End-to-End User Behavior Retrieval in Click-Through RatePrediction Model

08/10/2021
by   Qiwei Chen, et al.
0

Click-Through Rate (CTR) prediction is one of the core tasks in recommender systems (RS). It predicts a personalized click probability for each user-item pair. Recently, researchers have found that the performance of CTR model can be improved greatly by taking user behavior sequence into consideration, especially long-term user behavior sequence. The report on an e-commerce website shows that 23% of users have more than 1000 clicks during the past 5 months. Though there are numerous works focus on modeling sequential user behaviors, few works can handle long-term user behavior sequence due to the strict inference time constraint in real world system. Two-stage methods are proposed to push the limit for better performance. At the first stage, an auxiliary task is designed to retrieve the top-k similar items from long-term user behavior sequence. At the second stage, the classical attention mechanism is conducted between the candidate item and k items selected in the first stage. However, information gap happens between retrieval stage and the main CTR task. This goal divergence can greatly diminishing the performance gain of long-term user sequence. In this paper, inspired by Reformer, we propose a locality-sensitive hashing (LSH) method called ETA (End-to-end Target Attention) which can greatly reduce the training and inference cost and make the end-to-end training with long-term user behavior sequence possible. Both offline and online experiments confirm the effectiveness of our model. We deploy ETA into a large-scale real world E-commerce system and achieve extra 3.1% improvements on GMV (Gross Merchandise Value) compared to a two-stage long user sequence CTR model.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

09/01/2019

SDM: Sequential Deep Matching Model for Online Large-scale Recommender System

Capturing users' precise preferences is a fundamental problem in large-s...
09/07/2018

Action-conditional Sequence Modeling for Recommendation

In many online applications interactions between a user and a web-servic...
04/28/2020

CmnRec: Sequential Recommendations with Chunk-accelerated Memory Network

Recently, Memory-based Neural Recommenders (MNR) have demonstrated super...
05/02/2019

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the pr...
06/07/2021

DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction

In E-commerce, vouchers are important marketing tools to enhance users' ...
10/02/2020

Kalman Filtering Attention for User Behavior Modeling in CTR Prediction

Click-through rate (CTR) prediction is one of the fundamental tasks for ...
04/18/2020

Predicting Online Item-choice Behavior: A Shape-restricted Regression Perspective

This paper is concerned with examining the relationship between users' p...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.