Context-aware movie recommendations with factorization machines

被引：0

作者：

Jin, Ting ^{[1
,2
]}

Liu, Hui ^{[3
]}

Li, Danqing ^{[1
]}

Chen, Jing ^{[2
]}

机构：

[1] Shanghai Key Lab. of Intelligent Information Processing, Fudan University

[2] School of Information Science and Technology, Hainan University

[3] Research Lab. of Information Management, Changzhou University

来源：

Journal of Computational Information Systems | 2014年 / 10卷 / 08期

关键词：

Context-aware recommender systems; Factorization machines; Movie recommendations; Rating prediction; Relevant context;

D O I：

10.12733/jcis11151

中图分类号：

学科分类号：

摘要：

Recommender Systems play an important role in handing large amounts of information and support users by recommending content considered as being particularly interesting for them. In this paper, Contextaware Movie Recommender System is implemented by using the Collaborative Filtering framework that is integrated with a factorization machine approach as illustrated in the generic model. CARS makes use of multiple contextual information which provides useful details about user's current situation in order to give more relevant recommendations to users. In an offline experiment, it has been compared with other approaches (e.g. SVD++, KNN Item-based), with results that show significant improvement in the accuracy of recommendations. In addition, selecting suitable contextual features is important since only few of them are useful. This enables the error of rating prediction to be reduced. © 2014 Binary Information Press.

引用

页码：3313 / 3323

页数：10

共 19 条

[1]

Adomavicius G., Tuzhilin A., Context-aware recommender systems, Recommender Systems Handbook, pp. 217-256, (2011)

[2]

Dey A., Abowd G., Towards a better understanding of context and context-awareness, Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing, pp. 304-307, (1999)

[3]

Adomavicius G., Tuzhilin A., Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, 17, 6, pp. 734-749, (2005)

[4]

Toutain C., Bouabdallah F., Zemek A., Daloz R., Interpersonal context-aware communication services, IEEE Communications Magazine, pp. 68-74, (2011)

[5]

Adomavicius G., Sankaranarayanan R., Sen S., Tuzhilin A., Incorporating contextual information in recommender systems using a multidimensional approach, ACM Transactions on Information Systems, 23, 1, pp. 103-145, (2005)

[6]

Anand S.S., Mobasher B., Contextual recommendation, WebMine, pp. 142-160, (2006)

[7]

Berkovsky S., Kuflik T., Aroyo L., Heckmann D., Kroner A., Ricci F., Houben G.-J., Predicting user experiences through cross-context reasoning, LWA, pp. 27-31, (2006)

[8]

Rendle S., Gantner Z., Freudenthaler C., Lars schmidt-thieme, fast context-aware recommendations with factorization machines, SIGIR'11, (2011)

[9]

Rendle S., Factorization machines, data mining (ICDM), 2010 IEEE 10th International Conference on, pp. 995-1000, (2010)

[10]

Su X., Khoshgoftaar T.M., A survey of collaborative filtering techniques, Advances in Artificial Intelligence, (2009)

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