Alleviating the cold-start problem by incorporating movies facebook pages

作者：Rosli Ahmad Nurzid; You Tithrottanak; Ha Inay; Chung Kyung Yong; Jo Geun Sik^*

来源：Cluster Computing, 2015, 18(1): 187-197.

DOI：10.1007/s10586-014-0355-2

• Cold-start problem
• Recommender system
• Collaborative filtering
• Clustering
• Genre interest
• Social network

Recommender systems are generally known as predictive ecosystem which recommends an appropriate list of items that may imply their similar preference or interest. Nevertheless, most discussed issues in recommendation system research domain are the cold-start problem. In this paper we proposed a novel approach to address this problem by combining similarity values obtain from a movie "Facebook Pages". To achieve this, we first compute users' similarity according to the rating cast on our Movie Rating System. Then, we combined similarity value obtain from user's genre interest in "Like" information extracted from "Facebook Pages". Finally, all the similarity values are combined to produce a new user's similarity value. Our experiment results show that our approach is outperformed in cold-start problem compared to the benchmark algorithms. To evaluate whether our system is strong enough to recommend higher accuracy recommendation to users, we also conducted prediction coverage in this research work.

• 出版日期2015-3