Team

@inproceedings{	20.500.12708_175977,

title = {Diversifying Sentiments in News Recommendation},

author = {Mete Sertkan and Sophia Althammer and Sebastian Hofstätter and Julia Neidhardt},

doi = {10.34726/3903},

year  = {2022},

date = {2022-01-01},

volume = {3228},

series = {CEUR Workshop Proceedings},

abstract = {Personalized news recommender systems are widely deployed to filter the information overload caused by the sheer amount of news produced daily. Recommended news articles usually have a sentiment similar to the sentiment orientation of the previously consumed news, creating a self-reinforcing cycle of sentiment chambers around people. Wu et al. introduced SentiRec – a sentiment diversity-aware neural news recommendation model to counter this lack of diversity. 

 In this work, we reproduce SentiRec without access to the original source code and data sample. We re-implement SentiRec from scratch and use the Microsoft MIND dataset (same source but different subset as in the original work) for our experiments. We evaluate and discuss our reproduction from different perspectives. While the original paper mainly has a user-centric perspective on sentiment diversity by comparing the recommendation list to the user’s interaction history, we also analyze the intra-list sentiment diversity of the recommendation list. Additionally, we study the effect of sentiment diversification on topical diversity. Our results suggest that SentiRec does not generalize well to other data since the compared baselines already perform well, opposing the original work’s findings. While the original SentiRec utilizes a rule-based sentiment analyzer, we also study a pre-trained neural sentiment analyzer. However, we observe no improvements in effectiveness nor in sentiment diversity. To foster reproducibility, we make our source code publicly available.},

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_191201,

title = {Workshop on Recommenders in Tourism (RecTour)},

author = {Julia Neidhardt and Wolfgang Wörndl and Tsvi Kuflik and Dmitri Goldenberg and Markus Zanker},

editor = {Jennifer Golbeck and F. Maxwell Harper and Vanessa Murdock},

doi = {10.1145/3523227.3547416},

year  = {2022},

date = {2022-01-01},

pages = {678–679},

publisher = {Association for Computing Machinery},

address = {New York},

abstract = {The Workshop on Recommenders in Tourism (RecTour) 2022, which is held in conjunction with the 16th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_153156,

title = {Exploring Expressed Emotions for Neural News Recommendation},

author = {Mete Sertkan and Julia Neidhardt},

doi = {10.1145/3511047.3536414},

year  = {2022},

date = {2022-01-01},

pages = {22–28},

publisher = {Association for Computing Machinery},

address = {New York, NY, United States},

abstract = {Due to domain-specific challenges such as short item lifetimes and continuous cold-start issues, news recommender systems rely more on content-based methods to deduce reliable user models and make personalized recommendations. Research has shown that alongside the content of an item, the way it is presented to the users also plays a critical role. In this work, we focus on the effect of incorporating expressed emotions within news articles on recommendation performance. We propose a neural news recommendation model that disentangles semantic and emotional modeling of news articles and users. While we exploit the textual content for the semantic representation, we extract and combine emotions of different information levels for the emotional representation. Offline experiments on a real-world dataset show that our approach outperforms non-emotion-aware solutions significantly. Finally, we provide a future outline, where we plan to investigate a) the online performance and b) the explainability/explorability of our approach.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_153166,

title = {Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures},

author = {Julia Neidhardt and Mete Sertkan},

editor = {Ludovico Boratto and Stefano Faralli and Mirko Marras and stilo},

doi = {10.1007/978-3-031-09316-6_4},

year  = {2022},

date = {2022-01-01},

volume = {1610},

pages = {35–42},

publisher = {Springer Cham},

abstract = {The quality of recommender systems has traditionally only been assessed using accuracy measures. Research has shown that accuracy is only one side of the medallion and that we should also consider quality features that go beyond accuracy. Recently, also fairness-related aspects and bias have increasingly been considered as outcome dimensions in this context. While beyond-accuracy measures including diversity, novelty and serendipity and bias in recommendation have been subject to the research discourse, their interrelation and temporal and group dynamics are clearly under-explored. In this position paper, we propose an approach that groups users based on their behaviors and preferences and that addresses beyond-accuracy needs of those groups while controlling for bias. Further, we consider the analysis of long-term dynamics of different interrelated beyond-accuracy measures and bias as crucial research direction since it helps to advance the field and to address societal issues related to recommender systems and personalization.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_177100,

title = {Visualizing Parliamentary Speeches as Networks: the DYLEN Tool},

author = {Seung-Bin Yim and Katharina Wünsche and Asil Cetin and Julia Neidhardt and Andreas Baumann and Tanja Wissik},

editor = {Darja Fišer and Maria Eskevich and Jakob Lenardič and Franciska Jong},

doi = {10.34726/4105},

year  = {2022},

date = {2022-01-01},

pages = {56–60},

publisher = {European Language Resources Association (ELRA)},

abstract = {In this paper, we present a web based interactive visualization tool for lexical networks based on the utterances of Austrian Members of Parliament. The tool is designed to compare two networks in parallel and is composed of graph visualization, node-metrics comparison and time-series comparison components that are interconnected with each other.},

