Recommending Based on Implicit Feedback

Recommender systems have shown to be valuable tools for filtering, ranking, and discovery in a variety of application domains such as e-commerce, media repositories or document-based information in general that includes the various scenarios of Social Information Access discussed in this book. One key to the success of such systems lies in the precise acquisition or estimation of the user’s preferences. While general recommender systems research often relies on the existence of explicit preference statements for personalization, such information is often very sparse or unavailable in real-world applications. Information that allows us to assess the relevance of certain items indirectly through a user’s actions and behavior (implicit feedback) is in contrast often available in abundance. In this chapter we categorize different types of implicit feedback and review their use in the context of recommender systems and Social Information Access applications. We then extend the categorization scheme to be suitable to recent application domains. Finally, we present state-of-the-art algorithmic approaches, discuss challenges when using implicit feedback signals in particular with respect to popularity biases, and discuss selected recent works from the literature.

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Notes

As indicated in Sect. 2, we consider such information only as implicit feedback if the signal is related to some target recommendation object.

The BPR-OPT criterion used in the previously described BPR method has a close correspondence to the AUC measure.

This was for example done for the evaluation of the implicit-only algorithm BPR, see http://www.mymedialite.net/examples/item_recommendation_datasets.html.

Many more of the top-ranked elements might be relevant for the user, but no explicit information is given.

The data is not publicly available. The data was sampled in a way that no conclusions about visitor or sales numbers can be drawn. The data sample was taken within a very limited period of time. The importance of feature-based similarities was also the basis in [119].

References

  1. Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Semantic enrichment of Twitter posts for user profile construction on the social web. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 375–389. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21064-8_26ChapterGoogle Scholar
  2. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, ACM SIGMOD 1993, pp. 207–216 (1993) Google Scholar
  3. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, ICDE 1995, pp. 3–14 (1995) Google Scholar
  4. Ali, K., van Stam, W.: TiVo: making show recommendations using a distributed collaborative filtering architecture. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 394–401 (2004) Google Scholar
  5. Armentano, M.G., Godoy, D., Amandi, A.: Topology-based recommendation of users in micro-blogging communities. J. Comput. Sci. Technol. 27(3), 624–634 (2012) ArticleGoogle Scholar
  6. Armstrong, R., Freitag, D., Joachims, T., Mitchell, T.: WebWatcher: a learning apprentice for the world wide web. In: AAAI Technical Report SS-95-08, pp. 6–12 (1995) Google Scholar
  7. Ben-David, S., Lindenbaum, M.: Learning distributions by their density levels: a paradigm for learning without a teacher. J. Comput. Syst. Sci. 55, 171–182 (1997) ArticleMathSciNetMATHGoogle Scholar
  8. Bogers, T.: Tag-based recommendation. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 441–479. Springer, Cham (2018) Google Scholar
  9. Bohnert, F., Zukerman, I.: Personalised viewing-time prediction in museums. User Model. User-Adapt. Interact. 24(4), 263–314 (2014) ArticleGoogle Scholar
  10. Bonnin, G., Jannach, D.: Evaluating the quality of playlists based on hand-crafted samples. In: Proceedings of the 2013 International Society for Music Information Retrieval, ISMIR 2013, pp. 263–268 (2013) Google Scholar
  11. Bonnin, G., Jannach, D.: Automated generation of music playlists: survey and experiments. ACM Comput. Surv. 47(2), 26:1–26:35 (2014) ArticleGoogle Scholar
