User Diverse Preference Modeling by Multimodal Attentive Metric Learning
This addresses the problem of inaccurate recommendations for users with diverse item preferences in recommender systems, though it is an incremental improvement over existing metric learning and attention methods.
The paper tackles the problem of representing a user's fixed preference vector in recommender systems, which fails to capture diverse preferences across items, by proposing a Multimodal Attentive Metric Learning (MAML) method that uses attention networks on item multimodal features to model varying preferences, resulting in substantial outperformance over state-of-the-art baselines in experiments on large-scale datasets.
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.