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 can reward models capture diverse human preferences without excluding minority populations? Do structural constraints outperform deep architectures in recommendation systems? How can we prevent synthetic data from contaminating statistical inference and corpora? Why do LLM recommenders underperform collaborative filtering despite their capabilities? How do prompting refinements mask underlying biases and model frequency patterns? How can persona-attention mechanisms improve both recommendation quality and explainability? What causes retrieval-augmented generation systems to fail despite access to external knowledge? Why does memory consolidation cause performance regression in continual learning? Why do people disclose to AI systems despite their artificial nature? Does transformer attention architecture inherently drive sycophancy? How do LLM judges' systematic biases affect alignment and evaluation outcomes? How do recommenders balance exploiting fresh signals against maintaining preference stability? How should retrieval systems handle complex multi-step reasoning?