  12. Bothorel, C., Lathia, N., Picot-Clemente, R., Noulas, A.: Location recommendation with social media data. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 624–653. Springer, Cham (2018) Google Scholar
  13. Budzik, J., Hammond, K.: Watson: anticipating and contextualizing information needs. In: 62nd Annual Meeting of the American Society for Information Science, pp. 727–740 (1999) Google Scholar
  14. Carmel, D., Zwerdling, N., Guy, I., Ofek-Koifman, S., Har’El, N., Ronen, I., Uziel, E., Yogev, S., Chernov, S.: Personalized social search based on the user’s social network. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 1227–1236 (2009) Google Scholar
  15. Castagnos, S., Jones, N., Pu, P.: Eye-tracking product recommenders’ usage. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, pp. 29–36 (2010) Google Scholar
  16. Chau, P.Y.K., Ho, S.Y., Ho, K.K.W., Yao, Y.: Examining the effects of malfunctioning personalized services on online users’ distrust and behaviors. Decis. Support Syst. 56, 180–191 (2013) ArticleGoogle Scholar
  17. Chen, W.Y., Chu, J.C., Luan, J., Bai, H., Wang, Y., Chang, E.Y.: Collaborative filtering for Orkut communities: discovery of user latent behavior. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 681–690 (2009) Google Scholar
  18. Cheng, Z., Shen, J.: Just-for-me: an adaptive personalization system for location-aware social music recommendation. In: Proceedings of International Conference on Multimedia Retrieval, ICMR 2014, pp. 185:185–185:192 (2014) Google Scholar
  19. Claypool, M., Le, P., Wased, M., Brown, D.: Implicit interest indicators. In: Proceedings of the 6th ACM International Conference on Intelligent User Interfaces, IUI 2001, pp. 33–40 (2001) Google Scholar
  20. Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-N recommendation tasks. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, pp. 39–46 (2010) Google Scholar
  21. Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B., Sampath, D.: The YouTube video recommendation system. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, pp. 293–296 (2010) Google Scholar
  22. Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 233–240 (2006) Google Scholar
  23. De Pessemier, T., Dooms, S., Martens, L.: Context-aware recommendations through context and activity recognition in a mobile environment. Multimed. Tools Appl. 72(3), 2925–2948 (2014) ArticleGoogle Scholar
  24. Denis, F.: PAC learning from positive statistical queries. In: Richter, M.M., Smith, C.H., Wiehagen, R., Zeugmann, T. (eds.) ALT 1998. LNCS (LNAI), vol. 1501, pp. 112–126. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-49730-7_9ChapterGoogle Scholar
  25. Du, L., Li, X., Shen, Y.-D.: User graph regularized pairwise matrix factorization for item recommendation. In: Tang, J., King, I., Chen, L., Wang, J. (eds.) ADMA 2011, Part II. LNCS (LNAI), vol. 7121, pp. 372–385. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25856-5_28ChapterGoogle Scholar
  26. Gadanho, S.C., Lhuillier, N.: Addressing uncertainty in implicit preferences. In: Proceedings of the 2007 ACM Conference on Recommender Systems, RecSys 2007, pp. 97–104 (2007) Google Scholar
  27. Garcia, E., Romero, C., Ventura, S., Castro, C.: An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Model. User-Adapt. Interact. 19(1–2), 99–132 (2009) ArticleGoogle Scholar
  28. Gedikli, F., Jannach, D.: Improving recommendation accuracy based on item-specific tag preferences. ACM Trans. Intell. Syst. Technol. 4(1), 11:1–11:19 (2013) ArticleGoogle Scholar
  29. Gil, M., Pelechano, V.: Exploiting user feedback for adapting mobile interaction obtrusiveness. In: Bravo, J., López-de-Ipiña, D., Moya, F. (eds.) UCAmI 2012. LNCS, vol. 7656, pp. 274–281. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35377-2_38ChapterGoogle Scholar
  30. Glazyrin, N.: Music recommendation system for million song dataset challenge. CoRR abs/1209.3286 (2012) Google Scholar
  31. Guy, I.: People recommendation on social media. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 570–623. Springer, Cham (2018) Google Scholar
