# Learning Latent Vector Spaces for Product Search

arXiv: https://arxiv.org/abs/1608.07253 · HTML: https://mlsanity.com/paper/1608.07253/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [MS MARCO: Benchmarking Ranking Models in the Large-Data Regime](https://mlsanity.com/paper/2105.04021/) (2021)
- [Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision](https://mlsanity.com/paper/2206.14719/) (2022)
- [When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation](https://mlsanity.com/paper/2305.10822/) (2023)
- [Learning to Ask: Conversational Product Search via Representation Learning](https://mlsanity.com/paper/2411.14466/) (2024)
- [AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping](https://mlsanity.com/paper/2602.12315/) (2026)
- [Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2301.02458/) (2023)
- [Cracking Vector Search Indexes](https://mlsanity.com/paper/2503.01823/) (2025)
- [Information Discovery in e-Commerce](https://mlsanity.com/paper/2410.05763/) (2024)
- [SECRET: Semi-supervised Clinical Trial Document Similarity Search](https://mlsanity.com/paper/2505.10780/) (2025)
- [PSCon: Product Search Through Conversations](https://mlsanity.com/paper/2502.13881/) (2025)
- [Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap](https://mlsanity.com/paper/2501.14826/) (2025)
- [Deep Neural Networks for Accurate Depth Estimation with Latent Space Features](https://mlsanity.com/paper/2502.11777/) (2025)
- [DiffusionGS: Generative Search with Query Conditioned Diffusion in Kuaishou](https://mlsanity.com/paper/2508.17754/) (2025)
- [SeqLLM: Augmenting LLMs with Behavioral-Sequence Modeling for High-Stakes Decisions at WeChat Pay](https://mlsanity.com/paper/2608.03063/) (2026)

## Abstract (arXiv)

We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations. The power of the model lies in its ability to directly model the discriminative relation between products and a particular word. We compare our method to existing latent vector space models (LSI, LDA and word2vec) and evaluate it as a feature in a learning to rank setting. Our latent vector space model achieves its enhanced performance as it learns better product representations. Furthermore, the mapping from words to products and the representations of words benefit directly from the errors propagated back from the product representations during parameter estimation. We provide an in-depth analysis of the performance of our model and analyze the structure of the learned representations.