Marialena Kyriakidi
I work in the areas of recommender systems, conversational search and machine learning ^_^ . Publications are listed on Google Scholar.
Authored Publications
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REGEN: A Dataset and Benchmarks with Natural Language Critiques and Narratives
Kun Su
Krishna Sayana
Hubert Pham
James Pine
Yuri Vasilevski
Raghavendra Vasudeva
Liam Hebert
Ambarish Jash
Anushya Subbiah
Sukhdeep Sodhi
(2025)
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This paper introduces a novel dataset REGEN (Reviews Enhanced with GEnerative Narratives), designed to benchmark the conversational capabilities of recommender Large Language Models (LLMs), addressing the limitations of existing datasets that primarily focus on sequential item prediction. REGEN extends the Amazon Product Reviews dataset by inpainting two key natural language features: (1) user critiques, representing user "steering" queries that lead to the selection of a subsequent item, and (2) narratives, rich textual outputs associated with each recommended item taking into account prior context. The narratives include product endorsements, purchase explanations, and summaries of user preferences.
Further, we establish an end-to-end modeling benchmark for the task of conversational recommendation, where models are trained to generate both recommendations and corresponding narratives conditioned on user history (items and critiques). For this joint task, we introduce a modeling framework LUMEN (LLM-based Unified Multi-task Model with Critiques, Recommendations, and Narratives) which uses an LLM as a backbone for critiquing, retrieval and generation. We also evaluate the dataset's quality using standard auto-rating techniques and benchmark it by training both traditional and LLM-based recommender models. Our results demonstrate that incorporating critiques enhances recommendation quality by enabling the recommender to learn language understanding and integrate it with recommendation signals. Furthermore, LLMs trained on our dataset effectively generate both recommendations and contextual narratives, achieving performance comparable to state-of-the-art recommenders and language models.
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Personalisation in digital ecomuseums: the case of Pros-Eleusis
Ektor Vrettakis
Akrivi Katifori
Myrto Koukouli
Maria Boile
Apostolos Glenis
Dimitra Petousi
Maria Vayanou
Yannis Ioannidis
MDPI , Applied Sciences (2023) (to appear)
Preview abstract
In comparison with a traditional museum, an “ecomuseum” is radically different: It is not housed in a building and does not have a collection of physical objects or artifacts. It aims to help visitors discover the tangible and intangible cultural heritage of a region through the identification of important points of interest (POIs), while offering a variety of activities and direct engagement with the region’s cultural identity. The diversity and amount of information that may be available through digital means highlight the need for supporting the visitor in selecting which POIs to visit by offering personalized content. In this paper, we present our approach for a recommendation system for an ecomuseum, through its application in the city of Eleusis, Greece. We present the approach from needs to implementation, as well as the results of a preliminary evaluation, showing promising results for its application as an engaging visitor experience for an ecomuseum. We conclude the paper with a wider discussion about personalization in this context and in a cultural heritage context in general.
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