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277 results on '"Jäschke, Robert"'

1. On the Robustness of Cover Version Identification Models: A Study Using Cover Versions from YouTube

Author

Hachmeier, Simon and Jäschke, Robert

Subjects
Computer Science - Multimedia, Computer Science - Information Retrieval, Computer Science - Social and Information Networks
Abstract

Recent advances in cover song identification have shown great success. However, models are usually tested on a fixed set of datasets which are relying on the online cover song database SecondHandSongs. It is unclear how well models perform on cover songs on online video platforms, which might exhibit alterations that are not expected. In this paper, we annotate a subset of songs from YouTube sampled by a multi-modal uncertainty sampling approach and evaluate state-of-the-art models. We find that existing models achieve significantly lower ranking performance on our dataset compared to a community dataset. We additionally measure the performance of different types of versions (e.g., instrumental versions) and find several types that are particularly hard to rank. Lastly, we provide a taxonomy of alterations in cover versions on the web., Comment: accepted for presentation at iConference 2025

Published
2025

2. A Benchmark and Robustness Study of In-Context-Learning with Large Language Models in Music Entity Detection

Author

Hachmeier, Simon and Jäschke, Robert

Subjects
Computer Science - Computation and Language, Computer Science - Multimedia
Abstract

Detecting music entities such as song titles or artist names is a useful application to help use cases like processing music search queries or analyzing music consumption on the web. Recent approaches incorporate smaller language models (SLMs) like BERT and achieve high results. However, further research indicates a high influence of entity exposure during pre-training on the performance of the models. With the advent of large language models (LLMs), these outperform SLMs in a variety of downstream tasks. However, researchers are still divided if this is applicable to tasks like entity detection in texts due to issues like hallucination. In this paper, we provide a novel dataset of user-generated metadata and conduct a benchmark and a robustness study using recent LLMs with in-context-learning (ICL). Our results indicate that LLMs in the ICL setting yield higher performance than SLMs. We further uncover the large impact of entity exposure on the best performing LLM in our study.

Published
2024

3. Leveraging User-Generated Metadata of Online Videos for Cover Song Identification

Author

Hachmeier, Simon and Jäschke, Robert

Subjects
Computer Science - Multimedia, Computer Science - Information Retrieval
Abstract

YouTube is a rich source of cover songs. Since the platform itself is organized in terms of videos rather than songs, the retrieval of covers is not trivial. The field of cover song identification addresses this problem and provides approaches that usually rely on audio content. However, including the user-generated video metadata available on YouTube promises improved identification results. In this paper, we propose a multi-modal approach for cover song identification on online video platforms. We combine the entity resolution models with audio-based approaches using a ranking model. Our findings implicate that leveraging user-generated metadata can stabilize cover song identification performance on YouTube., Comment: accepted for presentation at NLP for Music and Audio (NLP4MusA) 2024

Published
2024

4. A Repository for Formal Contexts

Author

Hanika, Tom and Jäschke, Robert

Subjects
Computer Science - Artificial Intelligence, Computer Science - Digital Libraries
Abstract

Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of data sets are repeatedly used in scholarly articles and software tools, acting as de facto standard data sets. However, the distribution of the data sets poses a problem for the sustainable development of the research field. There is a lack of a central location that provides and describes FCA data sets and links them to already known analysis results. This article analyses the current state of the dissemination of FCA data sets, presents the requirements for a central FCA repository, and highlights the challenges for this., Comment: 16 pages

Published
2024

5. 'The Michael Jordan of Greatness': Extracting Vossian Antonomasia from Two Decades of the New York Times, 1987-2007

Author

Fischer, Frank and Jäschke, Robert

Subjects
Computer Science - Information Retrieval, Computer Science - Computation and Language
Abstract

Vossian Antonomasia is a prolific stylistic device, in use since antiquity. It can compress the introduction or description of a person or another named entity into a terse, poignant formulation and can best be explained by an example: When Norwegian world champion Magnus Carlsen is described as "the Mozart of chess", it is Vossian Antonomasia we are dealing with. The pattern is simple: A source (Mozart) is used to describe a target (Magnus Carlsen), the transfer of meaning is reached via a modifier ("of chess"). This phenomenon has been discussed before (as 'metaphorical antonomasia' or, with special focus on the source object, as 'paragons'), but no corpus-based approach has been undertaken as yet to explore its breadth and variety. We are looking into a full-text newspaper corpus (The New York Times, 1987-2007) and describe a new method for the automatic extraction of Vossian Antonomasia based on Wikidata entities. Our analysis offers new insights into the occurrence of popular paragons and their distribution.

