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 exclude minority user preferences? What structural factors drive popularity bias in recommendation systems? How can humans calibrate appropriate trust in AI systems? How can LLM recommenders match or exceed collaborative filtering performance? Can prompting strategies overcome LLM biases without model fine-tuning? How do social dynamics and selection effects compound in rating aggregates? When should retrieval-augmented systems decide to fetch new information? What memory architectures best support persistent reasoning across extended interactions? How do we evaluate AI systems when user perception misleads actual performance? What mechanisms drive sycophancy and how can we mitigate it? What dimensions of recommendation quality do standard metrics miss? Why can LLMs generate ideas better than they evaluate them? How can recommendation systems balance personalization with stability and coverage? How should dialogue systems best leverage conversation history for retrieval?