Suchergebnisse - "Computer Science - Information Retrieval"

Orthogonal nonnegative matrix factorization with the Kullback–Leibler divergence

Autoren: Jean Pacifique Nkurunziza Fulgence Nahayo Nicolas Gillis

Quelle: Pattern Recognition Letters. 197:353-358

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Machine Learning (stat.ML), Electrical Engineering and Systems Science - Signal Processing, Information Retrieval (cs.IR), Computer Science - Information Retrieval, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2410.07786

Exact and Efficient Unlearning for Large Language Model-Based Recommendation

Autoren: Zhiyu Hu Yang Zhang Minghao Xiao et al.

Quelle: IEEE Transactions on Knowledge and Data Engineering. 37:5866-5877

Schlagwörter: FOS: Computer and information sciences, Information Retrieval (cs.IR), Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2404.10327

Investigating Task Arithmetic for Zero-Shot Information Retrieval

Autoren: Marco Braga Pranav Kasela Alessandro Raganato et al.

Quelle: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. :2738-2743

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, Task Arithmetic, Domain-specific and Multilingual IR, Zero-Shot, Computation and Language (cs.CL), Information Retrieval (cs.IR), Computer Science - Information Retrieval, Machine Learning (cs.LG)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2505.00649
https://hdl.handle.net/11583/3002210
https://dl.acm.org/doi/pdf/10.1145/3726302.3730216
https://doi.org/10.1145/3726302.3730216

Information Retrieval in the Age of Generative AI: The RGB Model

Autoren: Michele Garetto Alessandro Cornacchia Franco Galante et al.

Quelle: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. :602-612

Schlagwörter: Performance (cs.PF), FOS: Computer and information sciences, Computer Science - Performance, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, AI, modelling, risks, information retrieval, Web answering, Information quality, Large Language Models, Retrieval-Augmented Generation, Automation bias, Stack Exchange, Information Retrieval (cs.IR), Computer Science - Information Retrieval

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2504.20610
https://hdl.handle.net/11583/2999757
https://doi.org/10.1145/3726302.3730008
https://dl.acm.org/doi/pdf/10.1145/3726302.3730008

SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Data Generation for Personalized Tourism Recommenders

Autoren: Ashmi Banerjee Adithi Satish Fitri Nur Aisyah et al.

Quelle: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. :3743-3752

Schlagwörter: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Information Retrieval (cs.IR), ddc, Computer Science - Information Retrieval

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2504.09277
https://mediatum.ub.tum.de/1833012

Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval

Autoren: Arthur Satouf Gabriel Ben-Zenou Benjamin Piwowarski et al.

Weitere Verfasser: Arthur Satouf Gabriel Ben-Zenou Benjamin Piwowarski et al.

Quelle: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. :2674-2678

Schlagwörter: FOS: Computer and information sciences, H.3, Rational Speech Acts, Computer Science - Computation and Language, 68P20, 68T50, [SHS.LANGUE] Humanities and Social Sciences/Linguistics, Neural Information Retrieval, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Computer Science - Information Retrieval, Sparse Retrieval, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Pragmatic Reasoning, Linguistic Pragmatics for Retrieval, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Computation and Language (cs.CL), Information Retrieval (cs.IR)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2505.03676
https://hal.science/hal-05074220v1
https://hal.science/hal-05074220v1/document
https://doi.org/10.1145/3726302.3730239

Efficient Conversational Search via Topical Locality in Dense Retrieval

Autoren: Cristina Ioana Muntean Franco Maria Nardini Raffaele Perego et al.

Quelle: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. :2749-2753

Schlagwörter: FOS: Computer and information sciences, H.3, Computer Science - Human-Computer Interaction, Dense retrieval models, Conversational search, Efficiency, Information Retrieval (cs.IR), Computer Science - Information Retrieval, Human-Computer Interaction (cs.HC)

Zugangs-URL: http://arxiv.org/abs/2504.21507
https://dl.acm.org/doi/10.1145/3726302.3730186
https://doi.org/10.1145/3726302.3730186
https://hdl.handle.net/20.500.14243/555889
https://hdl.handle.net/11568/1317868
https://doi.org/10.1145/3726302.3730186

Revisiting Algorithmic Audits of TikTok: Poor Reproducibility and Short-term Validity of Findings

Autoren: Matej Mosnar Adam Skurla Branislav Pecher et al.

