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

arXiv: https://arxiv.org/abs/1607.07326 · HTML: https://mlsanity.com/paper/1607.07326/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2016.

## Built on by (most cited first, 30 of 30)

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- [M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations](https://mlsanity.com/paper/2209.11824/) (2022)
- [Query2Prod2Vec Grounded Word Embeddings for eCommerce](https://mlsanity.com/paper/2104.02061/) (2021)
- [Beyond NDCG: behavioral testing of recommender systems with RecList](https://mlsanity.com/paper/2111.09963/) (2021)
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- [Theoretical Understandings of Product Embedding for E-commerce Machine Learning](https://mlsanity.com/paper/2102.12029/) (2021)
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- [Fast Slate Policy Optimization: Going Beyond Plackett-Luce](https://mlsanity.com/paper/2308.01566/) (2023)
- [LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation](https://mlsanity.com/paper/2310.19394/) (2023)
- [Two Is Better Than One: Dual Embeddings for Complementary Product Recommendations](https://mlsanity.com/paper/2211.14982/) (2022)
- [Multimodal Temporal Fusion Transformers Are Good Product Demand Forecasters](https://mlsanity.com/paper/2307.02578/) (2023)
- [Multi-Modal Recommendation System with Auxiliary Information](https://mlsanity.com/paper/2210.10652/) (2022)
- [Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?](https://mlsanity.com/paper/2304.04330/) (2023)
- [Within-basket Recommendation via Neural Pattern Associator](https://mlsanity.com/paper/2401.16433/) (2024)
- [Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendations](https://mlsanity.com/paper/2505.04948/) (2025)
- [Interact2Vec -- An efficient neural network-based model for simultaneously learning users and items embeddings in recommender systems](https://mlsanity.com/paper/2506.22648/) (2025)
- [Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendation](https://mlsanity.com/paper/2108.13824/) (2021)
- [Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework](https://mlsanity.com/paper/2210.16590/) (2022)
- [Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up](https://mlsanity.com/paper/2302.14395/) (2023)
- [Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience](https://mlsanity.com/paper/2309.14046/) (2023)
- [Identifying Shopping Intent in Product QA for Proactive Recommendations](https://mlsanity.com/paper/2404.06017/) (2024)
- [Towards Lifelong Learning Embeddings: An Algorithmic Approach to Dynamically Extend Embeddings](https://mlsanity.com/paper/2408.14118/) (2024)
- [Popularity Estimation and New Bundle Generation using Content and Context based Embeddings](https://mlsanity.com/paper/2412.17310/) (2024)
- [Clicks Versus Conversion: Choosing a Recommender's Training Objective in E-Commerce](https://mlsanity.com/paper/2508.10377/) (2025)

## 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.