A Deep Recommendation Model Considering the Impact of Time and Individual Diversity

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Abstract
Collaborative filtering (CF) technology has been widely used in recommendation systems. Usually, the latent factor model (LFM) is used as the basis for implementing CF recommendation in deep learning systems. This study differs from previous studies in two respects. First, for different target items, the user embedding vector should be dynamically adjusted according to the content of the target item. Therefore, we have added an attention mechanism to dynamically adjust the user’s embedding vector. However, people’s preferences usually change over time. Therefore, based on the above attention model, this study considers two time-decay functions to emphasize the user’s recent preferences. The first decay function considers the situation where the recent rating is more important than the long ago rating. The second time-decay function considers the situation, whereby users generally prefer movies that have been released recently rather than movies that have been released a long time ago. By combining these two time-decay functions with the attention model, we propose a time-decay adaptive latent factor model (TDADLFM) model for item score prediction. This study applies this model to a dataset integrating Movielens-10M and HetRec2011 and proves that all three new considerations can improve recommendation performance.

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