User Diverse Preference Modeling by Multimodal Attentive Metric Learning

TL;DR

This paper introduces a Multimodal Attentive Metric Learning approach that models user preferences more accurately by considering item-specific features and attention mechanisms, outperforming traditional methods in large-scale recommendation tasks.

Contribution

The paper proposes a novel attention-based metric learning framework that captures user preference diversity across items using multimodal features and improves recommendation accuracy.

Findings

Significantly outperforms state-of-the-art baselines on large-scale datasets.

Effectively models user preference diversity with attention mechanisms.

Addresses limitations of dot product similarity in traditional models.

Abstract

Most existing recommender systems represent a user's preference with a feature vector, which is assumed to be fixed when predicting this user's preferences for different items. However, the same vector cannot accurately capture a user's varying preferences on all items, especially when considering the diverse characteristics of various items. To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items. In particular, for each user-item pair, we propose an attention neural network, which exploits the item's multimodal features to estimate the user's special attention to different aspects of this item. The obtained attention is then integrated into a metric-based learning method to predict the user preference on this item. The advantage of metric learning is that it can naturally overcome the problem of dot product similarity, which is adopted by matrix factorization (MF) based recommendation models but does not satisfy the triangle inequality property. In addition, it is worth mentioning that the attention mechanism cannot only help model user's diverse preferences towards different items, but also overcome the geometrically restrictive problem caused by collaborative metric learning. Extensive experiments on large-scale real-world datasets show that our model can substantially outperform the state-of-the-art baselines, demonstrating the potential of modeling user diverse preference for recommendation.