filtering with diffusion-based similarity on tripartite graphs. Physica A, 2010, 389(6): 1259–1264.

    Article  Google Scholar 

  61. Shang M S, Zhang Z K. Diffusion-based recommendation in collaborative tagging systems. Chin. Phys. Lett., 2009, 26(11): 118903.

    Article  MathSciNet  Google Scholar 

  62. Wu P, Zhang Z K. Enhancing personalized recommendation in weighted social tagging networks. Physical Procdia, 2010, 3(5): 1877–1885.

    Article  Google Scholar 

  63. Salton G, McGill M J. Introduction to Model Information Retrieval. MuGraw-Hill, New York, 1983.

    Google Scholar 

  64. Zhang Z K, Liu C. Identifying the role of social tags and its application in recommender systems. Int. J. Complex Systems in Science, 2011, 1(1): 10–15.

    Google Scholar 

  65. Liang H, Xu Y, Li Y et al. Connecting users and items with weighted tags for personalized item recommendations. In Proc. 21st ACM Conf. Hypertext and Hypermedia (HT2010), Toronto, Ontario, Canada, Jun. 13–16, 2010, pp.51-60.

  66. Szomszor M, Cattuto C, Alani H et al. Folksonomies, the semantic web, and movie recommendation. In Proc. the 4th Euro. Semantic Web Conf., Innsbruck, Australia, Jun. 3–7, 2007, pp.71-84.

  67. Tso-Sutter K H L, Marinho L B, Schmidt-Thieme L. Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proc. the 2008 ACM Symposium on Applied Computing, Fortaleza, Ceará, Brazil, Mar. 16–20, 2008, pp.1995-1999.

  68. Kolda T G, Bader B W. Tensor decompositions and applications. SIAM Rev., 2009, 51(3): 455–500.

    Article  MathSciNet  MATH  Google Scholar 

  69. Xu Y, Zhang L, Liu W. Cubic analysis of social bookmarking for personalized recommendation. Frontiers of WWW Research and Development-APWeb 2006, Beijing, China, Apr. 21–25, 2006, pp.733-738.

  70. Symeonidis P. User recommendations based on tensor dimensionality reduction. Artificial Intelligence Applications and Innovations III, 2009, pp.331-340.

  71. Kantor P B, Ricci F, Rokach L, Shapira B. Recommender Systems Handbook. Springer, 2010.

  72. Wall M, Rechtsteiner A, Rocha L. Singular value decomposition and principal component analysis. A Practical Approach to Microarray Data Analysis, 2003, pp.91-109.

  73. De Lathauwer L, De Moor B, Vandewalle J. A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl., 2000, 21(4): 1253–1278.

    Article  MathSciNet  MATH  Google Scholar 

  74. Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis. Cambridge Univ. Press, 2004.

  75. Chin T J, Schindler K, Suter D. Incremental kernel SVD for face recognition with image sets. In Proc. Int. Conf. Automatic Face Gesture Recognition (FGR), Southampton, UK, Apr. 10–12, 2006, pp.461-466.

  76. Rendle S, Schmidt-Thieme L. Pairwise interaction tensor factorization for personalized tag recommendation. In Proc. 3 rd ACM Int. Conf. Web Search Data Mining, New York, USA, Feb. 4–6, 2010, pp.81-90.

  77. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR: Bayesian personalized ranking from implicit feedback. In Proc. 25th Conf. Uncertainty Arti. Intel., Montreal, Canada, Jun. 18–21, 2009, pp.452-461.

  78. Deerwester S, Dumais S T, Furnas G W, Landauer T K, Harshman R. Indexing by latent semantic analysis. J. Am. Soc. Info. Sci., 1990, 41(6): 391–407.

    Article  Google Scholar 

  79. Hofmann T. Probabilistic latent semantic indexing. In Proc. 22nd Ann. Int. ACM SIGIR Conf. Research and Development Information Retrieval, Berkeley, California, USA, Aug. 15–19, 1999, pp.50-57.

  80. Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation. J. Mach. Learn. Res., 2003, 3: 993–1022.

    MATH  Google Scholar 

  81. Gelfand A E, Smith A F M. Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc., 1990, 85(410): 398–409.

    Article  MathSciNet  MATH  Google Scholar 

  82. Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. B, 1977, 39(1): 1–38.

