Collaborative Filtering Recommendation Based On Tag And Time Information

Purpose — Recommendation system is an effective way to solve the problem of information overload,which can help users quickly find resources that may be interest.However,there are still some problems in the traditional recommendation system.Such as,data sparseness of scoring matrix,transfer of user interest preferences and cold start.Tag information has both resource description information and user interest preference information,its related applications are more and more widely used.It can be used not only for the recommendation of conventional resources such as movies and books,but also for the recommendation of social networking.It brings a new turn for traditional recommendation systems.However,the use of tag information also brings new problems,such as how to extract valid information from tags.For this reason,tag and time information are integrated into the traditional collaborative filtering algorithm.Strengthen the relationship between users and resources in the form of tags,strengthen the relationship between recommendation and user interest preferences in the form of time.Design/methodology/approach — Firstly,according to the information of resource description and user interest preference reflected by tags,tags are classified into standardized tags and social tags.The formula for calculating tag similarity is improved.Secondly,tag information is introduced into the basis of traditional collaborative filtering.The improved tag similarity calculation formula is used to predict the first score value,and the score prediction value is filled in the original score matrix.The user's nearest neighbor recommendation is made on the filled matrix to get the final recommendation result.Finally,aiming at the user interest transfer problem,the collaborative filtering algorithm which fuses tag information is further improved.The time weighting factor is added to the user's rating and tagging behavior to reduce the influence weight of the longer time interval rating and tagging behavior in the similarity calculation,so that the recommendation results can be more in line with the user's interest changes.Findings — Firstly,the socialized tags are preprocessed.According to the frequency of usage,the socialized tags with real distinguishing value are selected for subsequent calculation.The appropriate values of the two kinds of tags are determined by comparing the values of adjustment factors,which makes the results based on tag similarity more accurate.Secondly,tag and collaborative filtering recommendation algorithm are fused.The feasibility of the improved collaborative filtering algorithm based on tag information is verified by comparing with traditional collaborative filtering algorithm and tag-based recommendation algorithm.Finally,introducing time information to compare the values of different forgetting speeds to determine the appropriate values,and the feasibility of the improved algorithm based on time is verified by comparing with the tag-based collaborative filtering algorithm.Research limitations/implications — Social tags with differentiating value are selected only according to the frequency of use.However,in view of the fact that social tags are used by users with their free will and without the restriction of standard vocabulary and hierarchical structure,there may be polysemy and synonyms,which may lead to the incomprehension of the implicit information in social tags.In addition,the efficiency and accuracy of the algorithm need to be further improved.Practical implications — Through the collaborative filtering algorithm which fuses tag information,use tags to strengthen the links between users and resources,improve the accuracy of recommendation.By introducing time weighting factor to grasp the transfer of user's interest,provide more effective recommendation for users,enhance the cohesion between users and the system.Originality/value — The tags are divided into standardized tags and social tags,and an improved formula based on similarity calculation of tag information is proposed according to the different information reflected by the two tags.The traditional collaborative filtering method is improved by integrating tag information and time information,which makes the recommendation results more realistic and can alleviate the problems of data sparsity and user interest transfer to a certain extent.