Mohsen Jamali

A matrix factorization technique with trust propagation for recommendation in social networks

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.

TrustWalker : a random walk model for combining trust-based and item-based recommendation

Collaborative filtering is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it cannot make recommendations for so-called cold start users that have rated only a very small number of items. In addition, these methods do not know how confident they are in their recommendations. Trust-based recommendation methods assume the additional knowledge of a trust network among users and can better deal with cold start users, since users only need to be simply connected to the trust network. On the other hand, the sparsity of the user item ratings forces the trust-based approach to consider ratings of indirect neighbors that are only weakly trusted, which may decrease its precision. In order to find a good trade-off, we propose a random walk model combining the trust-based and the collaborative filtering approach for recommendation. The random walk model allows us to define and to measure the confidence of a recommendation. We performed an evaluation on the Epinions dataset and compared our model with existing trust-based and collaborative filtering methods.

Using a trust network to improve top-N recommendation

Top-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended for top-N recommendation, but this approach does not work accurately for cold start users that have rated only a very small number of items. In this paper we propose novel methods exploiting a trust network to improve the quality of top-N recommendation. The first method performs a random walk on the trust network, considering the similarity of users in its termination condition. The second method combines the collaborative filtering and trust-based approach. Our experimental evaluation on the Epinions dataset demonstrates that approaches using a trust network clearly outperform the collaborative filtering approach in terms of recall, in particular for cold start users.

A transitivity aware matrix factorization model for recommendation in social networks

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.

ETF: extended tensor factorization model for personalizing prediction of review helpfulness

Online reviews are valuable sources of information for a variety of decision-making processes such as purchasing products. As the number of online reviews is growing rapidly, it becomes increasingly difficult for users to identify those that are helpful. This has motivated research into the problem of identifying high quality and helpful reviews automatically. The current methods assume that the helpfulness of a review is independent from the readers of that review. However, we argue that the quality of a review may not be the same for different users. For example, a professional and an amateur photographer may rate the helpfulness of a review very differently. In this paper, we introduce the problem of predicting a personalized review quality for recommendation of helpful reviews. To address this problem, we propose a series of increasingly sophisticated probabilistic graphical models, based on Matrix Factorization and Tensor Factorization. We evaluate the proposed models using a database of 1.5 million reviews and more than 13 million quality ratings obtained from Epinions.com. The experiments demonstrate that the proposed latent factor models outperform the state-of-the art approaches using textual and social features. Finally, our experiments confirm that the helpfulness of a review is indeed not the same for all users and that there are some latent factors that affect a users evaluation of the review quality.

Modeling the temporal dynamics of social rating networks using bidirectional effects of social relations and rating patterns

In this paper we first observe and analyze the temporal behavior of users in a social rating network on expressing ratings and creating social relations. Then, we model the temporal dynamics of a SRN based on our observations and using bidirectional effects of ratings and social relationships. While existing models for other types of social networks have captured some of the factors, our model is the first one to represent all four factors, i.e. social relations-on-ratings (social influence), social relations-on-social relations (transitivity), ratings-on-social relations (selection), and ratings-on-ratings (correlational influence). We also model the strength of each effect throughout the evolution of a SRN. Using our model, we develop a generative model for SRNs. Such a model can serve as basis for several purposes, in particular link prediction, rating prediction and prediction of future community structures. Given the sensitive nature of social network data, there are only very few public social rating network datasets. This motivates the development of generative models to create such synthetic datasets for research purposes. Our experimental study on the Epinions dataset demonstrates that the proposed model produces social rating networks that agree with real world data on a comprehensive set of evaluation criteria much better than existing models.

FeedbackTrust: using feedback effects in trust-based recommendation systems

With the advent of online social networks, the trust-based approach to recommendation has emerged which exploits the trust network among users and makes recommendations based on the ratings of trusted users in the network. In this paper, we introduce a two dimensional trust model which dynamically gets updated based on userss feedbacks, in contrast to static trust values in current trust models. Explorability measures the extent to which a user can rely on recommendations returned by the social network of a trusted user. Dependability represents the extent to which a users own ratings can be trusted by users trusting him directly and indirectly. We propose a method to learn the values of explorability and dependability from raw trust data and feedback expressed by users on the recommendations they receive. Positive feedback will increase the trust and negative feedback will decrease the trust among users. We performed an evaluation on the Epinions dataset, demonstrating that exploiting user feedback results in lower prediction error compared to existing trust-based and collaborative filtering approaches.

A generalized stochastic block model for recommendation in social rating networks

The rapidly increasing availability of online social networks and the well-known effect of social influence have motivated research on social-network based recommenders. Social influence and selection together lead to the formation of communities of like-minded and well connected users. Exploiting the clustering of users and items is one of the most important approaches for model-based recommendation. Users may belong to multiple communities or groups, but only a few clustering algorithms allow clusters to overlap. One of these algorithms is the probabilistic EM clustering method, which assumes that data is generated from a mixture of Gaussian models. The mixed membership stochastic block model (MMB) transfers the idea of EM clustering from conventional, non-relational data to social network data. In this paper, we introduce a generalized stochastic blockmodel (GSBM) that models not only the social relations but also the rating behavior. This model learns the mixed group membership assignments for both users and items in an SRN. GSBM can predict the future behavior of users, both the rating of items and creation of links to other users. We performed experiments on two real life datasets from Epinions.com and Flixster.com, demonstrating the accuracy of the proposed GSBM for rating prediction as well as link prediction.

Review recommendation: personalized prediction of the quality of online reviews

The problem of identifying high quality and helpful reviews automatically has attracted many attention recently. Current methods assume that the helpfulness of a review is independent from the readers of that review. However, we argue that the quality of a review may not be the same for different users. In this paper, we employ latent factor models to address this problem. We evaluate the proposed models using a real life database from Epinions.com. The experiments demonstrate that the latent factor models outperform the state-of-the-art approaches and confirms that the helpfulness of a review is indeed not the same for all users.

Modeling and Comparing the Influence of Neighbors on the Behavior of Users in Social and Similarity Networks

Social networks are becoming more and more popular with the advent of numerous online social networking services. In this paper, we explore social rating networks, which record not only social relations but also user ratings for items. We analyze and model the effects of social influence and correlational influence in such networks, based on influence coefficients that measure the degree of influence in a network. We distinguish two types of user behavior: adopting an item and adopting a rating value for that item. We propose models to analyze and measure the influence of neighbors on both item and rating adoption behavior of users. Our experiments demonstrate that social influence has a much stronger impact on user behavior than correlational influence. Social and correlational influence are global effects in the entire network. However, there are local differences, i.e. certain users have a stronger social influence than others. To model this effect, we introduce the novel concept of social authority of individual users. We also propose an objective way to evaluate the social authority measure by injecting it into a simple recommender system.