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 submission readers: 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.
  • Keywords: Applications
  • Conflicts: criteo.com

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