# Social Knowledge for Cross-Domain User Preference Modeling

arXiv: https://arxiv.org/abs/2603.10148 · HTML: https://mlsanity.com/paper/2603.10148/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Builds on (most cited first, 14 of 14)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [RecMind: Large Language Model Powered Agent For Recommendation](https://mlsanity.com/paper/2308.14296/) (2023)
- [Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers](https://mlsanity.com/paper/2311.09180/) (2023)
- [PersonalLLM: Tailoring LLMs to Individual Preferences](https://mlsanity.com/paper/2409.20296/) (2024)
- [Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms](https://mlsanity.com/paper/1805.09843/) (2018)
- [TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding](https://mlsanity.com/paper/2006.01321/) (2020)
- [Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach](https://mlsanity.com/paper/2007.04833/) (2020)
- [Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions](https://mlsanity.com/paper/2311.04590/) (2023)
- [Aligning Large Language Models with Recommendation Knowledge](https://mlsanity.com/paper/2404.00245/) (2024)
- [Taxonomy-Guided Zero-Shot Recommendations with LLMs](https://mlsanity.com/paper/2406.14043/) (2024)
- [Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement](https://mlsanity.com/paper/2010.06472/) (2020)
- [Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model](https://mlsanity.com/paper/2104.03189/) (2021)
- [The X Types -- Mapping the Semantics of the Twitter Sphere](https://mlsanity.com/paper/2409.14584/) (2024)

## Abstract (arXiv)

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).