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

Content Provider	IEEE Xplore Digital Library
Author	Camoglu, O. Tianli Yu Bertelli, L. Vu, D. Muralidharan, V. Gokturk, S.
Copyright Year	2010
Description	Author affiliation: 777 Mariner's Island Blvd., San Mateo, CA, USA (Camoglu, O.; Tianli Yu; Bertelli, L.; Vu, D.; Muralidharan, V.; Gokturk, S.)
Abstract	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.
Starting Page	28
Ending Page	34
File Size	1858666
Page Count	7
File Format	PDF
ISBN	9781424470297
e-ISBN	9781424470303
DOI	10.1109/CVPRW.2010.5543748
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Histograms Machine learning algorithms Training data Machine learning Object detection Predictive models Hybrid power systems Computational complexity Delay
Content Type	Text
Resource Type	Article

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