基于贝叶斯方法与聚类的上下文感知推荐

Context-aware Recommendation Based on Bayes Method and Clustering

针对如何将上下文信息融入推荐过程以提高推荐准确度问题,提出基于贝叶斯方法与聚类的新的上下文建模方法.不同于现有上下文建模方法将所有上下文看成同等重要,该方法将各上下文分别以不同的影响权重融入用户兴趣模型中.首先采用特征聚类方法对项目进行聚类,然后利用贝叶斯公式计算单个上下文条件下用户喜欢某类项目的概率,再通过复合概率公式求得多个上下文条件下用户喜欢一类项目的联合概率.最后根据喜欢同一类项目的用户之间相似度更高这一认识,将所求的联合概率融入到传统协同过滤推荐算法中以提高推荐准确度.该文采用真实电影评分数据集进行对比实验,得出的结果验证了提出方法的有效性和可靠性.

To further improve the accuracy of recommendations, a new contextual modeling algorithm based on bayes method and clustering is proposed, which incorporates contextual information in recommender system. Different from the existing contextual modeling algorithm seeing all contexts as equally important, this algorithm incorporates the context respectively in different influence weight into the user interest model. This paper first cluster items using feature clustering method, and then use the bayesian formula to calculate the probability of a user liking items in a particular category with the single context conditions, so the joint probability of the user liking this kind of items with multiple context conditions is obtained. Finally, because the similarity between users those like items in same category as well should be higher,the joint probability above is incorporated into traditional collaborative filtering algorithm to improve the user similarity computing, which is beneficial to the improvement of rating prediction accuracy. Results of quantities of comparison experiments with a real world dataset demonstrate its validity and reliability.