Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
CONTENT2VEC: SPECIALIZING JOINT REPRESENTATIONS OF PRODUCT IMAGES AND TEXT FOR THE TASK OF PRODUCT RECOMMENDATION
Thomas Nedelec, Elena Smirnova, Flavian Vasile
Nov 05, 2016 (modified: Nov 23, 2016)ICLR 2017 conference submissionreaders: everyone
Abstract:We propose a unified product embedded representation that is optimized for the task of retrieval-based product recommendation. We generate this representation using Content2Vec, a new deep architecture that merges product content infor- mation such as text and image and we analyze its performance on hard recom- mendation setups such as cold-start and cross-category recommendations. In the case of a normal recommendation regime where collaborative information signal is available we merge the product co-occurence information and propose a sec- ond architecture Content2vec+ and show its lift in performance versus non-hybrid approaches.
TL;DR:We propose a unified product embedded representation that is optimized for the task of retrieval-based product recommendation.
Enter your feedback below and we'll get back to you as soon as possible.