    MathSciNet  MATH  Google Scholar 

  83. Umbrath A S, Wetzker R, Umbrath W, Hennig L. A hybrid PLSA approach for warmer cold start in folksonomy recommendation. In proc. ACM Recsys 2009 Workshop on Recommender Systems & the Social Web, New York, USA, Oct. 25, 2009, pp.87-90.

  84. Si X, Sun M. Tag-LDA for scalable real-time tag recommendation. J. Comput. Infor. Syst., 2009, 6(1): 23–31.

    Google Scholar 

  85. Krestel R, Fankhauser P, Nejdl W. Latent dirichlet allocation for tag recommendation. In Proc. 3 rd ACM Conf. Recomm. Syst., New York, USA, Oct. 23–25, 2009, pp.61-68.

  86. Krestel R, Fankhauser P. Tag recommendation using probabilistic topic models. In Proc. ECML PKDD Discovery Challenge 2009 (DC09), Bled, Slovenia, Sept. 7–11, 2009, p.131.

  87. Bundschus M, Yu S, Tresp V et al. Hierarchical bayesian models for collaborative tagging systems. In Proc. the 9th IEEE International Conference on Data Mining, Miami, USA, 2009, Dec. 6–9, 2009, pp.728-733.

  88. Bundschus M, Tresp V, Kriegel H P. Topic models for semantically annotated document collections. In Proc. NIPS Workshop: Applications for Topic Models: Text and Beyond, Whistler, Canada, Dec. 11, 2009, pp.1-4.

  89. Harvey M, Baillie M, Ruthven I, Carman M. Tripartite hidden topic models for personalised tag suggestion. In Proc. 32nd European Conf. IR Research, Milton Keynes, UK, Mar. 28–31, 2010, pp.432-443.

  90. Li D, He B, Ding Y et al. Community-based topic modeling for social tagging. In Proc. 9th ACM Int. Conf. Infor. Knowl. Manag. (CIKM2010), Toronto, Canada, Oct. 26–30, 2010, pp.54-63.

  91. Girvan M, Newman M E J. Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A., 2002, 99(12): 7821–7826.

    Article  MathSciNet  MATH  Google Scholar 

  92. Leskovec J, Lang K J, Mahoney M. Empirical comparison of algorithms for network community detection. In Proc. 19th Int. Conf. WWW, Raleigh, USA, Apr. 26–30, 2010, pp.631-640.

  93. Wang J W, Rong L L, Deng Q H, Zhang J Y. Evolving hypernetwork mode. Eur. Phys. J. B, 2010, 77(4): 493–498.

    Article  Google Scholar 

  94. Lambiotte R, Ausllos M. Collaborative tagging as a tripartite network. Lect. Notes Comput. Sci., 2006, 3993: 1114–1117.

    Article  Google Scholar 

  95. Cattuto C, Schmitz C, Baldassarri A, Servedio V D P, Loreto V, Hotho A, Grahl M, Stumme G. Network properties of folksonomies. AI Commun., 2007, 4: 245–262.

    MathSciNet  Google Scholar 

  96. Hotho A, Jäschke R, Schmitz C, Stumme G. Trend detection in folksonomies. Semantic Multimedia, 2006, 4306: 56–70.

    Article  Google Scholar 

  97. Peng J, Zeng D D, Zhao H, Wang F. Collaborative filtering in social tagging systems based on joint item-tag recommendations. In Proc. 9th ACM Int. Conf. Infor. Knowl. Manag., Toronto, Canada, Oct. 26–30,2010, pp.809-818.

  98. Gemmell J, Shepitsen A, Mobasher B, Burke R. Personalized recommendation in social tagging systems using hierarchical clustering. Intelligent Techniques for Web Personalization & Recommender Systems, Lausanne, Switzerland, Oct. 23–25, 2008, pp.259-266.

  99. Ramage D, Heymann P, Manning C D, Garcia-Molina H. Clustering the tagged web. In Proc. 2nd ACM Int. Conf. Web Search and Data Mining, Barcelona, Spain, Feb. 9–12, 2009, pp.54-63.

  100. Koutrika G, Effendi F A, Gyöngyi Z, Heymann P, Hector G M. Combating spam in tagging systems: An evaluation. ACM Trans. Web, 2008, 2(4): 22–57.

