面向社会化电子商务的信任感知协同过滤推荐方法

为提高社会化电子商务推荐服务的精确度和有效性,综合考虑交易评价得分、交易次数、交易金额、直接信任、推荐信任等影响社会化电子商务用户信任关系的因素,设计了一种信任感知协同过滤推荐方法.该方法利用置信因子计算用户间的信任关系,采用余弦相关度法计算用户间的相似度,引入调和因子综合用户信任关系和用户相似度对商品预测评分的影响,以平均绝对误差(MAE)、评分覆盖率和用户覆盖率作为评价指标.实验结果表明,与标准协同过滤推荐方法、基于规范矩阵因式分解的推荐方法相比,信任感知协同过滤推荐方法将MAE降低到0.162,并将评分覆盖率和用户覆盖率分别提高到77%和80%,能够解决交易评价较少商品的推荐问题.

Trust-aware collaborative filtering recommendation method for social E-commerce

For improving the accuracy and validity of social E-commerce recommendation services, a trust-aware collaborative filtering recommendation method was proposed with considering the factors that influence the trust relationship of users in social E-commerce, such as transaction evaluation score, transaction frequency, transaction amount, direct trust and recommended reputation. The belief factor was introduced to compute the trust relationship of social E-commerce users, the cosine similarity method was used to calculate the similarity of the users, the harmonic factor was used to synthesize the influence of the trust relationship and similarity on the users, the Mean Absolute Error (MAE), rating coverage and user coverage were used as the evaluation indexes. The experimental results show that the accuracy of the trust-aware collaborative filtering method is superior to the traditional collaborative filtering method and the regularized matrix factorization based collaborative filtering recommendation method in that the MAE reduces to 0.162, and the rating coverage and user coverage rise to 77% and 80% respectively. This proves that the trust-aware collaborative filtering method can solve the problem of recommending the commodities with less transaction evaluation.