ネットワーク構造からのRecommendation手法

Link Prediction Approach to Collaborative Filtering

Zan Huang, Xin Li, Hsinchun Chen

International Conference on Digital Libraries, Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries 2005

Abstract:
… the recommendation quality of collaborative filtering approaches is greatly limited by the data sparsity problem. To alleviate this problem we have previously proposed graph-based algorithms to explore transitive user-item associations. In this paper, we extend the idea of analyzing user-item interactions as graphs and employ link prediction approaches proposed in the recent network modeling literature for making collaborative filtering recommendations. We have adapted a wide range of linkage measures for making recommendations. Our preliminary experimental results based on a book recommendation dataset show that some of these measures achieved significantly better performance than standard collaborative filtering algorithms.

• 参考文献[3] Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering

• user-itemの2部グラフについてまだリンクのないノードの組み合わせをneighbor-baseの基準4つとpath-baseの基準2つの計6種類の手法で数値計算．
• userに値の高い組のitemを推薦する．
• user-baseやitem-baseの強調フィルタリングよりパフォーマンスのよい手法あり．