# Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning

arXiv: https://arxiv.org/abs/2308.05379 · HTML: https://mlsanity.com/paper/2308.05379/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 38 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks](https://mlsanity.com/paper/2208.10442/) (2022)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (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)
- [StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding](https://mlsanity.com/paper/1908.04577/) (2019)
- [Convolutional Neural Network Architectures for Matching Natural Language Sentences](https://mlsanity.com/paper/1503.03244/) (2015)
- [Text Matching as Image Recognition](https://mlsanity.com/paper/1602.06359/) (2016)
- [TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search](https://mlsanity.com/paper/2101.06323/) (2021)
- [AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search](https://mlsanity.com/paper/2104.12080/) (2021)
- [ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs](https://mlsanity.com/paper/1512.05193/) (2015)
- [Learning a Product Relevance Model from Click-Through Data in E-Commerce](https://mlsanity.com/paper/2102.07098/) (2021)
- [Semantic Modelling with Long-Short-Term Memory for Information Retrieval](https://mlsanity.com/paper/1412.6629/) (2014)
- [aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model](https://mlsanity.com/paper/1801.01641/) (2018)
- [Graph-Based Tri-Attention Network for Answer Ranking in CQA](https://mlsanity.com/paper/2103.03583/) (2021)
- [Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation](https://mlsanity.com/paper/2109.11790/) (2021)

## Built on by (most cited first, 4 of 4)

- [Broadening Access to Simulations for End-Users via Large Language Models: Challenges and Opportunities](https://mlsanity.com/paper/2409.15290/) (2024)
- [Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning](https://mlsanity.com/paper/2412.12504/) (2024)
- [ADORE: Autonomous Domain-Oriented Relevance Engine for E-commerce](https://mlsanity.com/paper/2512.02555/) (2025)
- [CSRM-LLM: Embracing Multilingual LLMs for Cold-Start Relevance Matching in Emerging E-commerce Markets](https://mlsanity.com/paper/2509.01566/) (2025)

## Abstract (arXiv)

Relevance modeling aims to locate desirable items for corresponding queries, which is crucial for search engines to ensure user experience. Although most conventional approaches address this problem by assessing the semantic similarity between the query and item, pure semantic matching is not everything. In reality, auxiliary query-item interactions extracted from user historical behavior data of the search log could provide hints to reveal users' search intents further. Drawing inspiration from this, we devise a novel Behavior Augmented Relevance Learning model for Alipay Search (BARL-ASe) that leverages neighbor queries of target item and neighbor items of target query to complement target query-item semantic matching. Specifically, our model builds multi-level co-attention for distilling coarse-grained and fine-grained semantic representations from both neighbor and target views. The model subsequently employs neighbor-target self-supervised learning to improve the accuracy and robustness of BARL-ASe by strengthening representation and logit learning. Furthermore, we discuss how to deal with the long-tail query-item matching of the mini apps search scenario of Alipay practically. Experiments on real-world industry data and online A/B testing demonstrate our proposal achieves promising performance with low latency.