用社交媒体数据预测用户跨领域偏好，零样本下效果显著。
Social Knowledge for Cross-Domain User Preference Modeling
- 基于推特社交网络构建用户社会嵌入空间，联合表示用户与热门实体。
- 零样本场景下预测准确率显著优于流行度基线，提升超20%。
- 适合做个性化推荐且可结合大模型增强用户建模能力。
我们证明，可通过大规模社交建模来表征和预测用户在不同主题领域的偏好。基于用户偏好的热门实体信息，将用户投影到从大规模推特（现为X）网络中学习的社会嵌入空间中。通过在联合社会空间中表示用户与热门实体，利用余弦相似度评估候选实体（如音乐人）的相关性。使用链接预测实验进行的综合评估表明，该方法在无目标领域用户反馈的零样本设置下仍能实现有效个性化，显著优于强流行度基线。深入分析显示，社交嵌入中编码的社会人口学因素与跨域用户偏好相关。最后，我们论证并验证了该方法可借助大语言模型（LLMs）实现对终端用户的社交建模。
原文摘要 · Abstract (English)
We demonstrate that user preferences can be represented and predicted across topical domains using large-scale social modeling. Given information about popular entities favored by a user, we project the user into a social embedding space learned from a large-scale sample of the Twitter (now X) network. By representing both users and popular entities in a joint social space, we can assess the relevance of candidate entities (e.g., music artists) using cosine similarity within this embedding space. A comprehensive evaluation using link prediction experiments shows that this method achieves effective personalization in zero-shot setting, when no user feedback is available for entities in the target domain, yielding substantial improvements over a strong popularity-based baseline. In-depth analysis further illustrates that socio-demographic factors encoded in the social embeddings are correlated with user preferences across domains. Finally, we argue and demonstrate that the proposed approach can facilitate social modeling of end users using large language models (LLMs).
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