    Article  Google Scholar 

  101. Pittel B. On spreading a rumor. SIAM J. Appl. Math., 1987, 47(1): 213–223.

    Article  MathSciNet  MATH  Google Scholar 

  102. Karp R, Schindelhauer C, Shenker S, Vocking B. Randomized rumor spreading. In Proc. 41st IEEE FOCS, Redondo Beach, USA, Nov. 12–14, 2000, pp.565-574.

  103. Kontostathis A, Galitsky L, Pottenger W M, Roy S, Phelps D J. A survey of emerging trend detection in textual data mining. Survey of Text Mining: Clustering, Classification, and Retrieval, 2003, Springer.

  104. Medhi D, Tipper D. Multi-layered network survivability-models, analysis, architecture, framework and implementation: An overview. In Proc. 2000 DARPA Information Survivability Conference & Exposition, Hilton Head, USA, Jan. 25–27, 2000, pp.173-186.

  105. Scott J. Social network analysis. ACM Trans. Web, 1988, 2(1): 109–127.

    Google Scholar 

  106. Medo M, Zhang Y C, Zhou T. Adaptive model for recommendation of news. EPL, 2009, 88: 38005.

    Article  Google Scholar 

  107. Huang J, Cheng X Q, Guo J, Shen H W, Yang K. Social recommendation with interpersonal influence. In Proc. 2010 Conf. ECAI 2010: 19th Euro. Conf. on Artif. Int., Lisbon, Portugal, Aug. 16–20, 2010, pp.601-606.

  108. Cimini G, Medo M, Zhou T, Wei D, Zhang Y C. Heterogeneity, quality, and reputation in an adaptive recommendation model. Eur. Phys. J. B, 2011, 80(2): 201–208.

    Article  Google Scholar 

  109. Heymann P, Ramage D, Garcia-Molina H. Social tag prediction. In Proc. 31st Annual Int. ACM SIGIR Conf. Research Development Information Retrieval, Singapore, Jul. 20–24, 2008, pp.531-538.

  110. Lü L, Zhou T. Link prediction in complex networks: A survey. Physica A, 2011, 390(6): 1150–1170.

    Google Scholar 

  111. Dubinko M, Kumar R, Magnani J, Novak J, Raghavan P, Tomkins A. Visualizing tags over time. ACM Trans. Web, 2007, 1(2): 7.

    Article  Google Scholar 

  112. Liu C, Yueng Y H, Zhang Z K. Self-organization in social tagging systems. Phys. Rev. E, 2011, 83: 066104.

    Article  Google Scholar 

  113. Barabási A L. The origin of bursts and heavy tails in human dynamics. Nature, 2005, 435(7039): 207–211.

    Google Scholar 

  114. Gantner Z, Rendle S, Schmidt-Thieme L. Factorization models for context-/time-aware movie recommendations. In Proc. Workshop on Context-Aware Movie Recommendation, Barcelona, Spain, Sept. 30, 2010, pp.14-19.

  115. Xiang L, Yuan Q, Zhao S, Chen L, Zhang X, Yang Q, Sun J. Temporal recommendation on graphs via long-and short-term preference fusion. In Proc. the 16th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., Washington, USA, 2010, pp.723-732.

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  1. Institute of Information Economy, Hangzhou Normal University, Hangzhou, 310036, China

    Zi-Ke Zhang & Yi-Cheng Zhang

  2. Web Sciences Center, University of Electronic Science and Technology, Chengdu, 610054, China

    Zi-Ke Zhang, Tao Zhou & Yi-Cheng Zhang

  3. Department of Physics, University of Fribourg, Chemin du Musée 1700, Fribourg, Switzerland

    Zi-Ke Zhang & Yi-Cheng Zhang

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  1. Zi-Ke Zhang
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Correspondence to Zi-Ke Zhang or Yi-Cheng Zhang.

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This work is partially supported by the Future and Emerging Technologies (FET) Programs of the European Commission FP7-COSI-ICT (QLectives with Grant No. 231200 and LiquidPub with Grant No. 213360). Z.-K.Zhang and T.Zhou acknowledge the National Natural Science Foundation of China under Grant Nos. 11105024, 60973069, 61103109, and 90924011, and the Science and Technology Department of Sichuan Province under Grant No. 2010HH0002.

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Zhang, ZK., Zhou, T. & Zhang, YC. Tag-Aware Recommender Systems: A State-of-the-Art Survey. J. Comput. Sci. Technol. 26, 767–777 (2011). https://doi.org/10.1007/s11390-011-0176-1

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