Team

@inproceedings{10.1007/978-3-031-58839-6_26,

title = {Unlocking the Potential of Content-Based Restaurant Recommender Systems},

author = {Dante Godolja and Thomas Elmar Kolb and Julia Neidhardt},

editor = {Katerina Berezina and Lyndon Nixon and Aarni Tuomi},

isbn = {978-3-031-58839-6},

year  = {2024},

date = {2024-01-01},

booktitle = {Information and Communication Technologies in Tourism 2024},

pages = {239–244},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Content-based restaurant recommender systems use features such as cuisine type, price range, and location to suggest dining options to users. Current research explores ways to improve their effectiveness. In this work, we explore different ideas on how to build a recommender system. We explore TF-IDF as a baseline and the state-of-the-art model SBERT. These ideas are tested on a real-world data-set of a digital restaurant guide. Evaluation involves both qualitative assessment by a domain expert and quantitative analysis. The results show that, with proper preprocessing, TF-IDF can achieve similar scores to SBERT and, depending on the scenario, even better results. However, SBERT still provides more novel recommendations than TF-IDF. Depending on the scenario, both models can be used to generate meaningful restaurant recommendations. However, more implicit aspects like a restaurant's atmosphere can hardly be captured by these models.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_191170,

title = {Like a Skilled DJ - an Expert Study on News Recommendations Beyond Accuracy},

author = {Thomas Elmar Kolb and Irina Nalis-Neuner and Julia Neidhardt},

editor = {Benjamin Kille},

doi = {10.34726/5332},

year  = {2023},

date = {2023-01-01},

volume = {3561},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {In the past, recommender systems were primarily focused on optimizing accuracy. However, in recent years, there has been an increasing awareness that considerations beyond accuracy are necessary. The definition of what constitutes a good recommendation is a crucial issue. The most precise prediction may not always be the recommendation that satisfies the user best. This study offers a comprehensive investigation into the present advancements within the realm of beyond-accuracy measurements, especially the metrics diversity, serendipity, and novelty. Collaborative efforts between algorithmic models and domain experts can enrich recommendation quality, particularly in labeling and categorizing content. To address this, we present a study conducted by experts in the news domain. This study provides new insights into the multifaceted nature of this challenge. Employing an interdisciplinary approach, we underscore the significance of constructing a system that revolves around the user. Recent discussions about algorithmic content filtering and its societal implications underscore the importance of maintaining human involvement in the decision-making loop.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_191183,

title = {Potentials of Combining Local Knowledge and LLMs for Recommender Systems},

author = {Thomas Elmar Kolb and Ahmadou Wagne and Mete Sertkan and Julia Neidhardt},

editor = {Vito Walter Anelli and Pierpaolo Basile and Gerard De Melo and Francesco Donini and Antonio Ferrara and Cataldo Musto and Fedelucio Narducci and Azzurra Ragone and Markus Zanker},

doi = {10.34726/5334},

year  = {2023},

date = {2023-01-01},

volume = {3560},

pages = {61–64},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {LLMs have revolutionized the understanding and generation of natural language, offering new possibilities for enhancing recommendation systems. In previous studies, LLMs exploit their global knowledge to provide zero- or few-shot recommendations. In this work, we aim to highlight the opportunities that LLMs pose to enrich the field of recommender systems combined with local knowledge. We propose to view recommender systems combined with LLMs from a broader perspective, recognizing them not merely as another method to replace existing recommendation approaches, but rather as a complementary and powerful approach to enhance and augment the overall recommendation process.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_177094,

title = {The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers},

author = {Thomas Kolb and Katharina Sekanina and Bettina Manuela Johanna Kern and Julia Neidhardt and Tanja Wissik and Andreas Baumann},

doi = {10.34726/4101},

year  = {2022},

date = {2022-01-01},

pages = {4708–4716},

publisher = {European Language Resources Association},

address = {Marseille, France},

abstract = {This paper introduces the Austrian German sentiment dictionary ALPIN to account for the lack of resources for dictionary-based sentiment analysis in this specific variety of German, which is characterized by lexical idiosyncrasies that also affect word sentiment. The proposed language resource is based on Austrian news media in the field of politics, an austriacism list based on different resources and a posting data set based on a popular Austrian news media. Different resources are used to increase the diversity of the resulting language resource. Extensive crowd-sourcing is performed followed by evaluation and automatic conversion into sentiment scores. We show that crowd-sourcing enables the creation of a sentiment dictionary for the Austrian German domain. Additionally, the different parts of the sentiment dictionary are evaluated to show their impact on the resulting resource. Furthermore, the proposed dictionary is utilized in a web application and available for future research and free to use for anyone.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_150340,

title = {The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic},

author = {Thomas Elmar Kolb and Irina Nalis and Mete Sertkan and Julia Neidhardt},

editor = {Kolb Thomas},

doi = {10.48550/ARXIV.2209.07608},

year  = {2022},

date = {2022-01-01},

abstract = {News recommender systems (NRs) have been shown to shape public discourse and to enforce behaviors that have a critical, oftentimes detrimental effect on democracies. Earlier research on the impact of media bias has revealed their strong impact on opinions and preferences. Responsible NRs are supposed to have depolarizing capacities, once they go beyond accuracy measures. We performed sequence prediction by using the BERT4Rec algorithm to investigate the interplay of news of coverage and user behavior. Based on live data and training of a large data set from one news outlet "event bursts", "rally around the flag" effect and "filter bubbles" were investigated in our interdisciplinary approach between data science and psychology. Potentials for fair NRs that go beyond accuracy measures are outlined via training of the models with a large data set of articles, keywords, and user behavior. The development of the news coverage and user behavior of the COVID-19 pandemic from primarily medical to broader political content and debates was traced. Our study provides first insights for future development of responsible news recommendation that acknowledges user preferences while stimulating diversity and accountability instead of accuracy, only.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_55666,

title = {A review and cluster analysis of German polarity resources for sentiment analysis},

author = {Bettina Kern and Andreas Baumann and Thomas Kolb and Katharina Sekanina and Klaus Hofmann and Tanja Wissik and Julia Neidhardt},

doi = {10.4230/OASIcs.LDK.2021.37},

isbn = {978-3-95977-199-3},

year  = {2021},

date = {2021-01-01},

booktitle = {3rd Conference on Language, Data and Knowledge (LDK 2021)},

pages = {1–17},

publisher = {OASICS},

address = {93},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}