Graph-Based Hybrid Recommendation Using Random Walk and Topic Modeling

In this paper, we propose a graph-based method for hybrid recommendation. Unlike a simple linear combination of several factors, our method integrates user-based, item-based and content-based techniques more fully. The interaction among different factors are not done once, but by iterative updates. The graph model is composed of target user's similar-minded neighbors, candidate items, target user's historical items and the topics extracted from items' contents using topic modeling. By constructing the concise graph, we filter out irrelevant noise and only retain useful information which is highly related to the target user. Top-N recommendation list is finally generated by using personalized random walk. We conduct a series of experiments on two datasets: movielen and lastfm. Evaluation results show that our proposed approach achieves good quality and outperforms existing recommendation methods in terms of accuracy, coverage and novelty.