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Recommender systems are efficient tools for online applications; these systems exploit historical user ratings on items to make recommendations of items to users. This paper aims to enhance dynamic collaborative filtering on recommender systems under volatile conditions in which both users' preferences and item properties dynamically change over time. Moreover, existing collaborative filtering models mainly rely on solving data sparsity by adding side information to improve performance. We propose a model to capture the user preference dynamics in the rating matrix by using a joint decomposition method to extract user latent transition patterns and combine latent factors together with the associated topic evolution of review texts by using topic modeling based on the dynamic environment. We evaluate the accuracy on real datasets, and the experimental results show that the model leads to a significant improvement compared with the state-of-the-art dynamic CF models.

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