Paper Number
ICIS2025-2157
Paper Type
Short
Abstract
Current recommender systems (RSs) on short video platforms prioritize behavioral signals, often neglecting users’ deeper value-driven motivations. We propose a novel value-aligned recommendation framework grounded in Schwartz value theory, leveraging large language models (LLMs) to simulate users and infer perceived values from video content. Our approach integrates these values into recommendation algorithms, enhancing models like SASRec and FDSA. Computational experiments demonstrate improved performance across key metrics, validating the framework’s effectiveness. This research pioneers LLM-based value extraction from multimedia, offering a scalable, theory-driven solution for value-aware RSs. The findings suggest potential for more user-centric systems, with future work planned for large-scale field experiments to assess real-world impact.
Recommended Citation
Chen, Kejun; Liu, Shuchang; Zhang, Jiayin; Yin, Zhitao; Bai, Yandong; Li, Xin; Xu, Sean Xin; Yu, Qing; and Li, Xiang, "Individualized Value Discovery using LLMs for Short Video Recommendation" (2025). ICIS 2025 Proceedings. 23.
https://aisel.aisnet.org/icis2025/gen_ai/gen_ai/23
Individualized Value Discovery using LLMs for Short Video Recommendation
Current recommender systems (RSs) on short video platforms prioritize behavioral signals, often neglecting users’ deeper value-driven motivations. We propose a novel value-aligned recommendation framework grounded in Schwartz value theory, leveraging large language models (LLMs) to simulate users and infer perceived values from video content. Our approach integrates these values into recommendation algorithms, enhancing models like SASRec and FDSA. Computational experiments demonstrate improved performance across key metrics, validating the framework’s effectiveness. This research pioneers LLM-based value extraction from multimedia, offering a scalable, theory-driven solution for value-aware RSs. The findings suggest potential for more user-centric systems, with future work planned for large-scale field experiments to assess real-world impact.
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