research-article

Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation

Authors:

Flavian Vasile,

Elena Smirnova,

Alexis ConneauAuthors Info & Claims

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Pages 225 - 232

https://doi.org/10.1145/2959100.2959160

Published: 07 September 2016 Publication History

Abstract

We propose Meta-Prod2vec, a novel method to compute item similarities for recommendation that leverages existing item metadata. Such scenarios are frequently encountered in applications such as content recommendation, ad targeting and web search. Our method leverages past user interactions with items and their attributes to compute low-dimensional embeddings of items. Specifically, the item metadata is injected into the model as side information to regularize the item embeddings. We show that the new item representations lead to better performance on recommendation tasks on an open music dataset.

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Index Terms

Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
      2. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Online advertising
      1. Display advertising

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cover image ACM Conferences

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

September 2016

490 pages

ISBN:9781450340359

DOI:10.1145/2959100

General Chairs:
Shilad Sen
Macalester College, USA
,
Werner Geyer
IBM Research Cambridge, USA
,
Program Chairs:
Jill Freyne
CSIRO, Australia
,
Pablo Castells
Universidad Autónoma de Madrid, Spain

Copyright © 2016 ACM.

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Published: 07 September 2016

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RecSys '16: Tenth ACM Conference on Recommender Systems

September 15 - 19, 2016

Massachusetts, Boston, USA

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RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

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He HYang XHuang FYi FLiang S(2024)GAT4Rec: Sequential Recommendation with a Gated Recurrent Unit and TransformersMathematics10.3390/math1214218912:14(2189)Online publication date: 12-Jul-2024
https://doi.org/10.3390/math12142189
Alves Gomes MMeyes RMeisen PMeisen T(2024)It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding RepresentationJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1901000819:1(135-151)Online publication date: 12-Jan-2024
https://doi.org/10.3390/jtaer19010008
NAGI YOKAMOTO K(2024)A Personalised Session-Based Recommender System with Sequential Updating Based on Aggregation of Item EmbeddingsIEICE Transactions on Information and Systems10.1587/transinf.2023DAP0006E107.D:5(638-649)Online publication date: 1-May-2024
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