Robust Recommendation Algorithm Based on Core User Extraction with User Trust and Similarity

Recommendation systems can help people make decisions conveniently. However, few studies consider the effect of removing irrelevant noise users and retaining a small number of core users to make recommendations. A new method of core user extraction is proposed based on trust relationship and interest similarity. First, all users trust and interest similarity between pairs are calculated and sorted, then according to the frequency and position weight users travel in the nearest neighbor in the list of two kinds of strategies for the selection of candidate core collection of users. Finally, according to the user's ability the core users are sieved out. Experimental results show that the core user recommendation effectiveness, and verify that the core of user 20% can reach more than recommended accuracy of 90%, and through the use of core user recommendation the negative effects caused by the attacks on the recommendation system can be resisted.