Abstract: With the rapid development of Internet technology,drastic Internet information explosion makes information overload as an increasingly serious problem.Faced with the massive Internet information,users consume a lot of time to search for information or products,but the search solution is constrained.The recommender systems is hence proposed to address the problem of information overload.The recommender systems use users’ historical behaviors to speculate their needs,interests,etc.,and recommend the information and products users may be interested in.As an important type of recommendation approach,the memory-based collaborative filtering methods establish the rating prediction function based on neighbor information of the user or pro-duct.The essence of the function is to precisely measure the similarity between users or products.The traditional similarity mea-sures such as Pearson,Cosin and Spearman rank correlation coefficients,only take into account the linear relationship between users,while the heuristic similarities,such as the PIP measurement based on three special factors and its improved version,only depict the non-liner relationship between users.Indeed,in the recommender systems,it is neither the linear relation nor the non-linear relation is good for measuring the similarity between users.In order to describe the similarity among users more finely,this paper proposed a similarity measure index of the correlation level considering the extreme rating behaviors based on anonli-near function.By integrating this index with the traditional linear correlation coefficients,this paper constructed a novel similarity measure.Comparative experiments were conducted to test the practicability and validity of the proposed approach on Ml (100k) and Ml-latest-small datasets.The results demonstrate that the proposed method performs better judged by indicators of MAE and RMSE.

Key words: Collaborative filtering, Extremely rating behavior, Memory-based collaborative filtering, Recommender systems, Similarity