115 citations · 450 across the 154 of their papers we have counts for
10 papers · 1 filter
Knowledge Graph Enhanced Memory-Augmented Retrieval for Long Context Modeling
Ghadir Alselwi, Basem Suleiman, Hao Xue +4
Long-context language modeling requires not only extending context windows but maintaining coherent understanding of entity states and relationships across thousands of tokens -- a…
User-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation
Jing Du, Zesheng Ye, Congbo Ma +2
Multi-modal recommendation (MMR) enriches item representations by introducing item content, e.g., visual and textual descriptions, to improve upon interaction-only recommenders. Th…
Refine-POI: Reinforcement Fine-Tuned Large Language Models for Next Point-of-Interest Recommendation
Peibo Li, Shuang Ao, Hao Xue +5
Advancing large language models (LLMs) for the next point-of-interest (POI) recommendation task faces two fundamental challenges: (i) although existing methods produce semantic IDs…
Information Retrieval for Climate Impact
Maarten de Rijke, Bart van den Hurk, Flora Salim +28
The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and di…
Long Context Modeling with Ranked Memory-Augmented Retrieval
Ghadir Alselwi, Hao Xue, Shoaib Jameel +3
Effective long-term memory management is crucial for language models handling extended contexts. We introduce the Enhanced Ranked Memory Augmented Retrieval (ERMAR) framework, whic…
GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems
Wilson Wongso, Hao Xue, Flora D. Salim
Traditional Point-of-Interest (POI) recommendation systems often lack transparency, interpretability, and scrutability due to their reliance on dense vector-based user embeddings.…
CAPRI-FAIR: Integration of Multi-sided Fairness in Contextual POI Recommendation Framework
Francis Zac dela Cruz, Flora D. Salim, Yonchanok Khaokaew +1
Point-of-interest (POI) recommendation considers spatio-temporal factors like distance, peak hours, and user check-ins. Given their influence on both consumer experience and POI bu…
Characterizing Information Seeking Processes with Multiple Physiological Signals
Kaixin Ji, Danula Hettiachchi, Flora D. Salim +2
Information access systems are getting complex, and our understanding of user behavior during information seeking processes is mainly drawn from qualitative methods, such as observ…
Large Language Models for Next Point-of-Interest Recommendation
Peibo Li, Maarten de Rijke, Hao Xue +3
The next Point of Interest (POI) recommendation task is to predict users' immediate next POI visit given their historical data. Location-Based Social Network (LBSN) data, which is…
MoParkeR : Multi-objective Parking Recommendation
Mohammad Saiedur Rahaman, Wei Shao, Flora D. Salim +5
Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only. However, there are other factors associated wi…