Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders

Exploiting diffusion-based structured learning for item interactions representations in multimodal recommender systems

Khan, N., & Sisodia, D. S. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104075 · Cited by 21 · see citing works

Artificial intelligence security and privacy: a survey

He, X., Xu, G., Han, X., Wang, Q., Zhao, L., Shen, C., Lin, C., Zhao, Z., Li, Q., Yang, L., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-025-4388-5 · Cited by 23 · see citing works

Generative Recommender with End-to-End Learnable Item Tokenization

Liu, E., Zheng, B., Ling, C., Hu, L., Li, H., & Zhao, W. X. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729989 · Cited by 13 · see citing works

Enhanced twitter sentiment analysis with dual joint classifier integrating RoBERTa and BERT architectures

He, L. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1477714 · Cited by 16 · see citing works

An AI ‐Assisted Framework for Improving Innovativeness in Small Businesses: A Human– AI Collaboration Perspective

Mirkovski, K., Williams, P., Liu, L., Liu, H., & Indulska, M. (2025) · Information Systems Journal · doi:10.1111/isj.12597 · Cited by 12 · see citing works

PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation

Ma, C., Xu, Z., Ren, Y., Hettiachchi, D., & Chan, J. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730238 · Cited by 10 · see citing works

Sequential recommender systems: A methodological taxonomy and research frontiers

Zhou, Y., Ma, G., Wen, X., Yang, X.-H., & Zhang, Y. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100818 · Cited by 8 · see citing works

Recall, Robustness, and Lexicographic Evaluation

Díaz, F., Ekstrand, M. D., & Mitra, B. (2025) · ACM Transactions on Recommender Systems · doi:10.1145/3728373 · Cited by 8 · see citing works

ADAPT: Fairness & diversity for sequential group recommendations

Lenzi, E., & Stefanidis, K. (2025) · Information Systems · doi:10.1016/j.is.2025.102572 · Cited by 6 · see citing works

D ata R ec : A Python Library for Standardized and Reproducible Data Management in Recommender Systems

Mancino, A. C. M., Bufi, S., Fazio, A. D., Ferrara, A., Malitesta, D., Pomo, C., & Noia, T. D. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730320 · Cited by 6 · see citing works

Bridging Textual-Collaborative Gap through Semantic Codes for Sequential Recommendation

Liu, E., Zheng, B., Zhao, W. X., & Wen, J.-R. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736869 · Cited by 7 · see citing works

Dictionary Multi-Modal Temporal Graph Learning

Liu, M., Liang, K., Li, M., Zhu, X., & Liu, X. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3679419 · Cited by 4 · see citing works

Text-attributed community detection in complex networks through LLMs and GNNs: A powerful fusion of language and graphs

Sruthi, K. S., Sreekumar, A., & Balakrishnan, K. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130573 · Cited by 4 · see citing works

A data-filtered sentiment analysis model for economic forecasting

Zheng, W., & Hara, K. (2025) · Decision Analytics Journal · doi:10.1016/j.dajour.2025.100629 · Cited by 3 · see citing works

Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation

Zheng, B., Zhang, J., Lu, H., Chen, Y., Chen, M.-G., Zhao, W. X., & Wen, J.-R. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709214 · Cited by 3 · see citing works

CoSRec: A Joint Conversational Search and Recommendation Dataset

Alessio, M., Merlo, S., Noia, T. D., Faggioli, G., Ferrante, M., Ferro, N., Muntean, C. I., Nardini, F. M., Narducci, F., Perego, R., et al. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730319 · Cited by 2 · see citing works

The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems

Maragheh, R. Y., & Deldjoo, Y. (2026) · ACM Transactions on Recommender Systems · doi:10.1145/3845988 · Cited by 14 · see citing works

E-GEO: A Testbed for Generative Engine Optimization in E-Commerce

Bagga, P. S., Farias, V. F., Korkotashvili, T., Peng, T., & Wu, Y. (2025) · arXiv · doi:10.48550/arxiv.2511.20867 · Cited by 1 · see citing works

Personalized Recommendation Agents with Self-Consistency

Wu, Z., Sheng, L., Xia, Y., Zhang, Y., Chen, Y., & Zhang, A. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3719229 · Cited by 1 · see citing works

A Review of Machine Learning Algorithms for the Amazon-M2 Dataset

Hsieh, C. H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93965-5_14 · Cited by 1 · see citing works

ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation

Kim, S., Lee, G., Kim, K.-H., Yoo, J., & Shin, K. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809942 · Cited by 1 · see citing works

CoSID: Concept-Conditioned Semantic-ID Decoding for Efficient Generative Recommendation

Gusak, D., Volodkevich, A., & Frolov, E. (2026) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3773078.3841283 · Cited by 1 · see citing works

Collaborative Optimization Approach for Workflow Agents in User Behavior Modeling

