Doctoral Symposium – Societal RecSys III

Date: Monday October 14
Time: 16:45-18:15
Location: Room H

• DSMultimodal Representation Learning for High-Quality Recommendations in Cold-Start and Beyond-Accuracy
  by Marta Moscati (Johannes Kepler University Linz)

  Recommender systems (RS) traditionally leverage the large amount of user–item interaction data. This exposes RS to a lower recommendation quality in cold-start scenarios, as well as to a low recommendation quality in terms of beyond-accuracy evaluation metrics. State-of-the-art (SotA) models for cold-start scenarios rely on the use of side information on the items or the users, therefore relating recommendation to multimodal machine learning (ML). However, the most recent techniques from multimodal ML are often not applied to the domain of recommendation. Additionally, the evaluation of SotA multimodal RS often neglects beyond-accuracy aspects of recommendation. In this work, we outline research into designing novel multimodal RS based on SotA multimodal ML architectures for cold-start recommendation, and their evaluation and benchmark with preexisting multimodal RS in terms of accuracy and beyond-accuracy aspects of recommendation quality.

• DSTowards Sustainable Recommendations in Urban Tourism
  by Pavel Merinov (Free University of Bozen-Bolzano)

• DSEnhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures
  by Thomas Elmar Kolb (TU Wien)

  The research domain of recommender systems is rapidly evolving. Initially, optimization efforts focused primarily on accuracy. However, recent research has highlighted the importance of addressing bias and beyond-accuracy measures such as novelty, diversity, and serendipity. With the rise of multi-domain recommender systems, the need to re-examine bias and beyond-accuracy measures in cross-domain settings has become crucial. Traditional methods face challenges such as cold-start problems, which can potentially be mitigated by leveraging LLMs. This proposed work investigates how LLM-based recommendation methods can enhance cross-domain recommender systems, focusing on identifying, measuring, and mitigating bias while evaluating the impact of beyond-accuracy measures. We aim to provide new insights by comparing traditional and LLM-based systems within a real-world environment encompassing the domains of news, books, and various lifestyle areas. Our research seeks to address the outlined gaps and develop effective evaluation strategies for the unique challenges posed by LLMs in cross-domain recommender systems.

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