JSAI2020
Jun 9 - Jun 12, 2020Virtual Meetings
JSAI2020
Jun 9 - Jun 12, 2020Virtual Meetings
[1E4-GS-9-04]Improving product search by multi-task learning using access log
〇Hitoshi Shimizu1, Tomoharu Iwata1(1. NTT Cmmunication Science Laboratories)
Keywords:
Information retrieval
The ranking of product search results displayed to users has a significant effect on sales of an E-Commerce website.
We propose a method to improve product search using access logs.
Using features obtained from products and queries and access logs as learning data, a neural network is trained to display frequently accessed products at high rank.
There are multiple types of access logs, such as not only conversions, but also carts and clicks.
We experimentally confirmed that training data other than the target type can improve learning to rank.
We propose a method to improve product search using access logs.
Using features obtained from products and queries and access logs as learning data, a neural network is trained to display frequently accessed products at high rank.
There are multiple types of access logs, such as not only conversions, but also carts and clicks.
We experimentally confirmed that training data other than the target type can improve learning to rank.