Zhang, X., Dou, R., Hu, E., Zhao, M., Ding, Y., & Dou, Z. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3719228 · Cited by 1 · see citing works

MemoryCD: Benchmarking Long-Context User Memory of LLM Agents for Lifelong Cross-Domain Personalization

Zhang, W., Wei, X., Huang, W.-C., Hui, Z., Wang, C., Gong, M., & Yu, P. S. (2026) · arXiv · doi:10.48550/arxiv.2603.25973

MimicAI: An LLM based system that mimics and explains

Sharma, U., & Ayachitula, S. (2024) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi62246.2024.00022

Document Valuation in LLM Summaries: A Cluster Shapley ApproachPreprint

Ye, Z., & Yoganarasimhan, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5255982

Comparing Llm-Based Topic Modeling and Sentiment Analysis with Human Evaluation of Consumer InsightsPreprint

Ponnusamy, M., Sonvani, T., Traylor, B., & Nanda, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5362200

A Comparative Study of Ensemble Learning Methodologies for Multilingual Sentiment Analysis

Ansari, F., Saxena, N., & Kulkarni, A. (2025) · doi:10.1109/icarai67046.2025.11137864

Autonomous Reasoning-Retrieval for Large Language Model Based Recommendation

Zheng, B., Wang, X., Liu, E., Wang, X., Lu, H., Chen, Y., Zhao, W. X., & Wen, J.-R. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761384

A Systematic Reproducibility Study of BSARec for Sequential Recommendation

Hutter, J., Bakker, H. C., Fris, S., Bernardy, M., & Liu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21324-2_2

Explanation-as-Signal: A Two-way Human-AI Feedback Loop for Mitigating Hallucinations in Text-to-Knowledge Transformation

Zablith, F., & Abbas, J. (2026) · Information Systems Frontiers · doi:10.1007/s10796-026-10738-y

A Comprehensive Review of Generative Artificial Intelligence in E-Commerce Applications

Rayavarapu, S. M., & Rao, G. V. R. K. (2025) · IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) · doi:10.1109/icccmla66092.2025.11580669

AI-NativeBench: An Open-Source White-Box Agentic Benchmark Suite for AI-Native Systems

Wang, Z., Yu, G., & Lyu, M. R.-T. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3821576

GLiNER-Reg: Generalized text regression using reference prompts

Suwannapichat, P., Changaival, B., Wu, C., & Bouvry, P. (2026) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse68839.2026.11596623

Breaking the Relevance–Diversity Seesaw: Hierarchical LLM Reasoning with RL for Industrial Novelty Recommendation

Sun, Y. W., Zou, Y., Wang, X., Xu, H., Yang, X., Xu, S., Qi, J., & Li, S. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3808399

Beyond the Lookup: Simulating Realistic User Uncertainty for the Evaluation of Conversational Agentic Recommenders

Petruzzelli, A., Martina, A. F. M., Musto, C., de Gemmis, M., Lops, P., & Semeraro, G. M. (2026) · Information Systems Frontiers · doi:10.1007/s10796-026-10787-3

BayesDR-LCB: Bayesian Doubly Robust Off-Policy Evaluation with Posterior Lower Confidence Bounds for Industrial Recommender Systems

Yan, R. (2026) · International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) · doi:10.1109/mlise70044.2026.11607685

Beyond Time as Context: Joint Item-Time Prediction for Sequential Recommendation

Huang, Y., Wang, P., Sun, L., Liu, C., Zhu, T., & Lv, W. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3717641

Attribution-guided prompting repairs explanation faithfulness without improving recommendation validityPreprint

Alvarez, M. A. C., Romero, K. Z., Heredia, J. O., Garcia, S., Mackenzie, A. F. R., & Herrera-García, U. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7240158

Do Retrieval-Trained Embeddings Help Linear Contextual Bandits?

Canim, M. (2026) · Mathematics · doi:10.3390/math14162874

Explainable Customer Feedback Analysis Using Graph-Based Orchestration and Emotion-Aware LLM Reasoning

Chennupati, P. K., Varanasi, V. S. V., Fakruddin, S., Yarlagadda, E. A., Devi, J. R., & Manasa, B. (2026) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon69332.2026.11629851

Two-Stage Graph Contrastive Distillation Recommendation Model

Yu, Z., Huang, Z., & Hu, J. (2026) · IEEE Access · doi:10.1109/access.2026.3722716

Answering Federated Range Queries with Local Differential Privacy

Zhang, Y., Ye, Q., Liu, J., & Dong, W. (2026) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65706.2026.00216

Comparative Evaluation of Generative AI Models for e-Commerce Product Recommendation

Liau, K.-Z., Haw, S.-C., Naveen, P., & Jayapradha, J. (2026) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd69640.2026.11637199

From Technical SEO to AI Readiness: Clustering E-Commerce Platforms for Generative and Answer Based Search

