CosRec: 2D Convolutional Neural Networks for Sequential Recommendation
This work addresses the challenge of improving recommendation accuracy for users by capturing intricate sequential patterns, representing an incremental advancement over existing sequential models.
The paper tackles the problem of modeling complex relationships in sequential recommendation by proposing CosRec, a 2D convolutional neural network that encodes item sequences into tensors and uses convolutional filters to capture pairwise and high-order interactions, achieving state-of-the-art performance on two public datasets.
Sequential patterns play an important role in building modern recommender systems. To this end, several recommender systems have been built on top of Markov Chains and Recurrent Models (among others). Although these sequential models have proven successful at a range of tasks, they still struggle to uncover complex relationships nested in user purchase histories. In this paper, we argue that modeling pairwise relationships directly leads to an efficient representation of sequential features and captures complex item correlations. Specifically, we propose a 2D convolutional network for sequential recommendation (CosRec). It encodes a sequence of items into a three-way tensor; learns local features using 2D convolutional filters; and aggregates high-order interactions in a feedforward manner. Quantitative results on two public datasets show that our method outperforms both conventional methods and recent sequence-based approaches, achieving state-of-the-art performance on various evaluation metrics.