  32. Guy, I., Zwerdling, N., Carmel, D., Ronen, I., Uziel, E., Yogev, S., Ofek-Koifman, S.: Personalized recommendation of social software items based on social relations. In: Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009, pp. 53–60 (2009) Google Scholar
  33. Guy, I., Zwerdling, N., Ronen, I., Carmel, D., Uziel, E.: Social media recommendation based on people and tags. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 194–201 (2010) Google Scholar
  34. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD 2000, pp. 1–12 (2000) Google Scholar
  35. Han, S., He, D.: Network-based social search. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 277–309. Springer, Cham (2018) Google Scholar
  36. Hariri, N., Mobasher, B., Burke, R.: Context-aware music recommendation based on latent topic sequential patterns. In: Proceedings of the 2012 ACM Conference on Recommender Systems, RecSys 2012, pp. 131–138 (2012) Google Scholar
  37. Hensley, C.B.: Selective dissemination of information (SDI). In: Proceedings of the 1963 Spring Joint Computer Conference, AFIPS 1963, pp. 257–262 (1963) Google Scholar
  38. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004) ArticleGoogle Scholar
  39. Hill, W.C., Hollan, J.D., Wroblewski, D., McCandless, T.: Edit wear and read wear. In: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 1992, pp. 3–9 (1992) Google Scholar
  40. Höök, K., Benyon, D., Munro, A.J.: Designing Information Spaces: The Social Navigation Approach. Springer Science & Business Media, London (2012). https://doi.org/10.1007/978-1-4471-0035-5BookMATHGoogle Scholar
  41. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272 (2008) Google Scholar
  42. Jannach, D., Hegelich, K.: A case study on the effectiveness of recommendations in the mobile internet. In: Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009, pp. 205–208 (2009) Google Scholar
  43. Jannach, D., Karakaya, Z., Gedikli, F.: Accuracy improvements for multi-criteria recommender systems. In: Proceedings of the 13th ACM Conference on Electronic Commerce, EC 2012, pp. 674–689 (2012) Google Scholar
  44. Jannach, D., Lerche, L., Jugovac, M.: Adaptation and evaluation of recommendations for short-term shopping goals. In: Proceedings of the 2015 ACM Conference on Recommender Systems, RecSys 2015, pp. 211–218 (2015) Google Scholar
  45. Jannach, D., Lerche, L., Kamehkhosh, I.: Beyond “hitting the hits”: generating coherent music playlist continuations with the right tracks. In: Proceedings of the 2015 ACM Conference on Recommender Systems, RecSys 2015, pp. 187–194 (2015) Google Scholar
  46. Jannach, D., Lerche, L., Kamehkhosh, I., Jugovac, M.: What recommenders recommend: an analysis of recommendation biases and possible countermeasures. User Model. User-Adapt. Interact. 25(5), 427–491 (2015) ArticleGoogle Scholar
  47. Jannach, D., Zanker, M., Ge, M., Gröning, M.: Recommender systems in computer science and information systems – a landscape of research. In: Huemer, C., Lops, P. (eds.) EC-Web 2012. LNBIP, vol. 123, pp. 76–87. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32273-0_7ChapterGoogle Scholar
  48. Jawaheer, G., Szomszor, M., Kostkova, P.: Comparison of implicit and explicit feedback from an online music recommendation service. In: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec 2010, pp. 47–51 (2010) Google Scholar
  49. Jawaheer, G., Weller, P., Kostkova, P.: Modeling user preferences in recommender systems: A classification framework for explicit and implicit user feedback. ACM Trans. Interact. Intell. Syst. 4(2), 8:1–8:26 (2014) ArticleGoogle Scholar
  50. Kanagal, B., Ahmed, A., Pandey, S., Josifovski, V., Yuan, J., Garcia-Pueyo, L.: Supercharging recommender systems using taxonomies for learning user purchase behavior. Proc. VLDB Endow. 5(10), 956–967 (2012) ArticleGoogle Scholar
  51. Ke, T., Yang, B., Zhen, L., Tan, J., Li, Y., Jing, L.: Building high-performance classifiers using positive and unlabeled examples for text classification. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds.) ISNN 2012, Part II. LNCS, vol. 7368, pp. 187–195. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31362-2_21ChapterGoogle Scholar
