Improving Accuracy of Recommender Systems using Social Network Information and Longitudinal Data

Merging Similarity and Trust Based Social Networks to Enhance the Accuracy of Trust-Aware Recommender Systems

In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the dev...

Hybrid Recommender Systems using Social Network Analysis

This study proposes novel hybrid social network analysis and collaborative filtering approach to enhance the performance of recommender systems. The proposed model selects subgroups of users in Internet community through social network analysis (SNA), and then performs clustering analysis using the information about subgroups. Finally, it makes recommendations using cluster-indexing CF based on...

Increasing the Accuracy of Recommender Systems Using the Combination of K-Means and Differential Evolution Algorithms

Recommender systems are the systems that try to make recommendations to each user based on performance, personal tastes, user behaviors, and the context that match their personal preferences and help them in the decision-making process. One of the most important subjects regarding these systems is to increase the system accuracy which means how much the recommendations are close to the user int...

Recommender Systems Using Social Network Analysis: Challenges and Future Trends

merging similarity and trust based social networks to enhance the accuracy of trust-aware recommender systems

in recent years, collaborative filtering (cf) methods are important and widely accepted techniques are available for recommender systems. one of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. however, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. with the dev...

Using past-prediction accuracy in recommender systems

This paper presents a new approach for memory-based collaborative filtering algorithms. In general, user-based rating prediction is a process in which each neighbor suggests a rating for the target item and the suggestions are combined by weighting the contribution of each neighbor. We present a new alternative that is independent of user rating scales and is based on what we call predictive pr...