Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
10-2024
Abstract
Despite the success of conventional collaborative filtering (CF) approaches for recommendation systems, they exhibit limitations in leveraging semantic knowledge within the textual attributes of users and items. Recent focus on the application of large language models for recommendation (LLM4Rec) has highlighted their capability for effective semantic knowledge capture. However, these methods often overlook the collaborative signals in user behaviors. Some simply instruct-tune a language model, while others directly inject the embeddings of a CF-based model, lacking a synergistic fusion of different modalities. To address these issues, we propose a framework of Collaborative Cross-modal Fusion with Large Language Models, termed CCF-LLM, for recommendation. In this framework, we translate the user-item interactions into a hybrid prompt to encode both semantic knowledge and collaborative signals, and then employ an attentive cross-modal fusion strategy to effectively fuse latent embeddings of both modalities. Extensive experiments demonstrate that CCF-LLM outperforms existing methods by effectively utilizing semantic and collaborative signals in the LLM4Rec context.
Keywords
Large Language Models, Recommendation systems, Cross-modal, Collaborative filtering
Discipline
Artificial Intelligence and Robotics | Computer Sciences
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024) : Boise, Idaho, USA, October 21-25
First Page
1565
Last Page
1574
Identifier
10.1145/3627673.3679596
Publisher
Association for Computing Machinery
City or Country
Boise, Idaho, USA
Citation
LIU, Zhongzhou; ZHANG, Hao; DONG, Kuicai; and FANG, Yuan.
Collaborative cross-modal fusion with Large Language Model for recommendation. (2024). Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024) : Boise, Idaho, USA, October 21-25. 1565-1574.
Available at: https://ink.library.smu.edu.sg/sis_research/9751
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3627673.3679596
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