Content2vec: Specializing Joint Representations of Product Images and Text for the Task of Product Recommendation

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 information such as text and image, and we analyze its performance on hard recommendation 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-occurrence information and propose a second architecture Content2vec+ and show its lift in performance versus non-hybrid approaches in both cold start and normal recommendation regimes.