# Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics
###### Abstract  
The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art. collaborative recommendation; TF-IDF method; user characteristics; fuzzy membership function; weighted fusion +
Footnote †: journal: Medical  
has been widely used, there are still some problems such as data sparsity, cold start, and information expiration, etc [11]. To solve the problems above, a series of improvements based on the traditional collaborative filtering algorithm were made and achieved some success. For example, [PERSON] et al. [12] alleviated the sparsity and cold start problems of the matrix using the film type label and director genre. [PERSON] et al. [13] used matrix decomposition technology to reduce the dimension of the matrix and improve the accuracy of the recommendation result. [PERSON] and [PERSON] [14] proposed a dynamic time drift model considering the influence of user interest changes on similarity in different time periods. [PERSON] et al. [15] proposed a collaborative filtering algorithm combining the KNN model and XGBost model. [PERSON] et al. [16] presented a new effective model-based trust collaborative filtering to improve the quality of recommendation. In addition, there are some collaborative filtering algorithms based on clustering [17], neural networks [18], and various probability models [19]. The above studies optimized the recommendation model to a certain extent and improved the accuracy of the recommendation results, but there are still some problems to be further studied. For example, most of the existing collaborative filtering algorithms only consider the rating information among users, but ignore the user characteristics and the impact of popular items on user similarity, which leads to poor recommendation results. To further improve the accuracy of recommendation, a collaborative filtering algorithm based on the TF-IDF method and user characteristics is proposed in this paper. In the proposed method, both the rating information and the characteristics of the users are fully considered. The contribution of this paper can be summarized as follows: (1) Based on the rating data, the TF-IDF method is used to calculate the user similarity matrix to punish the impact of popular items on user similarity, and to improve the ability of mining unpopular items. (2) The user characteristics are fully considered in the proposed method, which is used to calculate user similarity based on a fuzzy membership function, to deal with the cold start problem by combining different dimension characteristics information of users. (3) An adaptive weighted algorithm is presented to fuse the two kinds of similarities of users obtained on the above two steps, to form a new user comprehensive similarity for recommendation algorithm. At last, experiments are carried out on real data sets to evaluate the accuracy of the proposed recommendation model. Experimental results show that the proposed algorithm is better than the state-of-the-art algorithms in accuracy.