5 papers
Semantics Meet Signals: Dual Codebook Representationl Learning for Generative Recommendation
Zheng Hui, Xiaokai Wei, Reza Shirkavand +4
Generative recommendation has recently emerged as a powerful paradigm that unifies retrieval and generation, representing items as discrete semantic tokens and enabling flexible se…
Rotate Both Ways: Time-and-Order RoPE for Generative Recommendation
Xiaokai Wei, Jiajun Wu, Daiyao Yi +2
Generative recommenders, typically transformer-based autoregressive models, predict the next item or action from a user's interaction history. Their effectiveness depends on how th…
The Layout Is the Model: On Action-Item Coupling in Generative Recommendation
Xiaokai Wei, Jiajun Wu, Daiyao Yi +2
Generative Recommendation (GR) models treat a user's interaction history as a sequence to be autoregressively predicted. When both items and actions (e.g., watch time, purchase, co…
Solving the Content Gap in Roblox Game Recommendations: LLM-Based Profile Generation and Reranking
Chen Wang, Xiaokai Wei, Yexi Jiang +7
With the vast and dynamic user-generated content on Roblox, creating effective game recommendations requires a deep understanding of game content. Traditional recommendation models…
OMuleT: Orchestrating Multiple Tools for Practicable Conversational Recommendation
Se-eun Yoon, Xiaokai Wei, Yexi Jiang +5
In this paper, we present a systematic effort to design, evaluate, and implement a realistic conversational recommender system (CRS). The objective of our system is to allow users…