Showing cs.IRShow all
3 papers · 1 filter
cs.IR2025
Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
Yunjia Xi, Muyan Weng, Wen Chen +9
Recommender systems (RSs) often suffer from the feedback loop phenomenon, e.g., RSs are trained on data biased by their recommendations. This leads to the filter bubble effect that…
cs.IR2024
Beyond Positive History: Re-ranking with List-level Hybrid Feedback
Muyan Weng, Yunjia Xi, Weiwen Liu +5
As the last stage of recommender systems, re-ranking generates a re-ordered list that aligns with the user's preference. However, previous works generally focus on item-level posit…
cs.IR2024
Efficient and Deployable Knowledge Infusion for Open-World Recommendations via Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin +8
Recommender systems (RSs) play a pervasive role in today's online services, yet their closed-loop nature constrains their access to open-world knowledge. Recently, large language m…