  52. Kelly, D., Teevan, J.: Implicit feedback for inferring user preference: a bibliography. SIGIR Forum 37(2), 18–28 (2003) ArticleGoogle Scholar
  53. Khalili, A., Wu, C., Aghajan, H.: Autonomous learning of user’s preference of music and light services in smart home applications. In: Behavior Monitoring and Interpretation Workshop at German AI Conference (2009) Google Scholar
  54. Kliegr, T., Kuchar, J.: Orwellian eye: video recommendation with Microsoft Kinect. In: Proceedings 21st European Conference on Artificial Intelligence, ECAI/PAIS 2014, pp. 1227–1228 (2014) Google Scholar
  55. Kluver, D., Ekstrand, M., Konstan, J.: Rating-based collaborative filtering: algorithms and evaluation. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 344–390. Springer, Cham (2018) Google Scholar
  56. Kobsa, A., Koenemann, J., Pohl, W.: Personalised hypermedia presentation techniques for improving online customer relationships. Knowl. Eng. Rev. 16(2), 111–155 (2001) ArticleMATHGoogle Scholar
  57. Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: applying collaborative filtering to usenet news. Commun. ACM 40(3), 77–87 (1997) ArticleGoogle Scholar
  58. Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adapt. Interact. 22(1–2), 101–123 (2012) ArticleGoogle Scholar
  59. Kordumova, S., Kostadinovska, I., Barbieri, M., Pronk, V., Korst, J.: Personalized implicit learning in a music recommender system. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 351–362. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13470-8_32ChapterGoogle Scholar
  60. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 426–434 (2008) Google Scholar
  61. Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data (TKDD) 4(1), 1:1–1:24 (2010) MathSciNetGoogle Scholar
  62. Krohn-Grimberghe, A., Drumond, L., Freudenthaler, C., Schmidt-Thieme, L.: Multi-relational matrix factorization using Bayesian personalized ranking for social network data. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM 2012, pp. 173–182 (2012) Google Scholar
  63. Lee, D., Brusilovsky, P.: Recommendations based on social links. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 391–440. Springer, Cham (2018) Google Scholar
  64. Lee, D., Park, S., Kahng, M., Lee, S., Lee, S.: Exploiting contextual information from event logs for personalized recommendation. In: Lee, R. (ed.) Computer and Information Science 2010. SCI, vol. 317, pp. 121–139. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15405-8_11ChapterGoogle Scholar
  65. Lee, T.Q., Park, Y., Park, Y.T.: A time-based approach to effective recommender systems using implicit feedback. Expert Syst. Appl. 34(4), 3055–3062 (2008) ArticleGoogle Scholar
  66. Lerche, L., Jannach, D.: Using graded implicit feedback for Bayesian personalized ranking. In: Proceedings of the 2014 ACM Conference on Recommender Systems, RecSys 2014, pp. 353–356 (2014) Google Scholar
  67. Lerchenmüller, B., Wörndl, W.: Inference of user context from GPS logs for proactive recommender systems. In: AAAI Workshop Activity Context Representation: Techniques and Languages. AAAI Technical Report WS-12-05 (2012) Google Scholar
  68. Li, H., Wang, Y., Zhang, D., Zhang, M., Chang, E.Y.: PFP: parallel FP-growth for query recommendation. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, pp. 107–114 (2008) Google Scholar
  69. Li, X.-L., Liu, B.: Learning from positive and unlabeled examples with different data distributions. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 218–229. Springer, Heidelberg (2005). https://doi.org/10.1007/11564096_24ChapterGoogle Scholar
  70. Lieberman, H.: Letizia: an agent that assists web browsing. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI 1995, vol. 1, pp. 924–929 (1995) Google Scholar
  71. Lin, J., Sugiyama, K., Kan, M.Y., Chua, T.S.: Addressing cold-start in app recommendation: latent user models constructed from Twitter followers. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pp. 283–292 (2013) Google Scholar