Quelle: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval

Schlagwörter: Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Social and Information Networks, Information Retrieval (cs.IR), Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2504.18140

Towards Lossless Token Pruning in Late-Interaction Retrieval Models

Autoren: Yuxuan Zong Benjamin Piwowarski ZONG, Yuxuan

Weitere Verfasser: Yuxuan Zong Benjamin Piwowarski ZONG, Yuxuan

Quelle: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. :2407-2417

Schlagwörter: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, CCS Concepts, Multi-vector Retrieval, Computer Science - Computation and Language, Efficiency-Effectiveness Trade-off, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, CCS Concepts Information systems → Document representation Retrieval models and ranking Information retrieval Information Retrieval, Artificial Intelligence (cs.AI), [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Information retrieval Information Retrieval, Dense Retrieval, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Information systems → Document representation, Retrieval models and ranking, Computation and Language (cs.CL), Information Retrieval (cs.IR)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2504.12778

Effective Inference-Free Retrieval for Learned Sparse Representations

Autoren: Franco Maria Nardini Thong Nguyen Cosimo Rulli et al.

Quelle: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. :2936-2940

Schlagwörter: FOS: Computer and information sciences, Information Retrieval (cs.IR), Learned Sparse Retrieval, Inference-Free, Efficiency, Computer Science - Information Retrieval

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2505.01452
https://dl.acm.org/doi/abs/10.1145/3726302.3730185
https://doi.org/10.1145/3726302.3730185
https://hdl.handle.net/20.500.14243/549723

Position-aware Graph Transformer for Recommendation

Autoren: Jiajia Chen Jiancan Wu Jiawei Chen et al.

Quelle: ACM Transactions on Information Systems. 43:1-24

Schlagwörter: FOS: Computer and information sciences, Information Retrieval (cs.IR), Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2412.18731

FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services

Autoren: Wei Yuan Chaoqun Yang Guanhua Ye et al.

Quelle: ACM Transactions on Information Systems. 43:1-24

Schlagwörter: FOS: Computer and information sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Information Retrieval (cs.IR), Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2410.04927

Mitigating Propensity Bias of Large Language Models for Recommender Systems

Autoren: Guixian Zhang Guan Yuan Debo Cheng et al.

Quelle: ACM Transactions on Information Systems. 43:1-26

Schlagwörter: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Information Retrieval (cs.IR), Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2409.20052

Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-Agent LLMs

Autoren: Xiaopeng Li Lixin Su Pengyue Jia et al.

Quelle: ACM Transactions on Information Systems. 43:1-33

Schlagwörter: FOS: Computer and information sciences, Information Retrieval (cs.IR), Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2312.15450

Causality-Inspired Fair Representation Learning for Multimodal Recommendation

Autoren: Weixin Chen Li Chen Yongxin Ni et al.

Quelle: ACM Transactions on Information Systems. 43:1-29

Schlagwörter: FOS: Computer and information sciences, Information Retrieval (cs.IR), Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2310.17373

RADAR: Recall Augmentation through Deferred Asynchronous Retrieval

Autoren: Amit Jaspal Qian Dang Ajantha Ramineni

Quelle: Proceedings of the Nineteenth ACM Conference on Recommender Systems. :1046-1049

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Information Retrieval (cs.IR), Computer Science - Information Retrieval, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2506.07261

The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems

Autoren: Petr Kasalický Martin Spišák Vojtěch Vančura et al.

Quelle: Proceedings of the Nineteenth ACM Conference on Recommender Systems. :1099-1103

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Information Retrieval (cs.IR), Computer Science - Information Retrieval, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2505.11388

Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives

Autoren: Emrul Hasan Mizanur Rahman Chen Ding et al.

Quelle: ACM Computing Surveys. 58:1-41

Schlagwörter: FOS: Computer and information sciences, Information Retrieval (cs.IR), Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2405.05562

A Comprehensive Survey on Self-Supervised Learning for Recommendation

Autoren: Xubin Ren Wei Wei Lianghao Xia et al.

Quelle: ACM Computing Surveys. 58:1-38

Schlagwörter: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Information Retrieval (cs.IR), Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2404.03354

Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions

Autoren: Cheng-Te Li Yu-Che Tsai Chih-Yao Chen et al.

Quelle: ACM Computing Surveys. 58:1-51

Schlagwörter: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Social and Information Networks, Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2401.02143