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_58556,

title = {Workshop on Recommenders in Tourism (RecTour)},

author = {Julia Neidhardt and Wolfgang Wörndl and Tsvi Kuflik and Markus Zanker},

doi = {10.1145/3460231.3470930},

year  = {2021},

date = {2021-01-01},

booktitle = {Fifteenth ACM Conference on Recommender Systems},

publisher = {ACM},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_58574,

title = {WebTour 2021 Workshop on Web Tourism},

author = {Tsvi Kuflik and Catalin Mihai Barbu and Amra Delic and Dmitri Goldenberg and Julia Neidhardt and Ludovik Coba and Markus Zanker},

doi = {10.1145/3437963.3441836},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings of the 14th ACM International Conference on Web Search and Data Mining},

publisher = {ACM},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_58724,

title = {Semantische Netzwerke als Hilfsmittel für die Untersuchung des Begriffskomplexes Arbeit-Lohn-Vermögen im öffentlichen Diskurs Österreichs},

author = {Klaus Hofmann and Andreas Baumann and Anna Marakasova and Julia Neidhardt and Tanja Wissik},

year  = {2021},

date = {2021-01-01},

booktitle = {Momentum 2021 Kongress},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_55667,

title = {Exploring Causal Relationships Among Emotional and Topical Trajectories in Political Text Data},

author = {Andreas Baumann and Klaus Hofmann and Bettina Kern and Anna Marakasova and Julia Neidhardt and Tanja Wissik},

doi = {10.4230/OASIcs.LDK.2021.38},

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

year  = {2021},

date = {2021-01-01},

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

pages = {1–8},

publisher = {OASICS},

address = {93},

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}

}

@inproceedings{	20.500.12708_58393,

title = {Comparing Lexical Usage in Political Discourse across Diachronic Corpora.},

author = {Klaus Hofmann and Anna Marakasova and Andreas Baumann and Julia Neidhardt and Tanja Wissik},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of the Second ParlaCLARIN Workshop. Language Resources and Evaluation Conference (LREC 2020), Marseille, 11-16 May 2020.},

pages = {58–65},

publisher = {ACL Anthology},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_58382,

title = {Eliciting Touristic Profiles: A User Study on Picture Collections},

author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},

editor = {Tsvi Kuflik and Ilaria Torre and Robin Burke and Cristina Gena},

doi = {10.1145/3340631.3394868},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization},

publisher = {Association for Computing Machinery, New York, NY, USA},

abstract = {Eliciting the preferences and needs of tourists is challenging, since people often have difficulties to explicitly express them, especially in the initial phase of travel planning. Recommender systems employed at the early stage of planning can therefore be very beneficial to the general satisfaction of a user. Previous studies have explored pictures as a tool of communication and as a way to implicitly deduce a traveller's preferences and needs. In this paper, we conduct a user study to verify previous claims and conceptual work on the feasibility of modelling travel interests from a selection of a user's pictures. We utilize fine-tuned convolutional neural networks to compute a vector representation of a picture, where each dimension corresponds to a travel behavioural pattern from the traditional Seven-Factor model. In our study, we followed strict privacy principles and did not save uploaded pictures after computing their vector representation. We aggregate the representations of the pictures of a user into a single user representation, ie, touristic profile, using different strategies. In our user study with 81 participants, we let users adjust the predicted touristic profile and confirm the usefulness of our approach. Our results show that given a collection of pictures the touristic profile of a user can be determined.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_58381,

title = {From Pictures to Travel Characteristics: Deep Learning-Based Profiling of Tourists and Tourism Destinations},