  72. Lin, K.H., Chung, K.H., Lin, K.S., Chen, J.S.: Face recognition-aided IPTV group recommender with consideration of serendipity. Int. J. Future Comput. Commun. 3(2), 141–147 (2014) ArticleGoogle Scholar
  73. Lin, W., Alvarez, S.A., Ruiz, C.: Efficient adaptive-support association rule mining for recommender systems. Data Min. Knowl. Disc. 6(1), 83–105 (2002) ArticleMathSciNetGoogle Scholar
  74. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003) ArticleGoogle Scholar
  75. Liu, N.N., Xiang, E.W., Zhao, M., Yang, Q.: Unifying explicit and implicit feedback for collaborative filtering. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 1445–1448 (2010) Google Scholar
  76. Liu, Y., Zhao, P., Sun, A., Miao, C.: AdaBPR: a boosting algorithm for item recommendation with implicit feedback. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence, IJCAI 2015 (2015) Google Scholar
  77. Marlin, B.M., Zemel, R.S., Roweis, S., Slaney, M.: Collaborative filtering and the missing at random assumption. In: Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007, pp. 267–275 (2007) Google Scholar
  78. McFee, B., Bertin-Mahieux, T., Ellis, D.P., Lanckriet, G.R.: The million song dataset challenge. In: Proceedings of the 21st International Conference Companion on World Wide Web, WWW 2012 Companion, pp. 909–916 (2012) Google Scholar
  79. Mladenic, D.: Personal WebWatcher: design and implementation. Technical report IJS-DP-7472. J. Stefan Institute, Slovenia (1996) Google Scholar
  80. Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Commun. ACM 43(8), 142–151 (2000) ArticleGoogle Scholar
  81. Moore, J.L., Chen, S., Joachims, T., Turnbull, D.: Learning to embed songs and tags for playlist prediction. In: Proceedings of the 2012 International Society for Music Information Retrieval Conference, ISMIR 2012, pp. 349–354 (2012) Google Scholar
  82. Morita, M., Shinoda, Y.: Information filtering based on user behavior analysis and best match text retrieval. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1994, pp. 272–281 (1994) ChapterGoogle Scholar
  83. Nichols, D.M.: Implicit rating and filtering. In: Proceedings of 5th DELOS Workshop on Filtering and Collaborative Filtering (1997) Google Scholar
  84. Oard, D.W.: Modeling information content using observable behavior. In: Proceedings of the 64th Annual Conference of the American Society for Information Science and Technology (2001) Google Scholar
  85. Oard, D.W., Kim, J.: Implicit feedback for recommender systems. In: Proceedings of the 1998 AAAI Workshop on Recommender Systems (1998) Google Scholar
  86. O’Mahoney, M., Smyth, B.: From opinions to recommendations. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 480–509. Springer, Cham (2018) Google Scholar
  87. Palanivel, K., Sivakumar, R.: A study on implicit feedback in multicriteria e-commerce recommender system. J. Electron. Commer. Res. 11(2), 140–156 (2010) Google Scholar
  88. Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 502–511 (2008) Google Scholar
  89. Pan, W., Chen, L.: CoFiSet: collaborative filtering via learning pairwise preferences over item-sets. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 180–188 (2013) ChapterGoogle Scholar
  90. Pan, W., Chen, L.: GBPR: group preference based Bayesian personalized ranking for one-class collaborative filtering. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013, pp. 2691–2697 (2013) Google Scholar
  91. Pan, W., Zhong, H., Xu, C., Ming, Z.: Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks. Knowl.-Based Syst. 73, 173–180 (2015) ArticleGoogle Scholar
  92. Paquet, U., Koenigstein, N.: One-class collaborative filtering with random graphs. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, pp. 999–1008 (2013) Google Scholar
  93. Parra, D., Amatriain, X.: Walk the talk: analyzing the relation between implicit and explicit feedback for preference elicitation. In: Proceedings of the 19th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2011, pp. 255–268 (2011) ChapterGoogle Scholar
  94. Parra, D., Karatzoglou, A., Yavuz, I., Amatriain, X.: Implicit feedback recommendation via implicit-to-explicit ordinal logistic regression mapping. In: Proceedings of the CARS 2011 (2011) Google Scholar
