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  1. Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems (DLRS 2017)
  2. Specializing Joint Representations for the task of Product Recommendation
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Keynote: Bayesian Deep Learning Models for Recommendation Applications
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation
Specializing Joint Representations for the task of Product Recommendation
Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios
Towards Recommender Systems for Police Photo Lineup
Inter-Session Modeling for Session-Based Recommendation
A Deep Multimodal Approach for Cold-start Music Recommendation
Recurrent Latent Variable Networks for Session-Based Recommendation
Music Playlist Continuation by Learning from Hand-Curated Examples and Song Features: Alleviating the Cold-Start Problem for Rare and Out-of-Set Songs

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Specializing Joint Representations for the task of Product Recommendation

Content Provider ACM Digital Library
Author Smirnova, Elena Nedelec, Thomas Vasile, Flavian
Abstract We propose a unified product embedded representation that is optimized for the task of retrieval-based product recommendation. To this end, we introduce a new way to fuse modality-specific product embeddings into a joint product embedding, in order to leverage both product content information, such as textual descriptions and images, and product collaborative filtering signal. By introducing the fusion step at the very end of our architecture, we are able to train each modality separately, allowing us to keep a modular architecture that is preferable in real-world recommendation deployments. We analyze our performance on normal and hard recommendation setups such as cold-start and cross-category recommendations and achieve good performance on a large product shopping dataset.
Starting Page 10
Ending Page 18
Page Count 9
File Format PDF
ISBN 9781450353533
DOI 10.1145/3125486.3125489
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-08-27
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Representation learning Embeddings Second-order interactions Recommender systems
Content Type Text
Resource Type Article
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