User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation

A huge amount of user generated content related to movies is created with the popularization of web 2.0. With these continues exponential growth of data, there is an inevitable need for recommender systems as people find it difficult to make informed and timely decisions. Movie recommendation systems assist users to find the next interest or the best recommendation. In this proposed approach the authors apply the relationship of user feature-scores derived from user-item interaction via ratings to optimize the prediction algorithm’s input parameters used in the recommender system to improve the accuracy of predictions with less past user records. This addresses a major drawback in collaborative filtering, the cold start problem by showing an improvement of 8.4% compared to the base collaborative filtering algorithm. The user-feature generation and evaluation of the system is carried out using the ‘MovieLens 100k dataset’. The proposed system can be generalized to other domains as well.

[1]  SongJie Gong,et al.  A personalized recommendation system combining case-based reasoning and user-based collaborative filtering , 2009, 2009 Chinese Control and Decision Conference.

[2]  Chen Yang,et al.  A movie cold-start recommendation method optimized similarity measure , 2016, 2016 16th International Symposium on Communications and Information Technologies (ISCIT).

[3]  Thomas B. Sheridan,et al.  Swiss army knife and Ockham's razor: modeling and facilitating operator's comprehension in complex dynamic tasks , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[4]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[5]  Jian Yin,et al.  Effective association clusters filtering to cold-start recommendations , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[6]  Barry Smyth,et al.  PTV: Intelligent Personalised TV Guides , 2000, AAAI/IAAI.

[7]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[8]  Hyung Jun Ahn,et al.  A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..

[9]  Simon Fong,et al.  Using Genetic Algorithm for Hybrid Modes of Collaborative Filtering in Online Recommenders , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[10]  Zhigang Luo,et al.  A content-enhanced approach for cold-start problem in collaborative filtering , 2011, 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC).

[11]  F. O. Isinkaye,et al.  Recommendation systems: Principles, methods and evaluation , 2015 .

[12]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[13]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

[14]  Wei Chu,et al.  Information Services]: Web-based services , 2022 .

[15]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[16]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.