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Generative Conversational Recommender System
Sixiao Zhang, Mingrui Liu, Cheng Long
Conversational recommender systems aim to provide personalized recommendations via natural language interactions. However, existing approaches either decouple recommendation from d…
Facet-Aware Multi-Head Mixture-of-Experts Model with Text-Enhanced Pre-training for Sequential Recommendation
Mingrui Liu, Sixiao Zhang, Cheng Long
Sequential recommendation (SR) systems excel at capturing users' dynamic preferences by leveraging their interaction histories. Most existing SR systems assign a single embedding v…
On Mitigating Data Sparsity in Conversational Recommender Systems
Sixiao Zhang, Mingrui Liu, Wei Yuan +2
Conversational recommender systems (CRSs) infer user preferences from dialogue contexts, but they suffer from severe data sparsity in both dialogue and entity spaces. Dialogue data…
Data Watermarking for Sequential Recommender Systems
Sixiao Zhang, Cheng Long, Wei Yuan +2
In the era of large foundation models, data has become a crucial component in building high-performance AI systems. As the demand for high-quality and large-scale data continues to…
Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation
Mingrui Liu, Sixiao Zhang, Cheng Long
Sequential recommendation (SR) systems excel at capturing users' dynamic preferences by leveraging their interaction histories. Most existing SR systems assign a single embedding v…