Session 2: Models that Reflect Us: The Focus on Users’ Interests and Preferences on the Recommendation Process

Date: Tuesday September 23, 14:00–15:30 (GMT+2)
Session Chair: Elisabeth Lex

• RESA Multi-Factor Collaborative Prediction for Review-based Recommendation
  by Junrui Liu, Tong Li, Mingliang Yu, Shiqiu Yang, Zifang Tang, Zhen Yang

  For items, the higher the click-through rate, the higher the rating. Thus, existing recommendation methods implicitly model click behaviors by learning user preferences and achieving accurate predictions on rating prediction tasks. However, they ignore the help of the rating behaviors for the click-through rate prediction task (CTR). Although the rating behavior occurs after the click behavior, we can still get helpful information about clicks from ratings. In this paper, we propose a multi-factor collaborative prediction method (MFC), which mines the complex relationship between click and rating behaviors, achieving accurate prediction on CTR tasks. Specifically, we factorize the complex relationship into three simple relationships, i.e., linear, sharing, and cross-correlation relationships. Thus, MFC first extracts click factors, rating factors, and their sharing factor from user click and rating behaviors with user reviews, as review-based methods have achieved great results on rating predictions. Then, a rating factor regularization method is used to learn rating factors accurately, helping to model the true relationships between click and rating behavior. Finally, MFC combines those three factors to make predictions, while click and rating factors are used to model the linear and cross-correlation relationships, and the sharing factors correspond to the sharing relation. Experiments on five real-world datasets demonstrate that MFC outperforms the best baseline in terms of Accuracy, Precision, Recall, and F1-score, respectively. The source code is available at https://github.com/dianziliu/MFC .

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• RESA Non-Parametric Choice Model That Learns How Users Choose Between Recommended Options
  by Thorsten Krause, Harrie Oosterhuis

  Choice models predict which items users choose from presented options. In recommendation settings, they can infer user preferences while countering exposure bias. In contrast with traditional univariate recommendation models, choice models consider which competitors appeared with the chosen item. This ability allows them to distinguish whether a user chose an item due to preference, i.e., they liked it; or competition, i.e., it was the best available option. Each choice model assumes specific user behavior, e.g., the multinomial logit model. However, it is currently unclear how accurately these assumptions capture actual user behavior, how wrong assumptions impact inference, and whether better models exist. In this work, we propose the learned choice model for recommendation (LCM4Rec), a non-parametric method for estimating the choice model. By applying kernel density estimation, LCM4Rec infers the most likely error distribution that describes the effect of inter-item cannibalization and thereby characterizes the users’ choice model. Thus, it simultaneously infers what users prefer and how they make choices. Our experimental results indicate that our method (i) can accurately recover the choice model underlying a dataset; (ii) provides robust user preference inference, in contrast with existing choice models that are only effective when their assumptions match user behavior; and (iii) is more resistant against exposure bias than existing choice models. Thereby, we show that learning choice models, instead of assuming them, can produce more robust predictions. We believe this work provides an important step towards better understanding users’ choice behavior.

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• RESAuditing Recommender Systems for User Empowerment in Very Large Online Platforms under the Digital Services Act
  by Matteo Fabbri, Ludovico Boratto

  The governance of recommender systems (RSs) in very large online platforms (VLOPs) is expected to change significantly under the Digital Services Act (DSA), which imposes new obligations on transparency and user control. However, beyond legal compliance, a critical question remains: How can recommender systems be redesigned to genuinely empower users and foster meaningful personalization? This paper addresses this question by analyzing how three major short-video platforms—Instagram, TikTok, and YouTube—have implemented the DSA requirements for RSs. By reviewing their audit reports, systemic risk assessments, and compliance strategies, we evaluate the extent to which current approaches enhance user autonomy and control over content exposure. Building on this analysis, we outline a perspective for the future of VLOPs’ RSs grounded in speculative design. We argue that meaningful personalization should integrate algorithmic choice, balancing proportionality and granularity in RS customization, and content curation, ensuring diversity and authoritativeness to mitigate systemic risks. By bridging legal analysis, platform governance, and user-centered design, this paper outlines actionable pathways for aligning technical developments with regulatory objectives. Our findings contribute to interdisciplinary research on RSs by highlighting how platforms can move beyond minimal compliance toward a model that prioritizes user empowerment and content pluralism.

