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Video recommendation based on multi-modal information and multiple kernel

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Abstract

Collaborative Filter (CF) algorithms often suffer from data sparsity and item cold start problem, for the user-item matrix is insufficient and extremely sparse especially when new item is added to recommendation system. These two problems also exist in video recommendation process. We propose two methods to solve them by incorporating multimodal information and multiple kernel together. To solve item cold start problem, we learn a user taste hyper-plane by using multiple kernel SVM to represent the user taste, which is further used to predict the recommendation of new added videos. We combine the different user taste hyper-plane similarity and the traditional cosine similarity with a trade-off between them to address the data sparse problem. Experimental results show that our proposed algorithm can alleviate the data sparsity and item cold start problems.

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References

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

    Article  Google Scholar 

  2. Back F, Lanckriet G, Jordan M (2004) Multiple kernel learning, conic duality, and the SMO algorithm. In International Conference on Machine Learning (ICML), Banff, Alberta, Canada, pp. 41–48

  3. Balabanovic M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):766–772

    Article  Google Scholar 

  4. Baluja S, Seth R, Sivakumar D et al (2008) Video suggestion and discovery for youtube: taking random walks through the view graph. In International Conference on World Wide Web (WWW), pp. 895–904

  5. Calcanis C, Callaghan V, Gardner M et al (2008) Towards end-user physiological profiling for video recommendation engines. In International Conference on Intelligent Environments, pp.439–443

  6. Deshpande M, Karypis G (2004) Item-based Top-N recommendation algorithms. ACM Trans Inf Syst 22(1):177–185

    Article  Google Scholar 

  7. Eck D, Lamere P, Thierry B (2007) Recommendation Automatic Generation of Social Tags for Music. In Neural Information Processing Systems (NIPS), Vancouver, British Columbia, Canada, pp. 385-392

  8. Encyclopedia: http://en.wikipedia.org/wiki/YouTube/. Accessed July 2012

  9. Forbes P, Zhu M (2011) Content-boosted Matrix Factorization for Recommender Systems: Exp- eriments with Recipe Recommendation. In Conference on Recommender Systems (RecSys), Chicago, IL, USA, pp.261–264

  10. Gao Y, Peng J, Luo H et al (2009) An interactive approach for filtering out junk images from keyword-based google search results. IEEE Trans Circ Syst Video Technol 19(10):120–129

    Google Scholar 

  11. Grauman K, Darrell T (2007) The pyramid match kernel: efficient learning with sets of features. J Mach Learn Res 8:725–760

    MATH  Google Scholar 

  12. Grcar M, Fortuna B, Mladenic D (2005) kNN Versus SVM in the Collaborative Filtering Framework. In ACM WebKDD, Chicago, Illinois, USA, pp.251–260

  13. Gu Q, Zhou J, Dingy C (2010) Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs. SIAM International Conference on Data Mining (SDM), Columbus, pp 199–210

    Google Scholar 

  14. Guo G (2012) Resolving Data Sparsity and Cold Start in Recommender Systems. In international conference on User Modeling, Adaptation, and Personalization (UMAP), Montreal, Canada, pp. 361–364

  15. Konstan J, Miller B, Maltz D et al (1997) Applying collaborative filtering to Usenet news. Commun ACM 40(3):77–87

    Article  Google Scholar 

  16. Li B, Yang Q, Xue X (2009) Transfer Learning for Collaborative Filtering via a Rating-Matrix Generative Model. In International Conference on Machine Learning (ICML), Montreal, Quebec, Canada, pp.617–624

  17. Luo H, Fan J, Daniel A et al (2009) Personalized News Video Recommendation. In International Multimedia Modeling Conference (MMM), Sophia Antipolis, France, pp.459–471

  18. MacQueen J (1999) Some Methods for classification and Analysis of Multivariate Observations. Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, pp 281–297

    Google Scholar 

  19. Pazzani M (1999) A framework for collaborative, content-based and demographic filtering. Artif Intell Rev 13(5):393–408

    Article  Google Scholar 

  20. Purushotham S, Liu Y, Kuo C (2012) Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems. In International Conference on Machine Learning (ICML), Edinburgh, Scotland, UK, pp.223–230

  21. Sarwar B, Karypis G, Konstan J (2000) Application of dimensionality reduction in recommender system - A case study. ACM WebKDD 2000 Web Mining for E-Commerce Workshop, Boston

    Google Scholar 

  22. Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth. ACM Conf Hum Factors Comput Syst 1:210–217

    Google Scholar 

  23. Xia Y, Dong Y, Xing G (2006) Support vector machines for collaborative filtering. In annual Southeast regional conference (ACM-SE), New York, NY, USA, pp.169–174

  24. Xu J, Araki K (2006) A SVM-based Personal Recommendation System for TV Programs. In International Conference on Multi Media Modeling (MMM), Beijing, China, pp. 401–404

  25. Yang B, Mei T, Hua X et al (2007) Online Video Recommendation Based on Multimodal Fusion and Relevance Feedback. In International conference on Image and video retrieval (CIVR), Amsterdam, The Netherlands, pp.73–80

  26. Zhang D, Yu Z (2007) Spontaneous and Context-aware Media Recommendation in Heterogeneous Spaces. In Vehicular Technology Conferenc (VTC), Baltimore, MD, USA, pp.267–271

  27. Zhang M, Zhou Z (2009) Multi-instance clustering with applications to multi-instance prediction. Appl Intell 31(1):47–68

    Article  Google Scholar 

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Acknowledgments

This research is partly supported by the Special Prophase Project on special fund (973 Program) (Grant No. 2011CB311802), by the Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20106102110028. 20116102110027 and 20126101110022), by Natural Science Foundation of Shaanxi Province of China (Grant No. 2013JQ8022 and 2013JM8031). National Natural Science Foundation of China (Grant No. 61172123,61373117,61172170), Excellent Youth Research Star of Shaanxi Province of China (Grant No. 2012KJXX-35). And we would also like to thank Jun-li Liang for his help during the revision process and the editors and anonymous reviewers for their valuable comments and helpful suggestions, which greatly improved the quality of this paper.

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Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xi’an, China, 710069

    Zhan Li, Jin-Ye Peng, Guo-Hua Geng, Xiao-Jiang Chen & Pan-Pan Zheng

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  1. Zhan Li
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  2. Jin-Ye Peng
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  3. Guo-Hua Geng
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  4. Xiao-Jiang Chen
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  5. Pan-Pan Zheng
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Correspondence to Zhan Li.

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Li, Z., Peng, JY., Geng, GH. et al. Video recommendation based on multi-modal information and multiple kernel. Multimed Tools Appl 74, 4599–4616 (2015). https://doi.org/10.1007/s11042-013-1825-x

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