  95. Partridge, K., Price, B.: Enhancing mobile recommender systems with activity inference. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 307–318. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02247-0_29ChapterGoogle Scholar
  96. Pennacchiotti, M., Gurumurthy, S.: Investigating topic models for social media user recommendation. In: Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011, pp. 101–102 (2011) Google Scholar
  97. Pilászy, I., Zibriczky, D., Tikk, D.: Fast ALS-based matrix factorization for explicit and implicit feedback datasets. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, pp. 71–78 (2010) Google Scholar
  98. Pitman, A., Zanker, M.: An empirical study of extracting multidimensional sequential rules for personalization and recommendation in online commerce. In: 10. Internationale Tagung Wirtschaftinformatik, pp. 180–189 (2011) Google Scholar
  99. Pizzato, L., Chung, T., Rej, T., Koprinska, I., Yecef, K., Kay, J.: Learning user preferences in online dating. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD, Preference Learning Workshop (2010) Google Scholar
  100. Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3(3), 57:1–57:22 (2012) ArticleGoogle Scholar
  101. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2009, pp. 452–461 (2009) Google Scholar
  102. Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 811–820. ACM (2010) Google Scholar
  103. Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM 2010, pp. 81–90 (2010) Google Scholar
  104. Rodden, K., Fu, X., Aula, A., Spiro, I.: Eye-mouse coordination patterns on web search results pages. In: Extended Abstracts on Human Factors in Computing Systems, CHI EA 2008, pp. 2997–3002 (2008) Google Scholar
  105. Roth, M., Ben-David, A., Deutscher, D., Flysher, G., Horn, I., Leichtberg, A., Leiser, N., Matias, Y., Merom, R.: Suggesting friends using the implicit social graph. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 233–242 (2010) Google Scholar
  106. Said, A., Bellogín, A.: Comparative recommender system evaluation: benchmarking recommendation frameworks. In: Proceedings of the 2014 ACM Conference on Recommender Systems, RecSys 2014, pp. 129–136 (2014) Google Scholar
  107. Sakagami, H., Kamba, T.: Learning personal preferences on online newspaper articles from user behaviors. Comput. Netw. ISDN Syst. 29(8–13), 1447–1455 (1997) ArticleGoogle Scholar
  108. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, EC 2000, pp. 158–167 (2000) Google Scholar
  109. Schölkopf, B., Platt, J.C., Shawe-Taylor, J.C., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001) ArticleMATHGoogle Scholar
  110. Sen, S., Vig, J., Riedl, J.: Tagommenders: connecting users to items through tags. In: Proceedings of the 18th International World Wide Web Conference, WWW 2009, pp. 671–680 (2009) Google Scholar
  111. Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system. J. Mach. Learn. Res. 6, 1265–1295 (2005) MathSciNetMATHGoogle Scholar
  112. Shapira, B., Taieb-Maimon, M., Moskowitz, A.: Study of the usefulness of known and new implicit indicators and their optimal combination for accurate inference of users interests. In: Proceedings of the 2006 ACM Symposium on Applied Computing, SAC 2006, pp. 1118–1119 (2006) Google Scholar
  113. Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A.: xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance. In: Proceedings of the 2013 ACM Conference on Recommender Systems, RecSys 2013, pp. 431–434 (2013) Google Scholar
  114. Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Oliver, N., Hanjalic, A.: CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In: Proceedings of the 2012 ACM Conference on Recommender Systems, RecSys 2012, pp. 139–146 (2012) Google Scholar
  115. Sohn, T., Li, K.A., Griswold, W.G., Hollan, J.D.: A diary study of mobile information needs. In: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2008, pp. 433–442 (2008) Google Scholar
  116. Srebro, N., Jaakkola, T., et al.: Weighted low-rank approximations. In: Proceedings of the Twentieth International Conference on Machine Learning, ICML 2003, pp. 720–727 (2003) Google Scholar
