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Top1. Introduction
With the development of Internet technology, various kinds of information online provides colorful and convenient life to people, and they are now used to reading news and updates of friends from Internet, buying books, commodities and various things online, watching movies and listening to music through Internet. However, on the other hand, with the explosion of information, more and more people find it hard to get the information that they really require and are interested in quickly and effectively. In such a background, recommender system is proposed and applied in many online platforms as an effective way to solve the problem of “information overload”. It can provide more personalized services by predicting potential interests according to users’ historical choices. Recommender systems have already been applied widely. For example, Amazon.com uses one’s purchase records to recommend books, Adaptive Info.com uses one’s reading history to recommend news, and the TiVo digital video system recommends TV shows and movies on the basis of users’ viewing patterns and ratings.
The recommender system is very helpful for filtering information, and the core of recommender system is the recommendation algorithm. Recommender system helps users make choices by the way of information filtering, and a successful recommendation to one people may influence subsequent recommendation to other people. The influence is expanded greatly with successive recommendations. From such perspective, it can also explain the evolution of a popular movie.
Recommender systems typically produce a list of recommendations in one of the three ways: content-based (Peng, 2010), collaborative filtering (Chen, 2012; Li, 2010) and context-aware approaches (Adomavicius, 2011; Panniello, 2014). Content-based filtering is also referred to cognitive filtering, which recommends items based on a comparison between the content of the items and the user profiles. Collaborative filtering approaches are based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. Whereas context-aware approaches consider the influence of context factors, such as natural situation (time, location and etc.) and user’s profile (age, gender, profession and etc.), on users’ demand, preferences, and the selection and definition of neighbors. Based on the basic algorithms, many extensions have been made (Zheng, 2014; Habegger, 2014; Chen, 2010; Zhang, 2014; Xu, 2013; Zhang, 2015).