A Random Walk Based Model Incorporating Social Information for Recommendations
This addresses recommendation accuracy for users in systems with sparse data, but it is incremental as it builds on existing graph-based methods.
The paper tackled data sparsity and cold start problems in recommendation systems by proposing a hybrid collaborative filtering model based on a Markovian random walk that incorporates social information, showing improved performance on MovieLens and Epinions datasets, especially in cold start cases.
Collaborative filtering (CF) is one of the most popular approaches to build a recommendation system. In this paper, we propose a hybrid collaborative filtering model based on a Makovian random walk to address the data sparsity and cold start problems in recommendation systems. More precisely, we construct a directed graph whose nodes consist of items and users, together with item content, user profile and social network information. We incorporate user's ratings into edge settings in the graph model. The model provides personalized recommendations and predictions to individuals and groups. The proposed algorithms are evaluated on MovieLens and Epinions datasets. Experimental results show that the proposed methods perform well compared with other graph-based methods, especially in the cold start case.