# (Vector) Space is Not the Final Frontier: Product Search as Program Synthesis

arXiv: https://arxiv.org/abs/2304.11473 · HTML: https://mlsanity.com/paper/2304.11473/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 70 references that are in the map.
- First posted on arXiv 22 Apr 2023.

## Builds on (most cited first, 29 of 29)

- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023)
- [Transformer Memory as a Differentiable Search Index](https://mlsanity.com/paper/2202.06991/) (2022)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Multi-Stage Document Ranking with BERT](https://mlsanity.com/paper/1910.14424/) (2019)
- [Data Recombination for Neural Semantic Parsing](https://mlsanity.com/paper/1606.03622/) (2016)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [Compositional Semantic Parsing with Large Language Models](https://mlsanity.com/paper/2209.15003/) (2022)
- [Word meaning in minds and machines](https://mlsanity.com/paper/2008.01766/) (2020)
- [End-to-End Retrieval in Continuous Space](https://mlsanity.com/paper/1811.08008/) (2018)
- [Contrastive language and vision learning of general fashion concepts](https://mlsanity.com/paper/2204.03972/) (2022)
- [Improving Text-to-SQL with Schema Dependency Learning](https://mlsanity.com/paper/2103.04399/) (2021)
- [CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing](https://mlsanity.com/paper/2210.07074/) (2022)
- [Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario](https://mlsanity.com/paper/2007.14906/) (2020)
- [Choppy: Cut Transformer For Ranked List Truncation](https://mlsanity.com/paper/2004.13012/) (2020)
- [SIGIR 2021 E-Commerce Workshop Data Challenge](https://mlsanity.com/paper/2104.09423/) (2021)
- [Product Classification in E-Commerce using Distributional Semantics](https://mlsanity.com/paper/1606.06083/) (2016)
- [Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search](https://mlsanity.com/paper/2108.05317/) (2021)
- [How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead](https://mlsanity.com/paper/2005.12781/) (2020)
- [Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction](https://mlsanity.com/paper/2104.08874/) (2021)
- [You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack](https://mlsanity.com/paper/2107.07346/) (2021)
- [Reasonable Scale Machine Learning with Open-Source Metaflow](https://mlsanity.com/paper/2303.11761/) (2023)
- ["An Image is Worth a Thousand Features": Scalable Product Representations for In-Session Type-Ahead Personalization](https://mlsanity.com/paper/2003.07160/) (2020)
- ["Does it come in black?" CLIP-like models are zero-shot recommenders](https://mlsanity.com/paper/2204.02473/) (2022)
- [End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings](https://mlsanity.com/paper/1806.07296/) (2018)
- [From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search](https://mlsanity.com/paper/2103.12982/) (2021)
- [Personalized Re-ranking for Recommendation](https://mlsanity.com/paper/1904.06813/) (2019)

## Abstract (arXiv)

As ecommerce continues growing, huge investments in ML and NLP for Information Retrieval are following. While the vector space model dominated retrieval modelling in product search - even as vectorization itself greatly changed with the advent of deep learning -, our position paper argues in a contrarian fashion that program synthesis provides significant advantages for many queries and a significant number of players in the market. We detail the industry significance of the proposed approach, sketch implementation details, and address common objections drawing from our experience building a similar system at Tooso.