Interest-Behaviour Multiplicative Network for Resource-limited Recommendation

TL;DR

This paper introduces a novel interest-behavior multiplicative network designed for resource-limited recommendation scenarios, effectively modeling user preferences influenced by resource constraints using dynamic user-item interactions and mutual information.

Contribution

It proposes a new neural network framework that incorporates resource limitations and dynamic user-item interactions for improved recommendation accuracy.

Findings

The model outperforms existing methods on used car and Tmall datasets.

Resource limitations significantly impact user preferences and recommendation performance.

The approach effectively captures long-term dependencies in user behavior.

Abstract

Resource constraints, e.g. limited product inventory or financial strength, may affect consumers' choices or preferences in some recommendation tasks but are usually ignored in previous recommendation methods. In this paper, we aim to mine the cue of user preferences in resource-limited recommendation tasks, for which purpose we specifically build a large used car transaction dataset possessing resource-limitation characteristics. Accordingly, we propose an interest-behavior multiplicative network to predict the user's future interaction based on dynamic connections between users and items. To describe the user-item connection dynamically, mutually-recursive recurrent neural networks (MRRNNs) are introduced to capture interactive long-term dependencies, and meantime effective representations of users and items are obtained. To further take the resource limitation into consideration, a resource-limited branch is built to specifically explore the influence of resource variation on user preferences. Finally, mutual information is introduced to measure the similarity between the user action and fused features to predict future interaction, where the fused features come from both MRRNNs and resource-limited branches. We test the performance on the built used car transaction dataset as well as the Tmall dataset, and the experimental results verify the effectiveness of our framework.