A Fuzzy Collaborative Filtering Recommendation Algorithm Based On Time Factor

Collaborative filtering is one of the most classic recommendation algorithms,which is widely used,but also has the problem of sparse data and easy to be influenced by user rating habits.In order to resolve this problem,the user attributes and item attributes are introduced into the collaborative filtering recommendation algorithm.But for anonymous users,or the recommendation based on IP,the acquisition of user information becomes difficult.The time attribute of the item and the user are easy to obtain,and more objectivity with respect to user ratings.Collaborative filtering based on time decay function can reflect the change of time,but it still can not overcome the shortcomings of the user rating habits.To improve the accuracy of the user-based collaborative filtering algorithm,an algorithm compromises time factor and rating factor is proposed in this article.We use the response time of user to describe the user's preference for the item,and it is an extension of the traditional criteria which singly depends on rating.By updating the ratings through the weight of response time,the adverse influence caused by user rating habits becomes smaller.Besides,this paper discusses the difference between the pre-filtering and post-filtering.The differences of users' rating time can reflect the similarity between users.We assume that there is certain relationship between the difference of rating time and the age level,and the degree of active potential.And our method can enrich the single measurement of the traditional score similarity criterion,and overcome the shortcomings of traditional collaborative filtering algorithm which ignoring user's attributes.Besides,this method can alleviate the pressure of getting the user real property to some extent.In order to describe the possible deviation of the rating time of users,the fuzzy set isintroduced into our algorithm.Based on the idea of the LCLC(Learning from Common Local Clusters)algorithm,which is an active learning method,the users are divided into RP(Reliable Positive),LP(Likely Positive),LN(Likely Negative),RN(Reliable Negative)four categories according to the response time.And a Gauss fuzzy model is constructed according to the division.Then,we calculate the distance between users based on this model to measure the similarity between users,and update score based user similarity.Our experiment is based on the MovieLens data set,and traditional collaborative filtering algorithm based on Pearson similarity is compared with the three algorithms respectively.The experimental values of MAE are lower than that of the traditional collaborative filtering algorithm based on the similarity of Pearson.The experimental results show that the introduction of time factor and fuzzy set can improve the efficiency of recommendation algorithm to some extent.