11 papers
AsarRec: Adaptive Sequential Augmentation for Robust Self-supervised Sequential Recommendation
Kaike Zhang, Qi Cao, Fei Sun +3
Sequential recommender systems have demonstrated strong capabilities in modeling users' dynamic preferences and capturing item transition patterns. However, real-world user behavio…
GoalRank: Group-Relative Optimization for a Large Ranking Model
Kaike Zhang, Xiaobei Wang, Shuchang Liu +7
Mainstream ranking approaches typically follow a Generator-Evaluator two-stage paradigm, where a generator produces candidate lists and an evaluator selects the best one. Recent wo…
The 2nd Workshop on Human-Centered Recommender Systems
Kaike Zhang, Jiakai Tang, Du Su +6
Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics, e.g., accuracy, clicks, and…
From Generation to Consumption: Personalized List Value Estimation for Re-ranking
Kaike Zhang, Xiaobei Wang, Xiaoyu Yang +5
Re-ranking is critical in recommender systems for optimizing the order of recommendation lists, thus improving user satisfaction and platform revenue. Most existing methods follow…
Robust Recommender System: A Survey and Future Directions
Kaike Zhang, Qi Cao, Fei Sun +4
With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical…
LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning Attacks
Kaike Zhang, Qi Cao, Yunfan Wu +3
Sequential recommender systems stand out for their ability to capture users' dynamic interests and the patterns of item-to-item transitions. However, the inherent openness of seque…