Movie Recommendation Algorithm Using Social Network Analysis to Alleviate Cold-Start Problem
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초록
With the rapid increase of information on the World Wide Web, finding useful information on the internet hasbecome a major problem. The recommendation system helps users make decisions in complex data areas wherethe amount of data available is large. There are many methods that have been proposed in the recommendersystem. Collaborative filtering is a popular method widely used in the recommendation system. However,collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we proposea movie recommendation system by using social network analysis and collaborative filtering to solve thisproblem associated with collaborative filtering methods. We applied personal propensity of users such as age,gender, and occupation to make relationship matrix between users, and the relationship matrix is applied tocluster user by using community detection based on edge betweenness centrality. Then the recommendedsystem will suggest movies which were previously interested by users in the group to new users. We show shownthat the proposed method is a very efficient method using mean absolute error.
Abstract
With the rapid increase of information on the World Wide Web, finding useful information on the internet hasbecome a major problem. The recommendation system helps users make decisions in complex data areas wherethe amount of data available is large. There are many methods that have been proposed in the recommendersystem. Collaborative filtering is a popular method widely used in the recommendation system. However,collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we proposea movie recommendation system by using social network analysis and collaborative filtering to solve thisproblem associated with collaborative filtering methods. We applied personal propensity of users such as age,gender, and occupation to make relationship matrix between users, and the relationship matrix is applied tocluster user by using community detection based on edge betweenness centrality. Then the recommendedsystem will suggest movies which were previously interested by users in the group to new users. We show shownthat the proposed method is a very efficient method using mean absolute error.
참고문헌 (27)
- "Digital in 2018: world’s Internet users pass the 4 billion mark" (2018)[URL]
- S. M. Albladi, "User characteristics that influence judgment of social engineering attacks in social networks", Human-centric Computing and Information Sciences, 8 (2018)
- 정운해, "Performance Improvement of a Movie Recommendation System based on Personal Propensity and Secure Collaborative Filtering", JIPS(Journal of Information Processing Systems), 9(1), pp.157~172 (2013), 한국정보처리학회[DOI]
- L. T. Ponnam, "Movie recommender system using item based collaborative filtering technique", Proceedings of 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), pp.1~5 (2016)
- W. Li, "Personalized fitting recommendation based on support vector regression", Human-centric Computing and Information Sciences, 5 (2015)
- S. K. Tiwari, "An approach for recommender system by combining collaborative filtering with user demographics and items genres", International Journal of Computer Applications, 128(13), pp.16~24 (2015)
- O. H. Embarak, "A method for solving the cold start problem in recommendation systems", Proceedings of 2011 International Conference on Innovations in Information Technology, pp.238~243 (2011)
- J. Basiri, "Alleviating the cold-start problem of recommender systems using a new hybrid approach", Proceedings of 2010 5th International Symposium on Telecommunications, pp.962~967 (2010)
- F. Hao, "When social computing meets soft computing: opportunities and insights", Human-centric Computing and Information Sciences, 8 (2018)
- Fei Hao, "Similarity Evalution between Graphs: A Formal Concept Analysis Approach", JIPS(Journal of Information Processing Systems), 13(5), pp.1158~1167 (2017), 한국정보처리학회
- "ExpertSeer: a keyphrase based expert recommender for digital libraries" (2015)[URL]
- H. H. Chen, "Collabseer: a search engine for collaboration discovery", Proceedings of the 11th Annual International ACM, 2011, 231~240
- A. Felfernig, "The VITA financial services sales support environment", Proceedings of the 19th National Conference on Innovative Applications of Artificial Intelligence, pp.1692~1699 (2007)
- P. Gupta, "WTF: the who to follow system at Twitter", Proceedings of the 22nd International Conference on World Wide Web, pp.505~514 (2013)
- A. Souri, "Personality classification based on profiles of social networks’users and the five-factor model of personality", Human-centric Computing and Information Sciences, 8 (2018)
- C. C. Aggarwal, "Recommender Systems" (2016), Springer International Publishing
- L. Terveen, "Human-Computer Interaction in the New Millennium", pp.487~509 (2001), Addison-Wesley
- H. T. Zou, "Online group recommendation with local optimization", Computer Modeling in Engineering & Sciences, 115(2), pp.217~231 (2018)
- G. Adomavicius, "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions", IEEE Transactions on Knowledge & Data Engineering, 17(6), pp.734~749 (2005)
- Y. Wang, "Research on collaborative filtering recommendation algorithm based on Mahout", Proceedings of 2016 4th International Conference on Applied Computing and Information Technology, Intelligence and Applied Informatics, pp.1st International Conference on Big Data, Cloud Computing, Data Science & Engineering (ACIT-CSII-BCD), 2016
- H. Tan, "A collaborative filtering recommendation algorithm based on item classification", Proceedings of 2009 Pacific-Asia Conference on Circuits, Communications and Systems, pp.694~697 (2009)
- M. Vedanayaki, "A study of data mining and social network analysis", Indian Journal of Science and Technology, 7(S7), pp.185~187 (2014)
- J. Palau, "Cooperative Information Agents VIII", pp.137~151 (2004), Springer
- M. Girvan, "Community structure in social and biological networks", Proceedings of the National Academy of Sciences, 99(12), pp.7821~7826 (2002)
- B. N. Miller, "MovieLens unplugged: experiences with an occasionally connected recommender system", Proceedings of the 8th International Conference on Intelligent User Interfaces, pp.263~266 (2003)
- F. Ye, "A collaborative filtering recommendation based on users' interest and correlation of items", Proceedings of 2016 International Conference on Audio, Language and Image Processing (ICALIP), pp.515~520 (2016)
- S. G. Moghaddam, "A scalable collaborative recommender algorithm based on user densitybased clustering", Proceedings of the 3rd International Conference on Data Mining and Intelligent Information Technology Applications, pp.246~249 (2011)