Beyond Semantics: Learning a Behavior Augmented Relevance Model with
Self-supervised Learning
- URL: http://arxiv.org/abs/2308.05379v4
- Date: Tue, 24 Oct 2023 08:49:01 GMT
- Title: Beyond Semantics: Learning a Behavior Augmented Relevance Model with
Self-supervised Learning
- Authors: Zeyuan Chen, Wei Chen, Jia Xu, Zhongyi Liu, Wei Zhang
- Abstract summary: Relevance modeling aims to locate desirable items for corresponding queries.
auxiliary query-item interactions extracted from user historical behavior data could provide hints to reveal users' search intents further.
Our model builds multi-level co-attention for distilling coarse-grained and fine-grained semantic representations from both neighbor and target views.
- Score: 25.356999988217325
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Query-oriented Data Augmentation for Session Search [71.84678750612754]
We propose query-oriented data augmentation to enrich search logs and empower the modeling.
We generate supplemental training pairs by altering the most important part of a search context.
We develop several strategies to alter the current query, resulting in new training data with varying degrees of difficulty.
arXiv Detail & Related papers (2024-07-04T08:08:33Z) - Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation [65.16137964758612]
We explore the use of long-context capabilities in large language models to create synthetic reading comprehension data from entire books.
Our objective is to test the capabilities of LLMs to analyze, understand, and reason over problems that require a detailed comprehension of long spans of text.
arXiv Detail & Related papers (2024-05-31T20:15:10Z) - Beyond Two-Tower Matching: Learning Sparse Retrievable
Cross-Interactions for Recommendation [80.19762472699814]
Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications.
It suffers two main challenges, including limited feature interaction capability and reduced accuracy in online serving.
We propose a new matching paradigm named SparCode, which supports not only sophisticated feature interactions but also efficient retrieval.
arXiv Detail & Related papers (2023-11-30T03:13:36Z) - Semantic Equivalence of e-Commerce Queries [6.232692545488813]
This paper introduces a framework to recognize and leverage query equivalence to enhance searcher and business outcomes.
The proposed approach addresses three key problems: mapping queries to vector representations of search intent, identifying nearest neighbor queries expressing equivalent or similar intent, and optimizing for user or business objectives.
arXiv Detail & Related papers (2023-08-07T18:40:13Z) - Unified Embedding Based Personalized Retrieval in Etsy Search [0.42056926734482064]
We propose learning a unified embedding model incorporating graph, transformer and term-based embeddings end to end.
Our personalized retrieval model significantly improves the overall search experience, as measured by a 5.58% increase in search purchase rate and a 2.63% increase in site-wide conversion rate.
arXiv Detail & Related papers (2023-06-07T23:24:50Z) - Unified Visual Relationship Detection with Vision and Language Models [89.77838890788638]
This work focuses on training a single visual relationship detector predicting over the union of label spaces from multiple datasets.
We propose UniVRD, a novel bottom-up method for Unified Visual Relationship Detection by leveraging vision and language models.
Empirical results on both human-object interaction detection and scene-graph generation demonstrate the competitive performance of our model.
arXiv Detail & Related papers (2023-03-16T00:06:28Z) - UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question
Answering Over Knowledge Graph [89.98762327725112]
Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question.
We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
arXiv Detail & Related papers (2022-12-02T04:08:09Z) - Knowledge Guided Bidirectional Attention Network for Human-Object
Interaction Detection [3.0915392100355192]
We argue that the independent use of the bottom-up parsing strategy in HOI is counter-intuitive and could lead to the diffusion of attention.
We introduce a novel knowledge-guided top-down attention into HOI, and propose to model the relation parsing as a "look and search" process.
We implement the process via unifying the bottom-up and top-down attention in a single encoder-decoder based model.
arXiv Detail & Related papers (2022-07-16T16:42:49Z) - Approximate Nearest Neighbor Search under Neural Similarity Metric for
Large-Scale Recommendation [20.42993976179691]
We propose a novel method to extend ANN search to arbitrary matching functions.
Our main idea is to perform a greedy walk with a matching function in a similarity graph constructed from all items.
The proposed method has been fully deployed in the Taobao display advertising platform and brings a considerable advertising revenue increase.
arXiv Detail & Related papers (2022-02-14T07:55:57Z) - Sequential Search with Off-Policy Reinforcement Learning [48.88165680363482]
We propose a highly scalable hybrid learning model that consists of an RNN learning framework and an attention model.
As a novel optimization step, we fit multiple short user sequences in a single RNN pass within a training batch, by solving a greedy knapsack problem on the fly.
We also explore the use of off-policy reinforcement learning in multi-session personalized search ranking.
arXiv Detail & Related papers (2022-02-01T06:52:40Z) - Adaptive Attentional Network for Few-Shot Knowledge Graph Completion [16.722373937828117]
Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs.
Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties.
This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations.
arXiv Detail & Related papers (2020-10-19T16:27:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.