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Collaborative Filtering based on Clustering method using Genre and Interest in SNS
Tithrottanak You,Ahmad Nurzid Rosli,Inay Ha,Geun-Sik Jo 한국지능정보시스템학회 2012 한국지능정보시스템학회 학술대회논문집 Vol.2012 No.12
Recommender systems play an important role in online business ecosystem, especially to recommend users’ new items. The most critical problem in the recommender systems is providing high accuracy of recommendation to new users which lack of preference to compute similarity between users. In this paper, we propose a recommender system to solve the cold start problem by combining traditional collaborative filtering of users’ rating preference and the users’ genres interest that derived from SNS. First we compute users’ similarity according to their rating on movies. Second we also compute the users’ similarity from genre interest extracted from SNS. We combine these both similarities information in order to produce new user’s similarity. Our experiment results show that our approach is outperform in cold start problem compared to traditional collaborative filtering.
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Clustering Method based on Genre Interest for Cold-Start Problem in Movie Recommendation
Tithrottanak You(유띳로따낙),Ahmad Nurzid Rosli(누르지드),Inay Ha(하인애),Geun-Sik Jo(조근식) 한국지능정보시스템학회 2013 지능정보연구 Vol.19 No.1
Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans’ Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the “Facebook Page” that refers to specific product. The “Like” option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the “Facebook Page”. In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world’s leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the “sparsity problem”; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is “cold-start problem”; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users’ genre interest extracted from social network service (SNS) and user’s movies rating information system to solve the “cold-start problem.” Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user’s information from Facebook Graph, we can extract information from the “Facebook Page” which “Like” by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the “Facebook Page”. Formerly, the user must login with their Facebook account to l
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