mlsanityData to 7 Oct 2026

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

arXiv 1607.07326 ↗First posted 25 Jul 2016cs.IR cs.AIcited by 30

Not on the map. Why?

Abstract (arXiv)

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 in- jected into the model as side information to regularize the item embeddings. We show that the new item representa- tions lead to better performance on recommendation tasks on an open music dataset.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jul 2016.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 6; 2022: 6; 2023: 10; 2024: 4; 2025: 4.

30 papers.

Sort:

  1. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  2. 2025RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMscited by 58
  3. 2022Contrastive language and vision learning of general fashion conceptscited by 41
  4. 2022Conditional Generation Net for Medication Recommendationcited by 28
  5. 2023Borda Regret Minimization for Generalized Linear Dueling Banditscited by 17
  6. 2022M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendationscited by 9
  7. 2021Query2Prod2Vec Grounded Word Embeddings for eCommercecited by 8
  8. 2021Beyond NDCG: behavioral testing of recommender systems with RecListcited by 8
  9. 2023A Scalable Framework for Automatic Playlist Continuation on Music Streaming Servicescited by 7
  10. 2023GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Setscited by 7
  11. 2021Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interactioncited by 5
  12. 2021Theoretical Understandings of Product Embedding for E-commerce Machine Learningcited by 4
18 more
  1. 2021DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Predictioncited by 3
  2. 2023Fast Slate Policy Optimization: Going Beyond Plackett-Lucecited by 3
  3. 2023LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendationcited by 3
  4. 2022Two Is Better Than One: Dual Embeddings for Complementary Product Recommendationscited by 2
  5. 2023Multimodal Temporal Fusion Transformers Are Good Product Demand Forecasterscited by 2
  6. 2022Multi-Modal Recommendation System with Auxiliary Informationcited by 1
  7. 2023Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?cited by 1
  8. 2024Within-basket Recommendation via Neural Pattern Associatorcited by 1
  9. 2025Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendationscited by 1
  10. 2025Interact2Vec -- An efficient neural network-based model for simultaneously learning users and items embeddings in recommender systemscited by 1
  11. 2021Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendationcited by 0
  12. 2022Track2Vec: fairness music recommendation with a GPU-free customizable-driven frameworkcited by 0
  13. 2023Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-upcited by 0
  14. 2023Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experiencecited by 0
  15. 2024Identifying Shopping Intent in Product QA for Proactive Recommendationscited by 0
  16. 2024Towards Lifelong Learning Embeddings: An Algorithmic Approach to Dynamically Extend Embeddingscited by 0
  17. 2024Popularity Estimation and New Bundle Generation using Content and Context based Embeddingscited by 0
  18. 2025Clicks Versus Conversion: Choosing a Recommender's Training Objective in E-Commercecited by 0