Published
2019
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7. Analyzing the Web: Are Top Websites Lists a Good Choice for Research?

Author

Alby, Tom, Jäschke, Robert, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Silvello, Gianmaria, editor, Corcho, Oscar, editor, Manghi, Paolo, editor, Di Nunzio, Giorgio Maria, editor, Golub, Koraljka, editor, Ferro, Nicola, editor, and Poggi, Antonella, editor

Published
2022
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8. Evaluating Dataset Creation Heuristics for Concept Detection in Web Pages Using BERT

Author

Paris, Michael, Jäschke, Robert, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Qiu, Han, editor, Zhang, Cheng, editor, Fei, Zongming, editor, Qiu, Meikang, editor, and Kung, Sun-Yuan, editor

Published
2021
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9. Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information

Author

Tran, Tuan, Tran, Nam Khanh, Asmelash, Teka Hadgu, and Jäschke, Robert

Subjects
Computer Science - Information Retrieval, I.2.7, H.3.1
Abstract

Trending topics in microblogs such as Twitter are valuable resources to understand social aspects of real-world events. To enable deep analyses of such trends, semantic annotation is an effective approach; yet the problem of annotating microblog trending topics is largely unexplored by the research community. In this work, we tackle the problem of mapping trending Twitter topics to entities from Wikipedia. We propose a novel model that complements traditional text-based approaches by rewarding entities that exhibit a high temporal correlation with topics during their burst time period. By exploiting temporal information from the Wikipedia edit history and page view logs, we have improved the annotation performance by 17-28\%, as compared to the competitive baselines., Comment: Published via ACL to EMNLP 2025

Published
2017

10. World Literature According to Wikipedia: Introduction to a DBpedia-Based Framework

Author

Hube, Christoph, Fischer, Frank, Jäschke, Robert, Lauer, Gerhard, and Thomsen, Mads Rosendahl

Subjects
Computer Science - Information Retrieval, Computer Science - Computation and Language
Abstract

Among the manifold takes on world literature, it is our goal to contribute to the discussion from a digital point of view by analyzing the representation of world literature in Wikipedia with its millions of articles in hundreds of languages. As a preliminary, we introduce and compare three different approaches to identify writers on Wikipedia using data from DBpedia, a community project with the goal of extracting and providing structured information from Wikipedia. Equipped with our basic set of writers, we analyze how they are represented throughout the 15 biggest Wikipedia language versions. We combine intrinsic measures (mostly examining the connectedness of articles) with extrinsic ones (analyzing how often articles are frequented by readers) and develop methods to evaluate our results. The better part of our findings seems to convey a rather conservative, old-fashioned version of world literature, but a version derived from reproducible facts revealing an implicit literary canon based on the editing and reading behavior of millions of people. While still having to solve some known issues, the introduced methods will help us build an observatory of world literature to further investigate its representativeness and biases., Comment: 33 pages, 6 figures, 6 tables

Published
2017

13. Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations

Author

Landia, Nikolas, Doerfel, Stephan, Jäschke, Robert, Anand, Sarabjot Singh, Hotho, Andreas, and Griffiths, Nathan

Subjects
Computer Science - Information Retrieval, H.3.3, H.3.1
Abstract

The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.

