结合专家信任的协同过滤推荐算法研究

针对协同过滤推荐算法存在的邻居用户的相似度计算不精确、冷启动等问题，提出一种结合专家信任的协同过滤推荐算法，该算法从相似度、专家信任度和缓解冷启动3个方面进行研究。在相似度方面，将公共评分项蕴含的隐偏好以及项目冷门因子加入到相似度的计算公式中，实现对相似度的改进;在专家信任度方面，提出时间跨度因子，将专家的阅历值考虑到信任度计算中，实现对专家信任度的改进;在冷启动方面，通过联合专家用户和属性相似用户共同为目标用户产生推荐，有效地缓解了冷启动问题。应用MoviesLens数据集进行验证，实验结果表明，改进算法的平均绝对误差、准确率均优于传统算法。

Collaborative Filtering Recommendation Algorithm Combined with Expert Trust

Aiming at the problems of the collaborative filtering recommendation algorithm, such as inaccurate similarity calculation of neighbor users and cold start, a collaborative filtering recommendation algorithm combined with expert trust was proposed. The algorithm was studied from 3 aspects of similarity, expert trust and cold start alleviation. In the aspect of similarity, the implicit preference contained in the public score item and the unpopular factor of the item are added into the formula of similarity to improve the similarity. In the aspect of expert trust, the time span factor is proposed, and the experience value of experts is taken into account in the calculation of trust, so as to improve expert trust. In the aspect of cold start, the problem of cold start can be effectively alleviated by combining expert users and users with similar attributes to generate recommendations for target users. The MoviesLens data set is used to verify that the improved algorithm has better average absolute error and accuracy than the traditional algorithm.