Social Trust as a solution to address sparsity-inherent problems of Recommender systems
This work addresses sparsity and cold start issues in recommender systems, particularly benefiting new users, but it is incremental as it builds on existing trust-based approaches.
The authors tackled the sparsity and cold start problems in recommender systems by proposing a scheme that calculates hypothetical trust between users based on existing ratings, demonstrating that incorporating social networking principles improves performance compared to systems not using trust.
Trust has been explored by many researchers in the past as a successful solution for assisting recommender systems. Even though the approach of using a web-of-trust scheme for assisting the recommendation production is well adopted, issues like the sparsity problem have not been explored adequately so far with regard to this. In this work we are proposing and testing a scheme that uses the existing ratings of users to calculate the hypothetical trust that might exist between them. The purpose is to demonstrate how some basic social networking when applied to an existing system can help in alleviating problems of traditional recommender system schemes. Interestingly, such schemes are also alleviating the cold start problem from which mainly new users are suffering. In order to show how good the system is in that respect, we measure the performance at various times as the system evolves and we also contrast the solution with existing approaches. Finally, we present the results which justify that such schemes undoubtedly work better than a system that makes no use of trust at all.