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A hybrid user-based collaborative filtering algorithm with topic model
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DOI：10.1007/s10489-021-02207-7.png)
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Abstract
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Currently available Collaborative Filtering(CF) algorithms often utilize user behavior data to generate recommendations. The similarity calculation between users is mostly based on the scores, without considering the explicit attributes of the users with profiles, as these are difficult to generate, or their evolution of preferences over time. This paper proposes a collaborative filtering algorithm named T-LDA (Time-decay Dirichlet Allocation), which is based on the topic model. In this method, we generate a hybrid score for similarity calculation with topic model. However, most topic models ignore the attribute of time order. In order to further improve the prediction accuracy, a time-decay function is introduced in topic model. The experimental results show that this algorithm has better performance than currently available algorithms on the MovieLens dataset, Netflix dataset and la.fm dataset.
Keywords:
Collaborative filtering
LDA
Topic model
Time decay
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