  117. Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short text classification in Twitter to improve information filtering. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 841–842 (2010) Google Scholar
  118. Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) Pervasive 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24646-6_10ChapterGoogle Scholar
  119. Tavakol, M., Brefeld, U.: Factored MDPs for detecting topics of user sessions. In: Proceedings of the 2014 ACM Conference on Recommender Systems, RecSys 2014, pp. 33–40 (2014) Google Scholar
  120. Véras, D., Prota, T., Bispo, A., Prudêncio, R., Ferraz, C.: A literature review of recommender systems in the television domain. Expert Syst. Appl. 42(22), 9046–9076 (2015) ArticleGoogle Scholar
  121. Wang, J., Zhu, J.: On statistical analysis and optimization of information retrieval effectiveness metrics. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 226–233 (2010) Google Scholar
  122. Wang, S., Zhou, X., Wang, Z., Zhang, M.: Please spread: recommending tweets for retweeting with implicit feedback. In: Proceedings of the 2012 Workshop on Data-Driven User Behavioral Modelling and Mining from Social Media, DUBMMSM 2012, pp. 19–22 (2012) Google Scholar
  123. Ward, G., Hastie, T., Barry, S., Elith, J., Leathwick, J.R.: Presence-only data and the EM algorithm. Biometrics 65(2), 554–563 (2009) ArticleMathSciNetMATHGoogle Scholar
  124. White, R.W., Ruthven, I., Jose, J.M.: A study of factors affecting the utility of implicit relevance feedback. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 35–42 (2005) Google Scholar
  125. Yajima, Y.: One-class support vector machines for recommendation tasks. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 230–239. Springer, Heidelberg (2006). https://doi.org/10.1007/11731139_28ChapterGoogle Scholar
  126. Yu, H., Han, J., Chang, K.C.C.: PEBL: positive example based learning for web page classification using SVM. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 239–248 (2002) Google Scholar
  127. Yu, Z., Zhou, X.: TV3P: an adaptive assistant for personalized TV. IEEE Trans. Consum. Electron. 50(1), 393–399 (2004) ArticleGoogle Scholar
  128. Zanker, M., Jessenitschnig, M.: Collaborative feature-combination recommender exploiting explicit and implicit user feedback. In: Proceedings of the IEEE Conference on Commerce and Enterprise Computing, CEC 2009, pp. 49–56 (2009) Google Scholar
  129. Zhang, Y., Callan, J.: Combining multiple forms of evidence while filtering. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT 2005, pp. 587–595 (2005) Google Scholar
  130. Zhang, Y.C., Séaghdha, D.O., Quercia, D., Jambor, T.: Auralist: introducing serendipity into music recommendation. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM 2012, pp. 13–22 (2012) Google Scholar
  131. Zhao, S., Du, N., Nauerz, A., Zhang, X., Yuan, Q., Fu, R.: Improved recommendation based on collaborative tagging behaviors. In: Proceedings of the 13th ACM International Conference on Intelligent User Interfaces, IUI 2008, pp. 413–416 (2008) Google Scholar
  132. Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Towards mobile intelligence: learning from GPS history data for collaborative recommendation. Artif. Intell. 184/185, 17–37 (2012) ArticleMathSciNetGoogle Scholar
  133. Zibriczky, D., Hidasi, B., Petres, Z., Tikk, D.: Personalized recommendation of linear content on interactive TV platforms: beating the cold start and noisy implicit user feedback. In: ACM Workshop on TV and Multimedia Personalization, UMAP 2012 (2012) Google Scholar
  134. Zigoris, P., Zhang, Y.: Bayesian adaptive user profiling with explicit & implicit feedback. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, CIKM 2006, pp. 397–404 (2006) Google Scholar

Author information

Authors and Affiliations

  1. AAU Klagenfurt, 9020, Klagenfurt, Austria Dietmar Jannach
  2. Department of Computer Science, TU Dortmund University, 44221, Dortmund, Germany Lukas Lerche
  3. Free University of Bozen-Bolzano, 39100, Bozen, Italy Markus Zanker
  1. Dietmar Jannach