Learning to Rank for Recommender Systems

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Recommender Architectures

Recommender system aim at providing a personalized list of items ranked according to the preferences of the user, as such ranking methods are at the core of many recommendation algorithms. The topic of this tutorial focuses on the cutting-edge algorithmic development in the area of recommender systems. This tutorial will provide an in depth picture of the progress of ranking models in the field, summarizing the strengths and weaknesses of existing methods, and discussing open issues that could be promising for future research in the community. A qualitative and quantitative comparison between different models will be provided while we will also highlight recent developments in the areas of Reinforcement Learning.

Introduction. Recommender systems aim to provide users with personalized items, which are typically ranked in a descending order of predicted relevance [1]. Learning the personalized recommendation list can be cast as a ranking problem. Naturally this cutting-edge research topic, Learning to Rank (LtR) [4], has already attracted a lot of attention in the Information Retrieval and Machine Learning communities. Recent contributions to collaborative filtering (CF) have exploited LtR techniques for improving the ranking of the top-N recommendations. In this tutorial, we present the key ideas of different categories of learning to rank approaches, and demonstrate examples that extend these ideas to specific CF meth- ods. We also discuss a few open issues that remain challenging for future research in this direction.

Discussion / Conclusion. 3. OBJECTIVES The topic of this tutorial focuses on the cutting-edge algorithmic development in the area of recommender systems. This tutorial would bring a big picture of the progress of ranking models in the field, summarizing the strengths and weaknesses of existing methods, and discussing open issues that would be promising for the future research in the community. The tutorial is intended for researchers and practitioners in the area of recommender systems, especially those who are interested in recommendation algorithms.

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How do aggregate reward models systematically fail to capture diverse human preferences? Why do accuracy-optimized recommender systems fail to preserve diverse interests? Can synthetic data maintain sufficient diversity to prevent model performance collapse? What determines whether LLM recommenders outperform collaborative filtering approaches? Does prompt engineering fix reasoning problems or only shift systematic biases? How should recommendation systems balance model complexity, coverage, and concept drift? How do feedback loops and selection biases compound to distort aggregate ratings in review networks? When and how should systems adaptively retrieve information during reasoning? Why do persistent memory systems hurt continual learning performance? Does disclosing AI authorship increase or decrease reader trust and persuasion? What drives sycophancy in AI models and how can we reduce it? Why do LLMs excel at generating but assessing content poorly?