# Deep Multiple Quantization Network on Long Behavior Sequence for Click-Through Rate Prediction

## TL;DR

The paper introduces DMQN, a novel end-to-end method that compresses long user behavior sequences into multiple codeword representations, enhancing CTR prediction accuracy and efficiency in advertising systems.

## Contribution

It proposes a deep multiple quantization approach with hierarchical transduction for better sequence compression and relevance modeling in CTR prediction.

## Key findings

- DMQN improves CTR by 3.5% in industrial tests.
- DMQN reduces latency through caching intermediate representations.
- Experimental results validate the effectiveness of DMQN on multiple datasets.

## Abstract

In Click-Through Rate (CTR) prediction, the long behavior sequence, comprising the user's long period of historical interactions with items has a vital influence on assessing the user's interest in the candidate item. Existing approaches strike efficiency and effectiveness through a two-stage paradigm: first retrieving hundreds of candidate-related items and then extracting interest intensity vector through target attention. However, we argue that the discrepancy in target attention's relevance distribution between the retrieved items and the full long behavior sequence inevitably leads to a performance decline. To alleviate the discrepancy, we propose the Deep Multiple Quantization Network (DMQN) to process long behavior sequence end-to-end through compressing the long behavior sequence. Firstly, the entire spectrum of long behavior sequence will be quantized into multiple codeword sequences based on multiple independent codebooks. Hierarchical Sequential Transduction Unit is incorporated to facilitate the interaction of reduced codeword sequences. Then, attention between the candidate and multiple codeword sequences will output the interest vector. To enable online serving, intermediate representations of the codeword sequences are cached, significantly reducing latency. Our extensive experiments on both industrial and public datasets confirm the effectiveness and efficiency of DMQN. The A/B test in our advertising system shows that DMQN improves CTR by 3.5% and RPM by 2.0%.