Doctoral Symposium – Societal RecSys II

Date: Monday October 14
Time: 14:30-16:00
Location: Room H

• DSBridging Viewpoints in News with Recommender Systems
  by Jia Hua Jeng (MediaFutures, University of Bergen)

  News Recommender systems (NRSs) aid in decision-making in news media. However, undesired effects can emerge. Among these are selective exposures that may contribute to polarization, potentially reinforcing existing attitudes through belief perseverance—discounting contrary evidence due to their opposing attitudinal strength. This can be unsafe for people, making it difficult to accept information objectively. A crucial issue in news recommender system research is how to mitigate these undesired effects by designing recommender interfaces and machine learning models that enable people to consider to be more open to different perspectives. Alongside accurate models, the user experience is an equally important measure. Indeed, the core statistics are based on users’ behaviors and experiences in this research project. Therefore, this research agenda aims to steer the choices of readers’ based on altering their attitudes. The core methods plan to concentrate on the interface design and ML model building involving manipulations of cues, users’ behaviors prediction, NRSs algorithm and changing the nudges. In sum, the project aims to provide insight in the extent to which news recommender systems can be effective in mitigating polarized opinions.

• DSAI-based Human-Centered Recommender Systems: Empirical Experiments and Research Infrastructure
  by Ruixuan Sun (University of Minnesota)

  This is a dissertation plan built around human-centered empirical experiments evaluating recommender systems (RecSys). We see this as an important research theme since many AI-based RecSys algorithmic studies lack real human assessment. Therefore, we do not know how they work in the wild that only human experiments can tell us. We split this extended abstract into two parts – 1) A series of individual studies focusing on open questions about different human values or recommendation algorithms. Our completed works include user control over content diversity, user appreciation on DL-RecSys algorithms, and human-LLMRec interaction study. We also propose three future works to understand news recommendation depolarization, personalized news podcast, and interactive user representation; 2) An experimentation infrastructure named POPROX. As a personalized news recommendation platform, it aims to support the longitudinal study needs from the general AI and RecSys research community.

• DSBias in Book Recommendation
  by Savvina Daniil (CWI)

  Recommender systems are prevalent in many applications, but hide risks; issues like bias propagation have been on the focus of related studies in recent years. My own research revolves around tracking bias in the book recommendation domain. Specifically, I am interested in whether the incorporation of recommender systems in a library’s loaning system serves their social responsibility and purpose, with bias being the main point of concern. To this end, I engage with the topic in three ways; by mapping the area of ethics in book recommendation, by investigating and reflecting on challenges with studying bias in recommender systems in general, and by showcasing a set of social implication of statistical bias in the book recommendation domain in particular. In this doctoral symposium paper, I further elaborate on the problem at hand, the outline of my thesis, the progress I have made so far, as well as my plans for future work along with specific questions that have arisen from my research efforts.

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