Abstract
AbstractThe ranking algorithm in the recommender system aims at optimizing accuracy during training so that it pays too much attention to the relevance of the individual and ignores the mutual influence between the items in the list. In response to this problem, we propose dither, a re‐ranking model for the recommender system. We deploy the re‐ranking algorithm as an independent module after the ranking algorithm to achieve the function of decoupling from it. Our model formalizes the re‐ranking problem as a multi‐objective optimization problem. It re‐ranks the initial ranking list by balancing multiple indicators to generate an improved list and updates the list during frequent user interactions with the system. Through a case study on the MovieLens 100 K data set, the workflow, and effects of the dither model are demonstrated. In addition, the re‐ranking algorithm shows the performance advantages of our model over existing methods.
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