Recent research has unveiled the importance of online social networks for improving the quality of recommender systems and encouraged the research community to investigate better ways of exploiting the social information for recommendations. To contribute to this sparse field of research, in this paper we exploit users' interactions along three data sources (marketplace, social network and location-based) to assess their performance in a barely studied domain: recommending products and domains of interests (i.e., product categories) to people in an online marketplace environment. To that end we defined sets of content- and network-based user similarity features for each data source and studied them isolated using an user-based Collaborative Filtering (CF) approach and in combination via a hybrid recommender algorithm, to assess which one provides the best recommendation performance. Interestingly, in our experiments conducted on a rich dataset collected from SecondLife, a popular online virtual world, we found that recommenders relying on user similarity features obtained from the social network data clearly yielded the best results in terms of accuracy in case of predicting products, whereas the features obtained from the marketplace and location-based data sources also obtained very good results in case of predicting categories. This finding indicates that all three types of data sources are important and should be taken into account depending on the level of specialization of the recommendation task.
Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods. A social tagging system provides users an effective way to collaboratively annotate and organize items with their own tags. A social tagging system contains heterogeneous information like users' tagging behaviors, social networks, tag semantics and item profiles. All the heterogeneous information helps alleviate the cold start problem due to data sparsity. In this paper, we model a social tagging system as a multi-type graph. To learn the weights of different types of nodes and edges, we propose an optimization framework, called OptRank. OptRank can be characterized as follows:(1) Edges and nodes are represented by features. Different types of edges and nodes have different set of features. (2) OptRank learns the best feature weights by maximizing the average AUC (Area Under the ROC Curve) of the tag recommender. We conducted experiments on two publicly available datasets, i.e., Delicious and Last.fm. Experimental results show that: (1) OptRank outperforms the existing graph based methods when only (user, tag, item) relation is available. (2) OptRank successfully improves the results by incorporating social network, tag semantics and item profiles. While social interactions are critical to understanding consumer behavior, the relationship between social and commerce networks has not been explored on a large scale. We analyze Taobao, a Chinese consumer marketplace that is the world's largest e-commerce website. What sets Taobao apart from its competitors is its integrated instant messaging tool, which buyers can use to ask sellers about products or ask other buyers for advice. In our study, we focus on how an individual's commercial transactions are embedded in their social graphs. By studying triads and the directed closure process, we quantify the presence of information passing and gain insights into when different types of links form in the network. Using seller ratings and review information, we then quantify a price of trust. How much will a consumer pay for transaction with a trusted seller? We conclude by modeling this consumer choice problem: if a buyer wishes to purchase a particular product, how does (s)he decide which store to purchase it from? By analyzing the performance of various feature sets in an information retrieval setting, we demonstrate how the social graph factors into understanding consumer behavior. Music plays an important role in our everyday lives. Not surprisingly, shared musical taste is said to lead to social attraction. In this paper, we study in detail friendship links on the social music platform Last.fm, asking for similarities in taste as well as on demographic attributes and local network structure. On Last.fm, users connect to 'online' friends as usual, but also indicate strong 'real-life' friends by co-attending the same events. Thus, we can contrast these online ties with offline links of different strength. Complementing the analysis, we learn to predict both kinds of ties automatically, including public interaction data as additional relevant features. Our results emphasize the predictive power of the simple measure of mutual friends, while the indicative value of similarity on taste (though increasing with tie strength) is negligible. Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network among users and makes recommendations for a user based on the ratings of the users that have direct or indirect social relations with the given user. As one of their major benefits, social network based approaches have been shown to reduce the problems with cold start users. In this paper, we explore a model-based approach for recommendation in social networks, employing matrix factorization techniques. Advancing previous work, we incorporate the mechanism of trust propagation into the model. Trust propagation has been shown to be a crucial phenomenon in the social sciences, in social network analysis and in trust-based recommendation. We have conducted experiments on two real life data sets, the public domain Epinions.com dataset and a much larger dataset that we have recently crawled from Flixster.com. Our experiments demonstrate that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.