A Complex Network Approach for Collaborative Recommendation
This work addresses a bottleneck in recommendation systems for users in sparse data scenarios, but it is incremental as it builds on existing neighborhood-based methods.
The paper tackles the problem of finding effective neighbors in sparse datasets for collaborative filtering by proposing a two-phase approach that uses structural similarity from user-user and item-item networks. The result is that the proposed measures outperform existing state-of-the-art similarity measures across various evaluation metrics on real data.
Collaborative filtering (CF) is the most widely used and successful approach for personalized service recommendations. Among the collaborative recommendation approaches, neighborhood based approaches enjoy a huge amount of popularity, due to their simplicity, justifiability, efficiency and stability. Neighborhood based collaborative filtering approach finds K nearest neighbors to an active user or K most similar rated items to the target item for recommendation. Traditional similarity measures use ratings of co-rated items to find similarity between a pair of users. Therefore, traditional similarity measures cannot compute effective neighbors in sparse dataset. In this paper, we propose a two-phase approach, which generates user-user and item-item networks using traditional similarity measures in the first phase. In the second phase, two hybrid approaches HB1, HB2, which utilize structural similarity of both the network for finding K nearest neighbors and K most similar items to a target items are introduced. To show effectiveness of the measures, we compared performances of neighborhood based CFs using state-of-the-art similarity measures with our proposed structural similarity measures based CFs. Recommendation results on a set of real data show that proposed measures based CFs outperform existing measures based CFs in various evaluation metrics.