A Fast Recommendation Algorithm for Social Tagging Systems : A Delicious Case
This work addresses efficiency issues for users of social tagging systems like Delicious, though it is incremental as it builds on existing collaborative filtering methods.
The paper tackled the high computational cost of traditional collaborative filtering in social tagging systems by proposing a fast recommendation algorithm using coarse clustering, which reduced processing time by over 90% while improving accuracy.
The tripartite graph is one of the commonest topological structures in social tagging systems such as Delicious, which has three types of nodes (i.e., users, URLs and tags). Traditional recommender systems developed based on collaborative filtering for the social tagging systems bring very high demands on CPU time cost. In this paper, to overcome this drawback, we propose a novel approach that extracts non-overlapping user clusters and corresponding overlapping item clusters simultaneously through coarse clustering to accelerate the user-based collaborative filtering and develop a fast recommendation algorithm for the social tagging systems. The experimental results show that the proposed approach is able to dramatically reduce the processing time cost greater than $90\%$ and relatively enhance the accuracy in comparison with the ordinary user-based collaborative filtering algorithm.