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ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction

Authors:

Kangning Zhang,

Weinan ZhangAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 3319 - 3330

https://doi.org/10.1145/3589334.3645396

Published: 13 May 2024 Publication History

Abstract

Click-through rate (CTR) prediction has become increasingly indispensable for various Internet applications. Traditional CTR models convert the multi-field categorical data into ID features via one-hot encoding, and extract the collaborative signals among features. Such a paradigm suffers from the problem of semantic information loss. Another line of research explores the potential of pretrained language models (PLMs) for CTR prediction by converting input data into textual sentences through hard prompt templates. Although semantic signals are preserved, they generally fail to capture the collaborative information (e.g., feature interactions, pure ID features), not to mention the unacceptable inference overhead brought by the huge model size. In this paper, we aim to model both the semantic knowledge and collaborative knowledge for accurate CTR estimation, and meanwhile address the inference inefficiency issue. To benefit from both worlds and close their gaps, we propose a novel model-agnostic framework (i.e., ClickPrompt), where we incorporate CTR models to generate interaction-aware soft prompts for PLMs. We design a prompt-augmented masked language modeling (PA-MLM) pretraining task, where PLM has to recover the masked tokens based on the language context, as well as the soft prompts generated by CTR model. The collaborative and semantic knowledge from ID and textual features would be explicitly aligned and interacted via the prompt interface. Then, we can either tune the CTR model with PLM for superior performance, or solely tune the CTR model without PLM for inference efficiency. Experiments on four real-world datasets validate the effectiveness of ClickPrompt compared with existing baselines.

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Index Terms

ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

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Published: 13 May 2024

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Shanghai Municipal Science and Technology Major Project
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WWW '24: The ACM Web Conference 2024

May 13 - 17, 2024

Singapore, Singapore

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https://dl.acm.org/doi/10.1145/3701551.3703488
Lin JDai XXi YLiu WChen BZhang HLiu YWu CLi XZhu CGuo HYu YTang RZhang W(2025)How Can Recommender Systems Benefit from Large Language Models: A SurveyACM Transactions on Information Systems10.1145/367800443:2(1-47)Online publication date: 18-Jan-2025
https://dl.acm.org/doi/10.1145/3678004
Xi YLiu WLin JWu CChen BTang RZhang WYu Y(2025)Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language ModelsInformation Retrieval10.1007/978-981-96-1710-4_1(1-13)Online publication date: 1-Feb-2025
https://doi.org/10.1007/978-981-96-1710-4_1
Wang HLin JLi XChen BZhu CTang RZhang WYu Y(2024)FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR PredictionProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688106(94-104)Online publication date: 8-Oct-2024
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