Visually-Aware Personalized Recommendation using Interpretable Image
Representations
release_oj7bdoadwbcevbt2x76ksjti34
by
Charles Packer, Julian McAuley, Arnau Ramisa
2018
Abstract
Visually-aware recommender systems use visual signals present in the
underlying data to model the visual characteristics of items and users'
preferences towards them. In the domain of clothing recommendation,
incorporating items' visual information (e.g., product images) is particularly
important since clothing item appearance is often a critical factor in
influencing the user's purchasing decisions. Current state-of-the-art
visually-aware recommender systems utilize image features extracted from
pre-trained deep convolutional neural networks, however these extremely
high-dimensional representations are difficult to interpret, especially in
relation to the relatively low number of visual properties that may guide
users' decisions.
In this paper we propose a novel approach to personalized clothing
recommendation that models the dynamics of individual users' visual
preferences. By using interpretable image representations generated with a
unique feature learning process, our model learns to explain users' prior
feedback in terms of their affinity towards specific visual attributes and
styles. Our approach achieves state-of-the-art performance on personalized
ranking tasks, and the incorporation of interpretable visual features allows
for powerful model introspection, which we demonstrate by using an interactive
recommendation algorithm and visualizing the rise and fall of fashion trends
over time.
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