基于信任网络随机游走模型的协同过滤推荐

Collaborative Filtering Recommendation Based on Random Walk Model in Trust Network

协同过滤是目前应用最广泛和最成功的推荐技术之一。然而,目前该技术的发展面临着严重的冷启动和稀疏性问题,降低了其推荐质量,因此提出了一种基于信任网络随机游走模型的协同过滤推荐方法。该方法融合了基于信任和项目的协同过滤推荐方法,并引入了信任因子作为引导推荐的重要因素。随机游走模型不仅考虑了信任用户对目标项目的评分,也考虑了他们对与目标项目相似的项目的评分。随着随机游走深度的增加,以相似项目的评分信息来代替目标项目的评分信息的概率也逐渐增大。在Epinions真实数据集上的验证结果表明,该方法在推荐评价指标上比其他算法具有更好的推荐结果。

Collaborative filtering is one of the most widely used techniques for recommendation system which has been successfully applied in many applications. However,it suffers from serious problems of cold start and data sparsity. In addition,these methods can not indicate their confidence in recommendation. In this paper, we improved the random walk model combining trust-based and item-based collaborative filtering method for recommendation. The trust factor is introduced as an important factor of guiding recommendations. The random walk model considers not only the ratings of target item, but also those of the similar items. The probability of using the rating of a the similar item instead of a rating for the target item increases with increasing length of walk. Our framework contains both trust-based and item- based collaborative filtering recommendations as special cases. The empirical analysis on the Epinions dataset demon- strates that our method can provide better recommendation result in terms of evaluation metrics than other algorithms.