Abstract: 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.

Key words: collaborative filtering, similarity, implicit preference, project unpopular factor, expert trust, time span