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• RESHow Do Users Perceive Recommender Systems’ Objectives?
  by Patrik Dokoupil, Ludovico Boratto, Ladislav Peska

  Multi-objective recommender systems (MORS) aim to optimize multiple criteria while generating recommendations, such as relevance, novelty, diversity, or exploration. These algorithms are based on the assumption that an operationalization of these criteria (i.e., translating abstract goals into measurable metrics), will reflect how users perceive them. Nevertheless, such beliefs are rarely rigorously evaluated, which can lead to a mismatch between algorithmic goals and user satisfaction. Moreover, if users are allowed to control the RS via their propensities towards such objectives, the misconceptions may further impact users’ trust and engagement. To characterize this problem, we conduct a large user study focusing on recommender systems in two domains: books and movies. Part of the study is focused on how users perceive different recommendation objectives, which we compared with well-established metrics aiming at the same objectives. We found that despite such metrics correlating to some extent with users’ perceptions, the mapping is far from perfect. Moreover, we also report on conceptual-level differences in users’ understanding of RS objectives and how this affects the results. Study data are available from https://osf.io/2n9mf/.

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• RESLONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders
  by Zheng Chai, Qin Ren, Xijun Xiao, Huizhi Yang, Bo Han, Sijun Zhang, Di Chen, Hui Lu, Wenlin Zhao, Lele Yu, Xionghang Xie, Shiru Ren, Xiang Sun, Yaocheng Tan, Peng Xu, Yuchao Zheng, Di Wu

  Modeling ultra-long user behavior sequences is critical for capturing both long- and short-term preferences in industrial recommender systems. Existing solutions typically rely on two-stage or indirect modeling paradigms, incurring upstream-downstream inconsistency and computational inefficiency. In this paper, we present LONGER, a Long-sequence Optimized traNsformer for GPU-Efficient Recommenders. LONGER incorporates (i) a global token mechanism for stabilizing attention over long contexts, (ii) a token merge module with lightweight InnerTransformers and hybrid attention strategy to reduce quadratic complexity, and (iii) a series of engineering optimizations, including training with mixed-precision and activation recomputation, KV cache serving, and the fully synchronous model training and serving framework for unified GPU-based dense and sparse parameter updates. LONGER consistently outperforms strong baselines in both offline metrics and online A/B testing in both advertising and e-commerce services at ByteDance, validating its consistent effectiveness and industrial-level scaling laws. Currently, LONGER has been validated and fully deployed across dozens of real-world influential scenarios at ByteDance, serving billions of users.

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• RESOff-Policy Evaluation and Learning for Matching Markets
  by Yudai Hayashi, Shuhei Goda, Yuta Saito

  Matching users based on mutual preferences is a fundamental aspect of services driven by reciprocal recommendations, such as job search and dating applications. Although A/B tests remain the gold standard for evaluating new policies in recommender systems for matching markets, it is costly and impractical for frequent policy updates. Off-Policy Evaluation (OPE) thus plays a crucial role by enabling the evaluation of recommendation policies using only offline logged data naturally collected on the platform. However, unlike conventional recommendation settings, the large scale and bidirectional nature of user interactions in matching platforms introduce variance issues and exacerbate reward sparsity, making standard OPE methods unreliable. To address these challenges and facilitate effective offline evaluation, we propose novel OPE estimators, DiPS and DPR, specifically designed for matching markets. Our methods combine elements of the Direct Method (DM), Inverse Propensity Score (IPS), and Doubly Robust (DR) estimators while incorporating intermediate labels, such as initial engagement signals, to achieve better bias-variance control in matching markets. Theoretically, we derive the bias and variance of the proposed estimators and demonstrate their advantages over conventional methods. Furthermore, we show that these estimators can be seamlessly extended to offline policy learning methods for improving recommendation policies for making more matches. We empirically evaluate our methods through experiments on both synthetic data and A/B testing logs from a real job-matching platform. The empirical results highlight the superiority of our approach over existing methods in off-policy evaluation and learning tasks for a variety of configurations.

