Social Knowledge for Cross-Domain User Preference Modeling
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...
| Authors | Nir Lotan, Adir Solomon, Ido Guy et al. |
|---|---|
| Primary category | cs.SI |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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