Simulation of Bidirectional Personalized Recommendation Algorithm Based on Time Weight Factor
Bidirectional personalized recommendation requires obtaining interaction behavior data between users and items.However,during recommendation,only user interests and preferences are usually considered,ignoring the fact that user interests may change over time.In order to improve user satisfaction and accurately convert the demand and supply into website value,this paper put forward a bidirectional personalized recommendation algorithm based on time weight factor.Firstly,the relationship between users and adjacent users was associated,and their commodity pref-erences were analyzed at the same time.And then,the purchase similarity between users was calculated to obtain a set of similar recommended users.Secondly,the product preference weights of user groups were determined according to cosine similarity.Meanwhile,the purchase difference was calculated to obtain the standardized weight of user prefer-ence.Thirdly,the logistic function was introduced into the Pearson correlation similarity to determine the similarity between users.Finally,similar commodity information preferred by users was recommended.Thus,the bidirectional personalized recommendation was achieved.Experimental results prove that the proposed algorithm can accurately ob-tain the changes in user interests,and the average absolute error of recommendation is low.
Time weighting factorBidirectional personalized recommendationPreference weightSorting stageCosine similarity
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