Video recommendation based on multi-modal information and multiple kernel
Pages 4599 - 4616
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.
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
[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
[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
[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
[11]
Grauman K, Darrell T (2007) The pyramid match kernel: efficient learning with sets of features. J Mach Learn Res 8:725---760
[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
[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
[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
[19]
Pazzani M (1999) A framework for collaborative, content-based and demographic filtering. Artif Intell Rev 13(5):393---408
[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
[22]
Shardanand U, Maes P (1995) Social information filtering: algorithms for automating "word of mouth. ACM Conf Hum Factors Comput Syst 1:210---217
[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
Recommendations
Real-time Video Recommendation Exploration
SIGMOD '16: Proceedings of the 2016 International Conference on Management of DataVideo recommendation has attracted growing attention in recent years. However, conventional techniques have limitations in real-time processing, accuracy or scalability for the large-scale video data. To address the deficiencies of current ...
Personalized rough-set-based recommendation by integrating multiple contents and collaborative information
In recent years, explosively-growing information makes the users confused in making decisions among various kinds of products such as music, movies, books, etc. As a result, it is a challenging issue to help the user identify what she/he prefers. To ...
Personalized Recommendation Algorithm Using User Demography Information
WKDD '09: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data MiningPersonalized recommendation systems are web-based systems that aim at predicting a user’s interest on available products and services by relying on previously rated items and dealing with the problem of information and product overload. User demography ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © Copyright © 2015 Springer Science+Business Media New York.
Publisher
Kluwer Academic Publishers
United States
Publication History
Published: 01 June 2015
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 04 Jan 2025