HKUDS/EasyRec
[EMNLP'2025] "EasyRec: Simple yet Effective Language Model for Recommendation"
This project helps e-commerce managers, content strategists, and product recommenders improve how they suggest items to customers. By analyzing text descriptions of users' preferences and product features, it generates high-quality semantic embeddings. This allows for more relevant recommendations, even for new products or users without extensive interaction history.
140 stars.
Use this if you need to generate personalized product or content recommendations using detailed textual descriptions of user interests and item attributes, especially for cold-start scenarios or to enhance existing collaborative filtering systems.
Not ideal if your recommendation system relies solely on historical interaction data without rich textual profiles, or if you require an extremely lightweight solution for real-time, low-latency lookups without text processing.
Stars
140
Forks
16
Language
Python
License
—
Category
Last pushed
Nov 03, 2025
Commits (30d)
0
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