A Causal View for Item-level Effect of Recommendation on User Preference

Cai, Wei; Feng, Fuli; Wang, Qifan; Tian, Yang; Liu, Zhenguang; Xu, Congfu

doi:10.1145/3539597.3570461

Cited by 7 publications

(4 citation statements)

References 34 publications

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“…To overcome the limitation of traditional RSs, proactive recommendation actively guides users to jump out historical interests based on the assumption that recommended items can affect user interests [2]. As Figure 1 shows, proactive recommendation begins by selecting a target item or target topic representing the expected interest and aims to actively guide user interests towards this target by adjusting recommendation lists.…”

Section: Introductionmentioning

confidence: 99%

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Proactive Recommendation with Iterative Preference Guidance

Bi,

Wang,

Pan

et al. 2024

Companion Proceedings of the ACM Web Conference 2024

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Recommender systems mainly tailor personalized recommendations according to user interests learned from user feedback. However, such recommender systems passively cater to user interests and even reinforce existing interests in the feedback loop, leading to problems like filter bubbles and opinion polarization. To counteract this, proactive recommendation actively steers users towards developing new interests in a target item or topic by strategically modulating recommendation sequences. Existing work for proactive recommendation faces significant hurdles: 1) overlooking the user feedback in the guidance process; 2) lacking explicit modeling of the guiding objective; and 3) insufficient flexibility for integration into existing industrial recommender systems. To address these issues, we introduce an Iterative Preference Guidance (IPG) framework. IPG performs proactive recommendation in a flexible post-processing manner by ranking items according to their IPG scores that consider both interaction probability and guiding value. These scores are explicitly estimated with iteratively updated user representation that considers the most recent user interactions. Extensive experiments validate that IPG can effectively guide user interests toward target interests with a reasonable trade-off in recommender accuracy. The code is available at https://github.com/GabyUSTC/IPG-Rec. CCS CONCEPTS• Information systems → Recommender systems.

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Section: Introductionmentioning

confidence: 99%

“…The guiding process unfolds iteratively in a multi-round manner. During each round, RS recommends items 2 to the user, triggering the evolution of user interests and interactions on the recommendation accordingly.…”

Section: Introductionmentioning

confidence: 99%

Proactive Recommendation with Iterative Preference Guidance

Bi,

Wang,

Pan

et al. 2024

Companion Proceedings of the ACM Web Conference 2024

Self Cite

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“…This reinforces user biases and leads to a reduction in news diversity. Cai et al (2023) address issues like echo chambers and filter bubbles caused by RSs by concentrating on estimating the effects of recommending specific items on user preferences. They propose a method based on causal graphs that mitigates confounding bias without requiring costly randomized control trials.…”

Section: Introductionmentioning

confidence: 99%

Filter bubbles in recommender systems: Fact or fallacy—A systematic review

Areeb

Nadeem

Sohail

et al. 2023

WIREs Data Min & Knowl

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A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems (RSs). This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in RSs. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in RSs. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in RSs, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and RSs. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area.This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Application Areas > Internet Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Security and Privacy

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“…Ashton et al [32] propose that high recommendation diversities is related to long-term user engagement. Apart from diversities, Cai et al [33] conduct large-scale empirical studies and propose several surrogate criteria for optimizing long-term user engagements, including high-quality consumption, repeated consumption, etc.…”

Section: Reinforcement Learning For Sequential Recommendationmentioning

confidence: 99%

Robust and adaptive decision-making: a reinforcement learning perspective

Xue

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A Causal View for Item-level Effect of Recommendation on User Preference

Cited by 7 publications

References 34 publications

Proactive Recommendation with Iterative Preference Guidance

Proactive Recommendation with Iterative Preference Guidance

Filter bubbles in recommender systems: Fact or fallacy—A systematic review

Robust and adaptive decision-making: a reinforcement learning perspective

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