Multi-intent Compatible Transformer Network for Recommendation

The core of recommendation systems is to explore users' preferences from users' historical records and accordingly recommend items to meet users' interests. Previous works explore interaction graph to capture multi-order collaborative signals and derive high-quality representations of users and items, which effectively alleviates the interaction sparsity issue. Recent works extend the scope with a fine-grained perspective and achieve a great success in modeling users' diverse intents. Although these works distinguish intents, they ignore the hidden correlation among users' intents resulting in suboptimal recommendation performance. We argue that a user's interest is made up of multiple intents and these intents are compatible on the interest composition of the user. To this point, we propose multi-intent compatible transformer network (MCTN) to explore the correlation between intents on modeling users' interests for recommendation. Users and items are embedded into multiple intent spaces through disentangled graph convolution network to disentangle users' intents. MCTN conducts embedding propagation in each intent space to capture the multi-order collaborative signals on the specific intent. We introduce a transformer network to capture the dependence between intents and derive multi-intent compatible embeddings of users and items for recommendation. The experiments achieves state-of-the-art performance, which demonstrates the effectiveness of the proposed MCTN on modeling multi-intent compatibility into embeddings.