Recommendation System based on User Trust and Ratings

Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight. An empirical evaluation on Epinions.com dataset shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings.

2012

The use of trust in recommender systems has been shown to improve the accuracy of rating predictions, especially in the case where a user's rating significantly differs from the average. Different techniques have been used to incorporate trust into recommender systems, each showing encouraging results. However, the lack of trust information available in public datasets has limited the empirical analysis of these techniques and trust-based recommendation in general, with most analysis limited a single dataset. In this paper, we provide a more complete empirical analysis of trust-based recommendation. By making use of a method that infers trust between users in a social graph, we are able to apply trust-based recommendation techniques to three separate datasets. From this, we measure the overall accuracy of each technique in terms of the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) as well as measuring the prediction coverage of each technique. We thus provide a comparison and analysis of each technique on all three datasets.

2014 Iranian Conference on Intelligent Systems (ICIS), 2014

Collaborative Filtering systems consider users' social environment to predict what each user may like to visit in a social network i.e. they collect and analyze a large amount of information on users' behavior, activities or preferences and then predict or make suggestions to users. These systems use ranks or tags each user assign to different resources to make predictions. Lately, social tagging systems, in which users can insert new contents, tag, organize, share and search for contents, are becoming more popular. These social tagging systems have a lot of valuable information, but the data expansion in them is very fast and this has led to the need for recommender systems that will predict what each user may like or need make these suggestions to them. One of the problems in these systems is: "how much can we rely on the similar users, are they trustworthy?". In this article we use trust metric, which we conclude from users' tagging behavior, beside similarities to give suggestions. Results show considering trust in a collaborative system can lead to better performance in generating suggestions.

2005

We rely on the information from our trustworthy acquaintances to help us take even trivial decisions in our lives. Recommender Systems use the opinions of members of a community to help individuals in that community identify the information most likely to be interesting to them or relevant to their needs. These systems use the similarity between the user and recommenders or between the items to form recommendation list for the user. They do not take into consideration the social trust network between the entities in the society to ensure that the user can trust the recommendations received from the system. The paper proposes a model where a trust network exists between the peer agents and the personalized recommendations are generated on the basis of these trust relationships. The recommenders personalize recommendations by suggesting only those movies to user that matches its taste. Also, the social recommendation process is inherently fuzzy and uncertain. In the society, the information spreads through word-of-mouth and it is not possible to fully trust this information. There is uncertainty in the validity of such information. Again, when a product is recommended, it is suggested with linguistic quantifiers such as very good, more or less good, ordinary, and so on. Thus, uncertainty and fuzziness is inherent in the recommendation process. We have used Intuitionistic Fuzzy Sets to model such uncertainty and fuzziness in the recommendation process.

Recommendation systems are used to provide high quality recommendations to the users from large amount of choices. Accurate and quality recommendation is necessary in E-commerce sites. One of the most popular technique to implement a recommendation system is Collaborative Filtering (CF) [1]. It tries to find users similar to an active user and recommend him/her the items liked by these similar users. By the advent of social networks, social network based recommendation arised. In this technique a social network is constructed among the users and recommends users based on the ratings of the users who have direct or indirect social relation with the user. One of the most important benefit of social network approach is that it reduces cold start problem [1] [9].

Recommendation Services (RS) are an essential part of online marketing campaigns. They make it possible to automatically suggest advertisements and promotions that fit the interests of individual users. Social networking websites, and theWeb 2.0 in general, offer a collaborative online platform where users socialize, interact and discuss topics of interest with each other. These websites have created an abundance of information about users and their interests. The computational challenge however is to analyze and filter this information in order to generate useful recommendations for each user. Collaborative Filtering (CF) is a recommendation service technique that collects information from a user’s preferences and from trusted peer users in order to infer a new targeted suggestion. CF and its variants have been studied extensively in the literature on online recommending, marketing and advertising systems. However, most of the work done was based on Web 1.0, where all the information necessary for the computations is assumed to always be completely available. By contrast, in the distributed environment of Web 2.0, such as in current social networks, the required information may be either incomplete or scattered over different sources. In this paper, we propose the Multi-Collaborative Filtering Trust Network algorithm, an improved version of the CF algorithm designed to work on the Web 2.0 platform. Our simulation experiments show that the new algorithm yields a clear improvement in prediction accuracy compared to the original CF algorithm.

In traditional recommendation systems, the challenging issues in adopting similarity-based approaches are sparsity, cold-start users and trustworthiness. We present a new paradigm of recommendation system which can utilize information from social networks including user preferences, item`s general acceptance, and influence from friends. A probabilistic model, particularly for e-commerce networks, is developed in this paper to make personalized recommendations from such information. Our analysis reveals that similar friends have a tendency to select the same items and give similar ratings. We propose a trust-driven recommendation method known as HybridTrustWalker. First, a matrix factorization method is utilized to assess the degree of trust between users. Next, an extended random walk algorithm is proposed to obtain recommendation results. Experimental results show that our proposed system improves the prediction accuracy of recommendation systems, remedying the issues inherent in collaborative filtering to lower the user`s search effort by listing items of highest utility.

Proceedings of the 8th International Conference on Web Information Systems and Technologies, 2012

Recommender systems (RS) aim to predict items that users would appreciate, over a list of items. In evaluation of recommender systems, two issues can be defined: accuracy of prediction which implies the satisfaction of the user, coverage which implies the percentage of satisfied users. Collaborative filtering (CF) is the master approach in this domain, but still has some weaknesses especially about coverage. Trust-aware approach is today another promising approach in RS within social environments, whose prediction exceeds the quality of (CF). In this paper we propose several strategies to hybridize those both approaches in order to improve prediction quality, in the term of accuracy and coverage.

2014

Collaborative filtering (CF) is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it suffers from several inherent deficiencies such as data sparsity and cold start. To better show user preferences for the cold users additional information (e.g., trust) is often applied. We describe the stages based on which the ratings of an active user's trusted neighbors are incorporated to complement and represent the preferences of the active user. First, by discriminating between different users, we calculate the significance of each user to make recommendations. Then the trusted neighbors of the active user are identified and aggregated. Hence, a new rating profile can be formed to represent the preferences of the active user. In the next stage, similar users probed based on the new rating profile. Finally, recommendations are generated in the same way as the conventional CF with the difference that if a similar neighbor had not rated the target item, we will predict the value of the target item for this similar neighbor by using the ratings of her directly trusted neighbors and applying MoleTrust algorithm, so as to incorporate more similar users to generate prediction for this target item. Experimental results demonstrate that our method outperforms other counterparts both in terms of accuracy and coverage.

Digital Information Management (ICDIM), …, 2010

Recommender systems have been proposed to exploit the potential of social network by filtering the information and offer recommendations to a user that he is predicted to like. Collaborative Filtering (CF) is believed to be a suitable underlying technique for recommender systems on social network, because CF gathers tastes of similar users; and social network provides such a collaborative social environment. One inherent challenge however for running CF on social network is quantitative estimation of trust between friends. Although many researchers investigated trust metrics and models in social network, none so far has efficiently integrated them in a CF algorithm. The contribution of this paper is a framework of collaborative filtering on social network, and a novel approach in measuring trust factors by data-mining over a survey dataset provided by The Facebook Project. The quantitatively estimated trust factors can be used as input parameters in the CF algorithm. Facebook is taken as a case study here to illustrate the concepts.