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• RESParagon: Parameter Generation for Controllable Multi-Task Recommendation
  by Chenglei Shen, Jiahao Zhao, Xiao Zhang, Weijie Yu, Ming He, Jianping Fan

  Commercial recommender systems face the challenge that task requirements from platforms or users often change dynamically (e.g., varying preferences for accuracy or diversity). Ideally, the model should be re-trained after resetting a new objective function, adapting to these changes in task requirements. However, in practice, the high computational costs associated with retraining make this process impractical for models already deployed to online environments. This raises a new challenging problem: how to efficiently adapt the learned model to different task requirements by controlling the model parameters after deployment, without the need for retraining. To address this issue, we propose a novel controllable learning approach via parameter generation for controllable multi-task recommendation (Paragon), which allows the customization and adaptation of recommendation model parameters to new task requirements without retraining. Specifically, we first obtain the optimized model parameters through adapter tunning based on the feasible task requirements. Then, we utilize the generative model as a parameter generator, employing classifier-free guidance in conditional training to learn the distribution of optimized model parameters under various task requirements. Finally, the parameter generator is applied to effectively generate model parameters in a test-time adaptation manner given task requirements. Moreover, Paragon seamlessly integrates with various existing recommendation models to enhance their controllability. Extensive experiments on two public datasets and one commercial dataset demonstrate that Paragon can efficiently generate model parameters instead of retraining, reducing computational time by at least 94.6%. The code is released at https://anonymous.4open.science/r/Paragon-C726.

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• RESRecommendation and Temptation
  by Md Sanzeed Anwar, Paramveer S. Dhillon, Grant Schoenebeck

  Traditional recommender systems based on revealed preferences often fail to capture the fundamental duality in user behavior, where consumption choices are driven by both inherent value (enrichment) and instant appeal (temptation). Consequently, these systems may generate recommendations that prioritize short-term engagement over long-lasting user satisfaction. We propose a novel recommender design that explicitly models the tension between enrichment and temptation. We introduce a behavioral model that accounts for how both enrichment and temptation influence user choices, while incorporating the reality of off-platform alternatives. Building on this model, we formulate a novel recommendation objective aligned with maximizing consumed enrichment and prove the optimality of a locally greedy recommendation strategy. Finally, we present an estimation framework that leverages the distinction between explicit user feedback and implicit choice data while making minimal assumptions about off-platform options. Through comprehensive evaluation using both synthetic simulations and real-world data from the MovieLens dataset, we demonstrate that our approach consistently outperforms competitive baselines that ignore temptation dynamics either by assuming revealed preferences or recommending solely based on enrichment. Our work represents a paradigm shift toward more nuanced and user-centric recommender design, with significant implications for developing responsible AI systems that genuinely serve users’ long-term interests rather than merely maximizing engagement.

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• INDSEMORec: A Scalarized Efficient Multi-Objective Recommendation Framework
  by Sofia Maria Nikolakaki, Siyong Ma, Srivas Chennu, Humeyra Topcu Altintas

  Recommendation systems in multi-stakeholder environments often require optimizing for multiple objectives simultaneously to meet supplier and consumer demands. Serving recommendations in these settings relies on efficiently combining the objectives to address each stakeholder’s expectations, often through a scalarization function with pre-determined and fixed weights. In practice, selecting these weights becomes a consequent problem. Recent work has developed algorithms that adapt these weights based on application-specific needs by using RL to train a model [6]. While this solves for automatic weight computation, such approaches are not efficient for frequent weight adaptation. They also do not allow for human intervention oftentimes determined by business needs. To bridge this gap, we propose a novel multi-objective recommendation framework that is efficient for a small number of objectives. It also enables business decision makers to easily tune the optimization by assigning different importance to multiple objectives. We demonstrate the efficacy and efficiency of our framework through improvements in online business metrics.

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