Alleviating the sparsity problem of collaborative filtering using trust inferences (original) (raw)

Combining trust in collaborative filtering to mitigate data sparsity and cold-start problems

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.

Merging trust in collaborative filtering to alleviate data sparsity and cold start

Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Collaborative filtering is a widely accepted technique to generate recommendations based on the ratings of like-minded users. However, it suffers from several inherent issues such as data sparsity and cold start. To address these problems, we propose a novel method called ''Merge'' to incorporate social trust information (i.e., trusted neighbors explicitly specified by users) in providing recommendations. Specifically, ratings of a user's trusted neighbors are merged to complement and represent the preferences of the user and to find other users with similar preferences (i.e., similar users). In addition, the quality of merged ratings is measured by the confidence considering the number of ratings and the ratio of conflicts between positive and negative opinions. Further, the rating confidence is incorporated into the computation of user similarity. The prediction for a given item is generated by aggregating the ratings of similar users. Experimental results based on three real-world data sets demonstrate that our method outperforms other counterparts both in terms of accuracy and coverage.

Trust-aware Collaborative Filtering for Recommender Systems by Paolo Massa

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.

Trust as a Key to Improving Recommendation Systems

Lecture Notes in Computer Science, 2005

In this paper we propose a method that can be used to avoid the problem of sparsity in recommendation systems and thus to provide improved quality recommendations. The concept is based on the idea of using trust relationships to support the prediction of user preferences. We present the method as used in a centralized environment; we discuss its efficiency and compare its performance with other existing approaches. Finally we give a brief outline of the potential application of this approach to a decentralized environment.

Collaborative filtering-based recommender systems by effective trust

International Journal of Data Science and Analytics

Collaborative filtering (CF) is one of the most well-known and commonly used techniques to build recommender systems and generate recommendations. However, it suffers from several inherent issues such as data sparsity and cold start. This paper tends to describe the steps based on which the ratings of an active users trusted neighbors are combined to complement and represent the preferences to 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 established to represent the preferences of the active user. In the next step, 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, to combine more similar users to generate a prediction for this target item. Experimental results demonstrate that our method outperforms other counterparts both in terms of accuracy and in terms of coverage.

Trust Distrust Enhanced Recommendations Using an Effective Similarity Measure

Mining Intelligence and Knowledge Exploration, 2017

Collaborative filtering (CF), the most prevalent technique in the area of recommender systems (RSs), provides suggestions to users based on the tastes of their similar users. However, the new user and sparsity problems, degrade its efficiency of recommendations. Trust can enhance the recommendation quality by mimicking social dictum "friend of a friend will be a friend". However distrust, the another face of coin is yet to be explored along with trust in the area of RSs. Our work in this paper is an attempt toward introducing trust-distrust enhanced recommendations based on the novel similarity measure that combines user ratings and trust values for generating more quality recommendations. Our approach also exploits distrust links among users and analyses their propagation effects. Further, distrust values are also used for filtering more distrust-worthy neighbours from the neighbourhood set. Our experimental results show that our proposed approaches outperform the traditional CF and existing trust enhanced approaches in terms of various performance measures.

Trust-Based Recommendation: an Empirical Analysis

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.