Controllable Multi-Behavior Recommendation for In-Game Skins with Large Sequential Model
Authors: 
Yanjie Gou, Yuanzhou Yao, Zhao Zhang, Yiqing Wu, Yi Hu, Fuzhen Zhuang, Jiangming Liu, Yongjun XuAuthors Info & Claims
Pages 4986 - 4996
Abstract
Online games often house virtual shops where players can acquire character skins. Our task is centered on tailoring skin recommendations across diverse scenarios by analyzing historical interactions such as clicks, usage, and purchases. Traditional multi-behavior recommendation models employed for this task are limited. They either only predict skins based on a single type of behavior or merely recommend skins for target behavior type/task. These models lack the ability to control predictions of skins that are associated with different scenarios and behaviors. To overcome these limitations, we utilize the pretraining capabilities of Large Sequential Models (LSMs) coupled with a novel stimulus prompt mechanism and build a controllable multi-behavior recommendation (CMBR) model. In our approach, the pretraining ability is used to encapsulate users' multi-behavioral sequences into the representation of users' general interests. Subsequently, our designed stimulus prompt mechanism stimulates the model to extract scenario-related interests, thus generating potential skin purchases (or clicks and other interactions) for users. To the best of our knowledge, this is the first work to provide controlled multi-behavior recommendations, and also the first to apply the pretraining capabilities of LSMs in game domain. Through offline experiments and online A/B tests, we validate our method significantly outperforms baseline models, exhibiting about a tenfold improvement on various metrics during the offline test.
Supplemental Material
MP4 File - KDD 2024 - Controllable Multi-Behavior Recommendation for In-Game Skins with Large Sequential Model
A large sequential model with designed stimulus prompts can generate controlled recommendation results tailored to diverse scenarios and multiple user behaviors.
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August 2024
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This work is licensed under a Creative Commons Attribution International 4.0 License.
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Published: 24 August 2024
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KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 25 - 29, 2024
Barcelona, Spain
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