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

08/10/2023
by   Zeyuan Chen, et al.
1

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/13/2021

Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search

Result relevance prediction is an essential task of e-commerce search en...
research
04/29/2019

Learning Image Information for eCommerce Queries

Computing similarity between a query and a document is fundamental in an...
research
04/25/2021

AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search

Sponsored search ads appear next to search results when people look for ...
research
03/28/2018

Deeply Supervised Semantic Model for Click-Through Rate Prediction in Sponsored Search

In sponsored search it is critical to match ads that are relevant to a q...
research
12/01/2018

Approximating Categorical Similarity in Sponsored Search Relevance

Sponsored Search is a major source of revenue for web search engines. Si...
research
04/26/2021

A unified Neural Network Approach to E-CommerceRelevance Learning

Result relevance scoring is critical to e-commerce search user experienc...
research
03/02/2023

Matching-based Term Semantics Pre-training for Spoken Patient Query Understanding

Medical Slot Filling (MSF) task aims to convert medical queries into str...

Please sign up or login with your details

Forgot password? Click here to reset