80 citations · 121 across the 6 of their papers we have counts for
6 papers
Distribution-Level Contrastive Supervision for Generative Recommendation
Ziqi Xue, Dingxian Wang, Yimeng Bai +7
Recent generative recommenders improve scalability by retrieving items through token generation instead of traditional ranking over large candidate sets. Yet their training signals…
UniGRec: Unified Generative Recommendation with Soft Identifiers for End-to-End Optimization
Jialei Li, Yang Zhang, Yimeng Bai +7
Generative recommendation has recently emerged as a transformative paradigm that directly generates target items, surpassing traditional cascaded approaches. It typically involves…
Bi-Level Optimization for Generative Recommendation: Bridging Tokenization and Generation
Yimeng Bai, Chang Liu, Yang Zhang +5
Generative recommendation is emerging as a transformative paradigm by directly generating recommended items, rather than relying on matching. Building such a system typically invol…
Delayed Feedback Modeling with Influence Functions
Chenlu Ding, Jiancan Wu, Yancheng Yuan +5
In online advertising under the cost-per-conversion (CPA) model, accurate conversion rate (CVR) prediction is crucial. A major challenge is delayed feedback, where conversions may…
Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning
Yuyue Zhao, Jiancan Wu, Xiang Wang +3
Conventional recommender systems (RSs) face challenges in precisely capturing users' fine-grained preferences. Large language models (LLMs) have shown capabilities in commonsense r…
Explainable Reasoning over Knowledge Graphs for Recommendation
Xiang Wang, Dingxian Wang, Canran Xu +3
Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity be…