4 papers
On the Practice of Scaling Search Conversion Rate Prediction
James Pak, Jyun-Yu Jiang, Fan Zhang +13
Scaling a Search Conversion Rate (CVR) prediction model, especially in high-traffic environments, presents a challenge: superior model quality needs to be balanced with strict cons…
STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation
Zhenye Yang, Jinpeng Chen, Huan Li +6
Conversational recommender systems (CRSs) aim to proactively capture user preferences through natural language dialogue and recommend high-quality items. To achieve this, CRS gathe…
Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation
Jinpeng Chen, Jianxiang He, Huan Li +5
Session-based Recommendation (SBR) aims to predict the next item a user will likely engage with, using their interaction sequence within an anonymous session. Existing SBR models o…
Leveraging Multimodal Data and Side Users for Diffusion Cross-Domain Recommendation
Fan Zhang, Jinpeng Chen, Huan Li +6
Cross-domain recommendation (CDR) aims to address the persistent cold-start problem in Recommender Systems. Current CDR research concentrates on transferring cold-start users' info…