DTR4Rec: direct transition relationship for sequential recommendation

Abstract

Sequential recommendation aims at mining user interests through modeling sequential behaviors. Most existing sequential recommendation methods overlook the direct transition relationship among items, and only encode a user sequence as a whole, capturing the intention behind the sequence and predicting the next item with which the user might interact. However, in real-world scenarios, a small subset of items within a sequence may directly impact future interactions due to the direct transition relationship among items. To solve the above problem, in this paper, we propose a novel framework called Direct Transition Relationship for Recommendation (DTR4Rec). Specifically, we first construct a long-term direct transition matrix and a short-term co-occurrence matrix among items based on their occurrence patterns in the interaction data. The long-term direct transition matrix is constructed by counting the frequency of transitions from one item to another within a relatively long window. The short-term co-occurrence matrix is built by counting the frequency of co-occurrences of two items within a short window. We further utilize a learnable fusion approach to blend traditional sequence transition patterns with the direct transition relationship among items for predicting the next item. This integration is accomplished through a learnable fusion matrix. Additionally, in order to mitigate the data sparsity problem and enhance the generalization of the model, we propose a new paradigm for computing item similarity, which considers both collaborative filtering similarity and sequential similarity among items, then such similarity is utilized to substitute part of items in the sequence, thereby creating augmented data. We conduct extensive experiments on three real-world datasets, demonstrating that DTR4Rec outperforms state-of-the-art baselines for sequential recommendation.