Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation

TL;DR

This paper introduces HATE, a hierarchical attentive model that effectively captures intra- and inter-transaction dependencies for improved next-item recommendation accuracy.

Contribution

The paper presents a novel hierarchical attentive transaction embedding model that selectively models relevant recent transactions for better recommendation performance.

Findings

HATE outperforms existing methods on real-world datasets.

The hierarchical attention mechanism effectively captures dependencies.

Model achieves significant accuracy improvements.

Abstract

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 dependency and inter-transaction dependency. Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction while ignoring inter-transaction dependency 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 paper, 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 intraand 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-ofthe-art methods in terms of recommendation accuracy.