An interpretable sequential three-way recommendation based on collaborative topic regression

Owing to the imbalance of observed data and user's preference, it is necessary and meaningful to think over the multilevel characteristics of recommendation information (RI) during the recommendation process. At the same time, to better summarize user's preference and enhance user's beliefs, the interpretability of recommendation results also become more and more important in recommender system (RS). In view of the multilevel characteristics of RI and the interpretability of recommendation results, this paper proposes a novel interpretable sequential three-way recommendation strategy, namely, CTR-based cost-sensitive sequential three-way recommendation (CTR-CS3WR). First, in order to construct the interpretable granular features and multilevel information, we introduce collaborative topic regression (CTR) and design three novel granulation methods: PMF-based, LDA-based and CTR-based granulation method. Then, with the consideration of decision cost and time cost, a sequential three-way recommendation strategy is proposed to realize the multilevel recommendation. Finally, extensive experiments on two CiteUlike datasets verify the effectiveness of our proposed granulation methods and recommendation strategy.