KEYWORDS: Detection and tracking algorithms, Lithium, Electronic filtering, Operating systems, Mechanics, Local area networks, Internet, Integration, Information technology, Information science
Collaborative filtering-based algorithms are widely used to make recommendations without analyzing the contents. Time effect can be seen everywhere in our daily life. User interests will change over time, so we use the time-decay function to integrate the user-item rating matrix and adjust it by different time-decay factors to optimize the model. And we conducted experiments using the improved algorithm on the movie evaluation dataset movielens-1 m. The results show that the algorithm is able to improve the accuracy and coverage of recommendations under specific time factors, and also can partly improve the recommendation efficiency.
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