Hierarchical attentive transaction embedding with intra- and inter-transaction dependencies for next-item recommendation

A transaction-based recommender system (TBRS) aims to predict the next item by modeling dependencies in transactional data. Generally, two kinds of dependencies considered are intra-transaction dependence and inter-transaction dependence. Most existing TBRSs recommend next item by only modeling the intra-transaction dependence within the current transaction while ignoring inter-transaction dependence with recent transactions that may also affect the next item. However, as not all recent transactions are relevant to the current and next items, the relevant ones should be identified and prioritized. In this article, we propose a novel hierarchical attentive transaction embedding (HATE) model to tackle these issues. Specifically, a two-level attention mechanism integrates both item embedding and transaction embedding to build an attentive context representation that incorporates both intra- and inter-transaction dependencies. With the learned context representation, HATE then recommends the next item. Experimental evaluations on two real-world transaction datasets show that HATE significantly outperforms the state-of-the-art methods in terms of recommendation accuracy.