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Most existing recommender systems represent a user's preference with afeature vector, which is assumed to be fixed when predicting this user'spreferences for different items. However, the same vector cannot accuratelycapture a user's varying preferences on all items, especially when consideringthe diverse characteristics of various items. To tackle this problem, in thispaper, we propose a novel Multimodal Attentive Metric Learning (MAML) method tomodel user diverse preferences for various items. In particular, for eachuser-item pair, we propose an attention neural network, which exploits theitem's multimodal features to estimate the user's special attention todifferent aspects of this item. The obtained attention is then integrated intoa metric-based learning method to predict the user preference on this item. Theadvantage of metric learning is that it can naturally overcome the problem ofdot product similarity, which is adopted by matrix factorization (MF) basedrecommendation models but does not satisfy the triangle inequality property. Inaddition, it is worth mentioning that the attention mechanism cannot only helpmodel user's diverse preferences towards different items, but also overcome thegeometrically restrictive problem caused by collaborative metric learning.Extensive experiments on large-scale real-world datasets show that our modelcan substantially outperform the state-of-the-art baselines, demonstrating thepotential of modeling user diverse preference for recommendation.

User Diverse Preference Modeling by Multimodal Attentive Metric Learning