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Incorporating contextual information and collaborative filtering methods for multimedia recommendation in a mobile environment

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Abstract

Recommender systems have been developed in different application services. In addition to using recommendation techniques, it is helpful to employ contextual information in determining the relevance of an item to a users’s needs. To enhance recommendation performance, we present in this study two approaches that, in a direct way, integrate different types of contextual information and user ratings in computational methods. To verify the proposed approaches in making collaborative recommendations, we conduct a series of experiments to evaluate performance. The results show that the proposed context-aware methods outperform other conventional approaches. Moreover, we implement a mobile multimedia recommendation system on a cloud platform to demonstrate how our approaches can be used to develop a real-world application.

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References

  1. Adomavicius G, Sankaranarayanan R, Sen S, Tuzhilin A (2005) Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans Inf Syst 23(1):103–145

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Adomavicius G, Tuzhilin A (2011) Context-aware recommender systems. In: Ricci, L. Rokach, B. Shapira, P. Kantor (Eds.), Recommender Systems Handbook, Springer, Chapter 7, pp 217–253

  4. Agarwal D, Chen BC, Long B (2011) Localized factor models for multi-context recommendation. In: Proc ACM SIGKDD Int Conf Knowledge Discovery Data Mining, pp 609–617

  5. Baltrunas L, Ludwig B, Peer S, Ricci F (2013) Context relevance assessment and exploitation in mobile recommender systems. Pers Ubiquit Comput 16(5):507–526

    Article  Google Scholar 

  6. Baltrunas L, Ricci F (2009) Context-based splitting of item ratings in collaborative fltering. In: Proc ACM Int Conf Recommender Syst, pp 245–248

  7. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132

    Article  Google Scholar 

  8. Bottou L, Bousquet O (2008) The tradeoffs of large scale learning. Adv Neural Information Proc Syst 20:161–168

    Google Scholar 

  9. Campos PG, Díez F, Cantador I (2014) Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model User-Adap Inter 24(1–2):67–119

    Article  Google Scholar 

  10. Chen K, Chen T, Zheng G, Jin O, Yao E, Yu Y (2012) Collaborative personalized tweet recommendation. In: Proc ACM SIGIR Int Conf Res Develop Information Retrieval, pp 661–670

  11. Cheng Z, Shen J (2014) Just-for-Me: an adaptive personalization system for location-aware social music recommendation. In: Proc ACM SIGIR Int Conf Res Dev Information Retrieval, pp 1267–1268

  12. de Pessemier T, Dooms S, Martens L (2014) Context-aware recommendations through context and activity recognition in a mobile environment. Multimedia Tools and Applications 72(3):2925–2948

    Article  Google Scholar 

  13. He Q, Pei J, Kifer D, Mitra P, Giles L (2010) Context-aware citation recommendation. In: Proc 19th Int Conf World Wide Web, pp 421–430

  14. Hong JY, Suh EH, Kim SJ (2009) Context-aware systems: a literature review and classification. Expert Syst Appl 36(4):8509–8522

    Article  Google Scholar 

  15. Karatzoglou A, Amatriain X, Baltrunas L, Oliver N (2010) Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proc ACM Int Conf Recommender Syst, pp 79–86

  16. Khan WZ, Xiang Y, Aalsalem MY, Arshad Q (2013) Mobile phone sensing systems: a survey. IEEE Commun Surveys Tutorials 15(1):402–427

    Article  Google Scholar 

  17. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proc ACM SIGKDD Int Conf Knowledge Discovery Data Mining, pp 426–434

  18. Koren Y, Bell R (2011) Advances in collaborative filtering. In: F Ricci, L Rokach, B Shapira, P Kantor (Eds.), Recommender Systems Handbook, pp. 1–42

  19. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. IEEE Comput 42(8):30–37

    Article  Google Scholar 

  20. Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. IEEE Commun Magazine 48(9):140–150

    Article  Google Scholar 

  21. LDOS-CoMoDa dataset. University of Ljubljana, July 2012, http://212.235.187.145/ spletnastran/raziskave/um/comoda/comoda.php

  22. Lee WP, Kaoli C, Huang JY (2014) A smart TV system with body-gesture control, tag-based rating and context-aware recommendation. Knowl-Based Syst 56:167–178

    Article  Google Scholar 

  23. Lee WP, Lee KH (2014) Making smartphone service recommendations by predicting users’ intentions: a context-aware approach. Inf Sci 277:21–35