Kašparová, M., Bílková, R., & Meduna, D. (2026) · Zooming Innovation in Consumer Technologies Conference (ZINC) · doi:10.1109/zinc69910.2026.11655811

Mix-Attention for Multimodal Large Language Model-Enhanced Sequential Recommendation

Yang, S., Guo, Z., Ma, W., Zhang, M., Guo, H., & Wang, L. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3727279

Behavior-Evidence-Based Dynamic User Feature Calibration for Unified-Token Click-Through Rate Prediction

Wang, M., Wang, C., & Gao, Y. (2026) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia71496.2026.11684538

Enhancing Collaborative Filtering With Large Language Model Embeddings for User Cold-Start Recommendation

Rao, E. R. N., Koundinya, N. S., Veeramachaneni, S. D., & Pujari, A. K. (2026) · IEEE International Conference on Pattern Recognition and Machine Learning (PRML) · doi:10.1109/prml71097.2026.11687003

Stabilizing Stability and Plasticity in Graph-based Continual Recommender System

Chen, Y., Wang, X., & Li, Q. (2026) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3773078.3831837

In-Batch Negatives Can Silently Cripple LLM-Encoded Sequential Recommenders

Bae, Y. (2026) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3773078.3841255

Optimizing Collaborative Filtering in Very Sparse Datasets: a Confidence-based Approach

Stouraiti, V., Mpardis, G., Roumeliotis, K. I., Vassilakis, C., Tryfonopoulos, C., & Spiliotopoulos, D. (2026) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa71656.2026.11705472

Unveiling Structural Distributional Bias in Collaborative Filtering for Ultra-Sparse Datasets

Nanos, J., Margaris, D., Mpardis, G., Roumeliotis, K. I., Vassilakis, C., & Spiliotopoulos, D. (2026) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa71656.2026.11705590

Objective-Aligned Visual Neural Collaborative Filtering for Item Cold-Start Recommendation: Disentangling Loss from Architecture

Tran, N. B., Nguyen, H. Q. H., & Tran, H. L. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.20469

Two Timescales, Two Calibers: Auditing Consumer Adaptation in Algorithmically Mediated Environments

鲁丹, Liu, H., Yu, X., & Shi, D. (2026) · Information · doi:10.3390/info17100985

Eigen-Value: Efficient Domain-Robust Data Valuation via Eigenvalue-Based Approach

Choi, Y., Kang, J., Lim, S., & Song, K. (2026) · doi:10.1109/cvprf71909.2026.00233

A Dual-Process Memory-Augmented Large Language Model Agent for Explainable Sequential Recommendation

Wang, G., Li, P., Feng, E., Shen, A., & Rhee, M. (2026) · IEEE Access · doi:10.1109/access.2026.3741894

BASG: A Behavior-Augmented Semantic Generalization Framework for Zero-Shot Cross-Domain Sequential Recommendation

Pan, Z., Li, P., & Li, Y. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10100344

Predicting Review Helpfulness in Online Cosmetic and Personal Care Shopping

Kim, H. (2026) · Journal of the Korean Society of Clothing and Textiles · doi:10.5850/jksct.2026.50.4.824

AutoML-Based Fake Review Detection: A Unified Textual and Multimodal Learning Framework

Gedara, T. M. H., Loffredo, R., & Arachchige, N. M. K. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11728669

TEG-Rec: Scene-driven Thought-Evolution Generative Recommender

Xu, C., & Zhang, Z. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729583

Diversity-Oriented Interaction Augmentation for Multimodal Recommendation

Lu, J., & Yamashita, H. (2026) · IEEE Access · doi:10.1109/access.2026.3744680

Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation

Wang, S., Fan, W., Feng, Y., Lin, S., Ma, X., Wang, S., & Yin, D. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1317 · Cited by 62 · see citing works

Item-Language Model: Improving Large Language Model for Recommendation via Item-Language Representation Learning

Yang, L., Subbiah, A., Patel, H., Li, J. Y., Song, Y., Mirghaderi, R., Aggarwal, V., Feng, F., Xu, Z., Liu, D., et al. (2025) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2025.ijcnlp-long.19

Multilingual Universal Sentence Encoder for Semantic Retrieval

Yang, Y., Cer, D. M., Ahmad, A., Guo, M., Law, J., Constant, N., Ábrego, G. H., Yuan, S., Tar, C., Sung, Y.-H., et al. (2020) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2020.acl-demos.12 · Cited by 411 · see citing works

Multimodal Item Scoring for Natural Language Recommendation via Gaussian Process Regression with LLM Relevance Judgments

Liu, Y. S., Wen, Q., Liang, J., Zhao, M., Cui, J., Korikov, A., Toroghi, A., Kim, J., & Sanner, S. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.1836

SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval

Schmidt, F. D., Dietsche, M., Ponzetto, S. P., & Glavaš, G. (2019) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d19-3034 · Cited by 1 · see citing works