D ec 2 01 4 Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

Collaborative filtering algorithms haven been widely used in recommender systems. However, they often suffer from the data sparsity and cold start problems. With the increasing popularity of social media, these problems may be solved by using social-based recommendation. Social-ba sed recommendation, as an emerging research area, uses social information to help mitigate the data sparsity and cold start problems, and it has been demonstrated that the social-base d recommendation algorithms can efficiently improve the recommendation performance. However, few of the existing algori thms have considered using multiple types of relations within on e social network. In this paper, we investigate the social-ba sed recommendation algorithms on heterogeneous social networ ks and proposed Hete-CF, a Social Collaborative Filtering algorithm using heterogeneous relations. Distinct from the exiting methods, Hete-CF can effectively utilize multiple types of relations in a heterogeneous social network. In addition, Hete-CF is a genral approach and can be used in arbitrary social networks, including event based social networks, location based social network s, and any other types of heterogeneous information networks asso ciated with social information. The experimental results on two realworld data sets, DBLP (a typical heterogeneous information network) and Meetup (a typical event based social network) s how the effectiveness and efficiency of our algorithm. I. I NTRODUCTION With the advent of Internet era and the emergence of Big Data, users constantly suffered from information overload . Therefore, recommendation systems, as effective methods to deal with information overload, become a very popular research topic in recent years [1, 2]. On the other hand, recommendation systems also help e-commerce companies provide personalised services [3]. Many companies, includ ing YouTubeand Amazon, have launched their own personalised recommender systems so that they can provide better service s. Among many recommendation algorithms, Collaborative Filtering [2] has been widely used in both social networks and online stores. Most of the collaborative filtering metho ds aim to provide recommendations or rating predictions based on historical user-item preference records [1]. However, i n the real world, users only rate a limited number of items. For example, inAmazon, a user always buys a small fraction of all available commodities, which makes the corresponding user item information matrix very sparse. Consequently, collab or tive filtering recommendation algorithms severely suffer f rom the cold start and data sparsity problems [4]. In order to deal with the data sparsity problem, many algorithms have been proposed. Social-based recommendati o , as one of the efficient and emerging methods, has attracted much attention in recent years [1, 2, 5–9]. Social-based recommendation utilises social information to help improv e the recommendation performance. For example, Zhang et al. [5] consider the recommendation system on EBSN (Event Based Social Network) [6], which contains both online and offline networks. The algorithm presented in [5] uses the soc ial information extracted from offline networks to help make rec ommendations in online networks. Another recommendation system research on EBSN also demonstrates the effectivenes s of this method [7]. In [7], the author proposed LCARS, a location-content-aware recommender system, and LCARS considers both personal interests and local preferences to make recommendations. In IJCAI-13, Yang et al.proposed the TrustMF recommendation algorithm [2]. TrustMF considers the trust and trustee information between users from the soc ial network. Similar to TrustMF, Xiao Yuet al. proposed several recommendation algorithms [8, 9] based on heterogeneous information networks by introducing the relationships bet w en items. In [1], the cross-domain knowledge was used to improv e the recommendation performance. In this paper, we will intr oduce the above-related research in detail in Section V. Previous research has demonstrated that more effective information could lead to better recommendation results [2 , 6]. However, most of the above algorithms only use part of the information in social networks (either user-user or ite mitem information). In order to make better use of the social information, in this work we study the collaborative filteri ng method on heterogeneous social networks. Different from previous research, in this paper we will utilize all three ty pes of relations, that is, not only the user-user and item-item rel ations, but also the user-item relations. As in [9, 10], a heterogeneous social network contains more information than homogeneous ones and may have more semantic meaning. Fig.1 shows a simple heterogeneous network (Bibliographic Network), and from this figure we can see that there are three types of objects in this network: Author, Paper, and Conference. It is also noted that there is more than one relation in the network. In order to utilize the se elations in a heterogeneous network, we can use meta-path [11] to present each type of relations. Meta-path is an effec tiv way to represent relationships in heterogeneous informati on networks [11]. For example, the co-author relationship can