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Found 4 papers, 3 papers with code
Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation
We conduct extensive experiments to evaluate the performance of representative sequential recommendation models in the setting of lifelong sequences.
Rethinking Large Language Model Architectures for Sequential Recommendations
This design stems from our empirical observation that beam search decoding is ultimately unnecessary for sequential recommendations.
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases.
On the User Behavior Leakage from Recommender System Exposure
Given the fact that system exposure data could be widely accessed from a relatively larger scope, we believe that the user past behavior privacy has a high risk of leakage in recommender systems.
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