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An efficient fashion-driven learning approach to model user preferences in on-line shopping scenarios

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6 Author(s)

In this work we tackle the problem of search personalization for on-line soft goods shopping. By learning what the user likes and what the user does not like, better search rankings and therefore a better overall shopping experience can be obtained. The first contribution of the work is in terms of feature selection: given the specific nature of the domain, we combine the traditional visual and text feature into a fashion-driven low dimensional space, compact yet very discriminative. On the learning stage, we describe a two step hybrid learning algorithm, that combines a discriminative model learned off-line over historical data, with an extremely efficient generative model, updated on-line according to the user behavior. Qualitative and quantitative analyses show promising results.

Published in:

Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on

Date of Conference:

13-18 June 2010

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