Recommender systems associated with social networks often use social explanations (e.g. "X, Y and 2 friends like this") to support the recommendations. We present a study of the effects of these social explanations in a music recommendation context. We start with an experiment with 237 users, in which we show explanations with varying levels of social information and analyze their effect on users' decisions. We distinguish between two key decisions: the likelihood of checking out the recommended artist, and the actual rating of the artist based on listening to several songs. We find that while the explanations do have some influence on the likelihood, there is little correlation between the likelihood and actual (listening) rating for the same artist. Based on these insights, we present a generative probabilistic model that explains the interplay between explanations and background information on music preferences, and how that leads to a final likelihood rating for an artist. Acknowledging the impact of explanations, we discuss a general recommendation framework that models external informational elements in the recommendation interface, in addition to inherent preferences of users.
Models based on these theories and the availability of social connection information have been proposed to support collaborating filtering algorithms that use social information @cite_17 @cite_8 , focusing on preferences in users' immediate social networks @cite_27 @cite_1 and computing trust between people in networks @cite_4 to improve recommendations.
@cite_4 @cite_8 @cite_1 @cite_27 @cite_17
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In Service-Oriented Computing environments, there is a large number of service providers providing a variety of services to service customers. Conventional recommender systems, which adopt the information filtering techniques, can be used to automatically generate recommendations of service providers to service customers who are also the system users. However, data sparsity and trust enhancement are the traditional problems in recommender systems. Targeting the data sparsity problem, recent studies on recommender systems have started to leverage information from online social networks to collect recommendations from more participants and derive the final recommendation. However, this requires the methods to infer the trust between participants without any direct interactions in online social networks, which should take into account both the social context of participants and the context of the target services to be recommended, for trust enhanced recommendations. In this paper, we first present a contextual social network model that takes into account both participants' personal characteristics (referred to as the independent social context, including preference and expertise in domains) and mutual relations (referred to as the dependent social context, including the trust, social intimacy, and interaction context between two participants). In addition, we propose a new probabilistic approach, SocialTrust, as the first solution in the literature, to social context-aware trust inference in social networks. The result delivered by this approach is particularly important in evaluating the trust from a source participant to an end recommender who recommends a target service or service provider, via the sub-network consisting of intermediate participants recommenders between them and relevant contextual information. Moreover, we propose algorithms that consider cycles and information updates in social networks. Experiments demonstrate that our approach is effective and superior to existing trust inference methods, and can deliver more reasonable and trustworthy results. The proposed algorithms considering cycles and information updates in social networks are efficient and applicable to real social networks. In the age of information overload, collaborative filtering and recommender systems have become essential tools for content discovery. The advent of online social networks has added another approach to recommendation whereby the social network itself is used as a source for recommendations i.e. users are recommended items that are preferred by their friends. In this paper we develop a new model-based recommendation method that merges collaborative and social approaches and utilizes implicit feedback and the social graph data. Employing factor models, we represent each user profile as a mixture of his own and his friends' profiles. This assumes and exploits "homophily" in the social network, a phenomenon that has been studied in the social sciences. We test our model on the Epinions data and on the Tuenti Places Recommendation data, a large-scale industry dataset, where it outperforms several state-of-the-art methods. 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 who 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 in a principled way. 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. Our experiments demonstrate that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users. 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.