# Enhancing Multi-field B2B Cloud Solution Matching via Contrastive Pre-training

arXiv: https://arxiv.org/abs/2402.07076 · HTML: https://mlsanity.com/paper/2402.07076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 56 references that are in the map.
- First posted on arXiv 11 Feb 2024.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [A Decomposable Attention Model for Natural Language Inference](https://mlsanity.com/paper/1606.01933/) (2016)
- [Enhanced LSTM for Natural Language Inference](https://mlsanity.com/paper/1609.06038/) (2016)
- [Semi-supervised Sequence Learning](https://mlsanity.com/paper/1511.01432/) (2015)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph](https://mlsanity.com/paper/2105.02605/) (2021)
- [Towards Universal Sequence Representation Learning for Recommender Systems](https://mlsanity.com/paper/2206.05941/) (2022)
- [Neural News Recommendation with Attentive Multi-View Learning](https://mlsanity.com/paper/1907.05576/) (2019)
- [Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval](https://mlsanity.com/paper/1502.06922/) (2015)
- [TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search](https://mlsanity.com/paper/2101.06323/) (2021)
- [An Embedding Learning Framework for Numerical Features in CTR Prediction](https://mlsanity.com/paper/2012.08986/) (2020)
- [TwinBERT: Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval](https://mlsanity.com/paper/2002.06275/) (2020)
- [Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking](https://mlsanity.com/paper/2108.10510/) (2021)
- [Encoder Adaptation of Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2110.01599/) (2021)
- [Neural Ranking Models with Multiple Document Fields](https://mlsanity.com/paper/1711.09174/) (2017)
- [MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling](https://mlsanity.com/paper/2210.08753/) (2022)
- [New Hybrid Techniques for Business Recommender Systems](https://mlsanity.com/paper/2109.13922/) (2021)
- [CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection](https://mlsanity.com/paper/2211.17171/) (2022)

## Abstract (arXiv)

Cloud solutions have gained significant popularity in the technology industry as they offer a combination of services and tools to tackle specific problems. However, despite their widespread use, the task of identifying appropriate company customers for a specific target solution to the sales team of a solution provider remains a complex business problem that existing matching systems have yet to adequately address. In this work, we study the B2B solution matching problem and identify two main challenges of this scenario: (1) the modeling of complex multi-field features and (2) the limited, incomplete, and sparse transaction data. To tackle these challenges, we propose a framework CAMA, which is built with a hierarchical multi-field matching structure as its backbone and supplemented by three data augmentation strategies and a contrastive pre-training objective to compensate for the imperfections in the available data. Through extensive experiments on a real-world dataset, we demonstrate that CAMA outperforms several strong baseline matching models significantly. Furthermore, we have deployed our matching framework on a system of Huawei Cloud. Our observations indicate an improvement of about 30% compared to the previous online model in terms of Conversion Rate (CVR), which demonstrates its great business value.