5 citations · 15 across the 12 of their papers we have counts for
13 papers
Purely Semantic Indexing for LLM-based Generative Recommendation and Retrieval
Ruohan Zhang, Jiacheng Li, Julian McAuley +1
Semantic identifiers (IDs) have proven effective in adapting large language models for generative recommendation and retrieval. However, existing methods often suffer from semantic…
Inductive Generative Recommendation via Retrieval-based Speculation
Yijie Ding, Jiacheng Li, Julian McAuley +1
Generative recommendation (GR) is an emerging paradigm that tokenizes items into discrete tokens and learns to autoregressively generate the next tokens as predictions. While this…
Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders
Yupeng Hou, Jiacheng Li, Xiangjun Fu +4
Feature engineering has long been central to recommender systems, yet effectively leveraging textual item features remains challenging. Recent advances in large language models (LL…
Text Is All You Need: Learning Language Representations for Sequential Recommendation
Jiacheng Li, Ming Wang, Jin Li +4
Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequen…
Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting
William Hogan, Jiacheng Li, Jingbo Shang
Open-world Relation Extraction (OpenRE) has recently garnered significant attention. However, existing approaches tend to oversimplify the problem by assuming that all unlabeled te…
CLIP also Understands Text: Prompting CLIP for Phrase Understanding
An Yan, Jiacheng Li, Wanrong Zhu +3
Contrastive Language-Image Pretraining (CLIP) efficiently learns visual concepts by pre-training with natural language supervision. CLIP and its visual encoder have been explored o…
UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation
Jiacheng Li, Zhankui He, Jingbo Shang +1
Personalized natural language generation for explainable recommendations plays a key role in justifying why a recommendation might match a user's interests. Existing models usually…
SPOT: Knowledge-Enhanced Language Representations for Information Extraction
Jiacheng Li, Yannis Katsis, Tyler Baldwin +4
Knowledge-enhanced pre-trained models for language representation have been shown to be more effective in knowledge base construction tasks (i.e.,~relation extraction) than languag…
Personalized Showcases: Generating Multi-Modal Explanations for Recommendations
An Yan, Zhankui He, Jiacheng Li +2
Existing explanation models generate only text for recommendations but still struggle to produce diverse contents. In this paper, to further enrich explanations, we propose a new t…
Fine-grained Contrastive Learning for Relation Extraction
William Hogan, Jiacheng Li, Jingbo Shang
Recent relation extraction (RE) works have shown encouraging improvements by conducting contrastive learning on silver labels generated by distant supervision before fine-tuning on…
Coarse-to-Fine Sparse Sequential Recommendation
Jiacheng Li, Tong Zhao, Jin Li +5
Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-t…
UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining
Jiacheng Li, Jingbo Shang, Julian McAuley
High-quality phrase representations are essential to finding topics and related terms in documents (a.k.a. topic mining). Existing phrase representation learning methods either sim…
Weakly Supervised Named Entity Tagging with Learnable Logical Rules
Jiacheng Li, Haibo Ding, Jingbo Shang +2
We study the problem of building entity tagging systems by using a few rules as weak supervision. Previous methods mostly focus on disambiguation entity types based on contexts and…