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20172026
most citedTop-N Recommendation Algorithms: A Quest for the State-of-the-Art

51 citations · 180 across the 50 of their papers we have counts for

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55 papers · 1 filter

cs.IR2026

Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems

Xinyu Lin, Yashar Deldjoo, Sunhao Dai +7

The rapid integration of large language model-based agents into recommender systems has driven a shift from static, ranking-based pipelines toward autonomous and interactive system…

cs.IR2026

Popcorn: A Configurable Benchmark for Visual Evidence in Multimodal Movie Recommendation

Ali Tourani, Fatemeh Nazary, Yashar Deldjoo +1

Movies are long-form audiovisual works, yet recommender benchmarks often rely on trailers, thumbnails, or metadata. These sources differ in semantics and scalability: full movies p…

cs.IR2026

Training-free Graph-based Imputation of Missing Modalities in Multimodal Recommendation

Daniele Malitesta, Emanuele Rossi, Claudio Pomo +2

Multimodal recommender systems (RSs) represent items in the catalog through multimodal data (e.g., product images and descriptions) that, in some cases, might be noisy or (even wor…

cs.IR2026

A Reproducible and Fair Evaluation of Partition-aware Collaborative Filtering

Domenico de Gioia, Claudio Pomo, Ludovico Boratto +1

Similarity-based collaborative filtering (CF) models have long demonstrated strong offline performance and conceptual simplicity. However, their scalability is limited by the quadr…

cs.IR2026

Exploring Approaches for Detecting Memorization of Recommender System Data in Large Language Models

Antonio Colacicco, Vito Guida, Dario Di Palma +2

Large Language Models (LLMs) are increasingly applied in recommendation scenarios due to their strong natural language understanding and generation capabilities. However, they are…

cs.IR2026

Exploring Diversity, Novelty, and Popularity Bias in ChatGPT's Recommendations

Dario Di Palma, Giovanni Maria Biancofiore, Vito Walter Anelli +2

ChatGPT has emerged as a versatile tool, demonstrating capabilities across diverse domains. Given these successes, the Recommender Systems (RSs) community has begun investigating i…