Academic Journal of Computing & Information Science, 2020, 3(3); doi: 10.25236/AJCIS.2020.030308.
Wenjing Yan1, *
1 School of Information engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
*Corresponding author e-mail: email@example.com
In this paper, when using Apriori algorithm to mine association rules, it often appears that the antecedent of the rule is the same, but the recommended consequent is more than one. In order to solve the problem, this paper proposes a weighted association rule recommendation algorithm which combines time and rating information. First of all, we describe the user's personalization and interest from the perspective of time and rating. From the point of view of time, the user's viewing data is sorted according to time to generate orderly user number, movie type and corresponding rating data. Time weight is introduced to add time weight to the user's rating on the sequence, so as to describe the importance of time. From the perspective of scoring, this paper filters out the low scoring and studies whether the scoring will affect the recommendation effect. Then, time and rating weighting are introduced to get the weighted value of association rules. Then, the weights and association rules are combined to select association rules whose confidence and support meet the threshold to generate strong association rules. Finally, strong association rules are used to recommend users, so as to improve the efficiency of the recommendation system.
time, rating, weighted association rules, recommendation system
Wenjing Yan. Research on Association Rule Recommender System Combining Time and Rating Information. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 3: 70-82. https://doi.org/10.25236/AJCIS.2020.030308.
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