    Article  Google Scholar 

  24. Li B, Yang Q, Xue X (2009) Transfer learning for collaborative filtering via a rating-matrix generative model. In: Proc Twenty-Sixth Annual Int Conf Machine Learning, pp 617–624

  25. Liu L, Lecue F, Mehandjiev N, Xu L (2010) Using context similarity for service recommendation. In: Proc IEEE Fourth Int Conf Semantic Comput, pp 277–284

  26. Mnih A, Salakhutdinov R (2007) Probabilistic matrix factorization. In Adv Neural Information Proc Syst, pp 1257–1264

  27. Nguyen TV, Karatzoglou A, Baltrunas L (2014) Gaussian process factorization machines for context-aware recommendations. In: Proc Thirty-Seventh Int ACM SIGIR Conf Res Dev Information Retrieval, pp 63–72

  28. Pan W, Liu NN, Xiang EW, Yang Q (2011) Transfer learning to predict missing ratings via heterogeneous user feedbacks. In: Proc Twenty-Second Int Joint Conf Artificial Intelligence, pp 2318–2323

  29. Panniello U, Gorgoglione M (2012) Incorporating context into recommender systems: an empirical comparison of context-based approaches. Electron Commer Res 12(1):1–30

    Article  Google Scholar 

  30. Panniello U, Tuzhilin A, Gorgoglione M, Palmisano C, Pedone A (2009) Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. In: Proc ACM Int Conf Recommender Systems, pp 265–268

  31. Panniello U, Tuzhilin A, Gorgoglione M (2014) Comparing context-aware recommender systems in terms of accuracy and diversity. User Model User-Adap Inter 24(1–2):35–65

    Article  Google Scholar 

  32. Porteous I, Asuncion AU, Welling M (2010) Bayesian matrix factorization with side information and Dirichlet process mixtures. In: Proc 24th AAAI Conf Artificial Intelligence, pp 563–568

  33. Qian X, Feng H, Zhao G, Mei T (2014) Personalized recommendation combining user interest and social circle. IEEE Trans Knowledge Data Eng 26(7):1487–1502

    Article  Google Scholar 

  34. Querciaxy D, Lathiaz N, Calabresey F, Di Lorenzoy G, Crowcroftx J (2010) Recommending social events from mobile phone location data. In: Proc IEEE Int Conf Data Mining, pp 971–976

  35. Rendle S (2012) Factorization machines with libfm. ACM Trans Intelligent Syst Tech 3(3), 57

    Google Scholar 

  36. Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In: F Ricci, L Rokach, B Shapira, P Kantor, (Eds.), Recommender Systems Handbook, pp 1–35

  37. Sánchez-Pi N, Carbó J, Molina JM (2012) A knowledge-based system approach for a context-aware system. Knowl-Based Syst 27:1–17

    Article  Google Scholar 

  38. Shi Y, Larson M, Hanjalic A (2014) Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput Surv 47(1), 3

    Article  Google Scholar 

  39. Soares M, Viana P (2014) Tuning metadata for better movie content-based recommendation systems. Multimedia Tools Appl. doi:10.1007/s11042- 014-1950-1

    Google Scholar 

  40. Strobbe M, van Laere O, Ongenae F, Dauwe S, Dhoedt B, de Turck F, Demeester P, Luyten K (2012) Novel applications integrate location and context information. IEEE Pervasive Comput 11(2):64–73

    Article  Google Scholar 

  41. Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artificial Intelligence 2009: Article no 421425

  42. Su JH, Yeh HH, Yu PS, Tseng VS (2010) Music recommendation using content and context information mining. Intelligent Syst 25(1):16–26

    Article  Google Scholar 

  43. Xiong L, Chen X, Huang TK, Schneider J, Carbonell JG (2010) Temporal collaborative filtering with Bayesian probabilistic tensor factorization. In: Proc SIAM Conf Data Mining, pp 211–222

  44. Zhong E, Fan W, Yang Q (2012) Contextual collaborative filtering via hierarchical matrix factorization. In: Proc SIAM Int Conf Data Mining, pp 744–755

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Authors and Affiliations

  1. Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan

    Wei-Po Lee & Guan-Yu Tseng

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  1. Wei-Po Lee
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  2. Guan-Yu Tseng
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Correspondence to Wei-Po Lee.

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Lee, WP., Tseng, GY. Incorporating contextual information and collaborative filtering methods for multimedia recommendation in a mobile environment. Multimed Tools Appl 75, 16719–16739 (2016). https://doi.org/10.1007/s11042-015-2915-8

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  • DOI: https://doi.org/10.1007/s11042-015-2915-8

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