author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},

editor = {Julia Neidhardt and Wolfgang Wörndl},

doi = {10.1007/978-3-030-36737-4_12},

isbn = {9783030367374},

year  = {2020},

date = {2020-01-01},

booktitle = {Information and Communication Technologies in Tourism 2020},

pages = {142–153},

publisher = {Springer},

abstract = {Tourism products are complex and strongly tied to emotions. Thus, it is not easy for consumers to explicitly communicate their travel preferences, needs, and interest, especially in the early phase of travel decision making. In the spirit of the idiom "A picture is worth a thousand words" we utilize pictures to characterize tourists as well as tourism destinations in order to build the foundations of a recommender system (RS). In this work all entities (i.e., users and items) are characterized using the Seven-Factor Model. Pre-labelled pictures are used in order to train convolutional neural networks (CNN) in a transfer learning manner with the goal to extract the Seven-Factors of a given picture. We demonstrate that touristic characteristics can be extracted out of pictures. Furthermore, we show that those characteristics can be aggregated for a collection of pictures, such that a representation of a user or a destination can be determined respectively.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_58385,

title = {PicTouRe - A Picture-Based Tourism Recommender},

author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},

doi = {10.1145/3383313.3411526},

year  = {2020},

date = {2020-01-01},

booktitle = {Fourteenth ACM Conference on Recommender Systems},

publisher = {Association for Computing Machinery, New York, United States},

abstract = {We present PicTouRe-a picture-based tourism recommender. PicTouRe aims to mitigate people´s difficulties in explicitly expressing their touristic preferences, which is even more challenging in the initial phase of travel decision making. Addressing this issue, with PicTouRe we follow the idiom "a picture is worth a thousand words" and use pictures as a tool to implicitly elicit peoples´ touristic preferences. We describe the core concept of PicTouRe-the Generic Profiler, which in essence determines an explainable vector representation, ie, touristic profile, given any picture collection as input. We showcase a user´s journey through PicTouRe and describe the steps behind. Finally, we present results of a first user study supporting our approach. PicTouRe is available under https://pictoprof. ec. tuwien. ac. at and a demo video under https://youtu. be/xZnXLPcenEs.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_55595,

title = {Short-term Semantic Shifts and their Relation to Frequency Change},

author = {Anna Marakasova and Julia Neidhardt},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of the Probability and Meaning Conference (PaM 2020)},

pages = {146–153},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_58392,

title = {Semantic shifts in the Austrian public discourse: A lexical networks approach},

author = {Andreas Baumann and Klaus Hofmann and Anna Marakasova and Julia Neidhardt and Tanja Wissik},

year  = {2020},

date = {2020-01-01},

booktitle = {42nd Annual Conference of the German Linguistic Society (DGfS 2020)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_57580,

title = {A Framework for Recommender Systems Based on a Finite Multidimensional Model Space.},

author = {Leonhard Seyfang and Julia Neidhardt},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the ACM Workshop of Recommenders in Tourism (RecTour) 2019},

pages = {27–31},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_58133,

title = {Preference Networks and Non-Linear Preferences in Group Recommendations},

author = {Amra Delic and Francesco Ricci and Julia Neidhardt},

doi = {10.1145/3350546.3352556},

year  = {2019},

date = {2019-01-01},

booktitle = {IEEE/WIC/ACM International Conference on Web Intelligence},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_58129,

title = {From Pictures to Touristic Profiles: A Deep-Learning Based Approach},

author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},

isbn = {978-1-5262-0820-0},

year  = {2019},

date = {2019-01-01},

booktitle = {ProceedingsDSRS-Turing´19.},

pages = {75–78},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_55541,

title = {DYLEN: Diachronic Dynamics of Lexical Networks.},

author = {Andreas Baumann and Julia Neidhardt and Tanja Wissik},

year  = {2019},

date = {2019-01-01},

booktitle = {Poster Proceedings of 2nd Conference on Language, Data and Knowledge (LDK 2019)},

pages = {1–5},

series = {CEUR Workshop Proceedings},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_57685,

title = {Documents, Topics, and Authors: Text Mining of Online News},

author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},

doi = {10.1109/cbi.2019.00053},

year  = {2019},

date = {2019-01-01},

booktitle = {2019 IEEE 21st Conference on Business Informatics (CBI)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_57654,

title = {RecTour 2019},

author = {Julia Neidhardt and Wolfgang Wörndl and Tsvi Kuflik and Markus Zanker and Catalin-Mihai Barbu},

doi = {10.1145/3298689.3346969},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the 13th ACM Conference on Recommender Systems},

publisher = {ACM Digital Library},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_57691,

title = {The Potential of Chatbots: Analysis of Chatbot Conversations},

author = {Mubashra Akhtar and Julia Neidhardt and Hannes Werthner},

doi = {10.1109/cbi.2019.00052},

year  = {2019},

date = {2019-01-01},

booktitle = {2019 IEEE 21st Conference on Business Informatics (CBI)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}