Published
2013

16. Online Evaluation

Author

Marinho, Leandro Balby, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, Symeonidis, Panagiotis, Balby Marinho, Leandro, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, and Symeonidis, Panagiotis

Published
2012
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17. Real World Social Tagging Recommender Systems

Author

Marinho, Leandro Balby, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, Symeonidis, Panagiotis, Balby Marinho, Leandro, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, and Symeonidis, Panagiotis

Published
2012
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18. Advanced Techniques

Author

Marinho, Leandro Balby, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, Symeonidis, Panagiotis, Balby Marinho, Leandro, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, and Symeonidis, Panagiotis

Published
2012
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19. Recommender Systems

Author

Marinho, Leandro Balby, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, Symeonidis, Panagiotis, Balby Marinho, Leandro, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, and Symeonidis, Panagiotis

Published
2012
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20. Baseline Techniques

Author

Marinho, Leandro Balby, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, Symeonidis, Panagiotis, Balby Marinho, Leandro, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, and Symeonidis, Panagiotis

Published
2012
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21. Social Tagging Systems

Author

Marinho, Leandro Balby, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, Symeonidis, Panagiotis, Balby Marinho, Leandro, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, and Symeonidis, Panagiotis

Published
2012
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23. Visualisierung von Open Data

Author

Hillebrand, Vera, Jäschke, Robert, Sturm, Raphael, Hillebrand, Vera, Jäschke, Robert, and Sturm, Raphael

Abstract

Die Offenlegung von Verwaltungsdaten ist ein wichtiger Bestandteil der in den letzten Jahren immer mehr an Bedeutung gewinnenden Open Government Initiative, die zum Ziel hat, die Transparenz, Offenheit und Beteiligungsmöglichkeiten in Regierungs- und Verwaltungshandeln zu erhöhen. Zur Stärkung des Vertrauens zwischen politischen Institutionen und Bürger*innen, Medien, Wissenschaft und Wirtschaft werden offene Regierungsdaten in Open Data Portalen zur freien Wiederverwendung veröffentlicht. Unter der Annahme, dass die alleinige Bereitstellung der offenen Daten nicht automatisch in eine hochfrequente Nutzung selbiger resultiert, wird basierend auf dem aktuellen Stand der Forschung hinsichtlich der Nutzer*innenfreundlichkeit von Open Data Portalen die These aufgegriffen, dass die Bereitstellung von Visualisierungen die Wiederverwendbarkeit von offenen Daten erhöht. Die vorliegende Arbeit geht daher der Frage nach, wie Visualisierungen in ein Open Data Portal eingebunden werden können. Zur Beantwortung dieser Frage wird am Beispiel des Open Data Portals Data.Europa.eu eine Analyse der bestehenden Implementierung sowie die Untersuchung von drei Best-Practice Portalen hinsichtlich ihrer Umsetzung von Visualisierungen vorgenommen. Aufbauend auf der Analyse wird ein Konzept zur Einbindung von Visualisierungen entwickelt. Das entwickelte Konzept wird anschließend hinsichtlich der Nutzer*innenfreundlichkeit mithilfe eines Usability-Tests evaluiert. Die Ergebnisse zeigen auf, dass die Einbindung von Visualisierungen das Verständnis der vorhandenen Daten und somit die Wiederverwendbarkeit unterstützt., The disclosure of administrative data is an important component of the increasingly significant Open Government initiative that aims to enhance transparency, openness, and opportunities for participation in governmental and administrative actions. Open government data is published in open data portals for free reuse to strengthen trust between political institutions and citizens, media, academia, and business. Assuming that the mere provision of open data does not automatically result in its high-frequency utilization, this work addresses the thesis based on the current state of research regarding the user-friendliness of open data portals, proposing that the provision of visualizations increases the reusability of open data. Consequently, this study investigates how visualizations can be integrated into an open data portal, using the example of the data.europa.eu open data portal. An analysis of the existing implementation of the portal and an examination of three best-practice portals in terms of their visualization implementation are conducted to answer this question. Building upon the analysis, a concept for integrating visualizations is developed. Subsequently, the developed concept is evaluated for user-friendliness through a usability test. The results indicate that the integration of visualizations supports the understanding of the available data and thereby enhances its reusability.

Published
2023

26. Conclusions

Author

Marinho, Leandro Balby, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, Symeonidis, Panagiotis, Balby Marinho, Leandro, Hotho, Andreas, Jäschke, Robert, Nanopoulos, Alexandros, Rendle, Steffen, Schmidt-Thieme, Lars, Stumme, Gerd, and Symeonidis, Panagiotis

Published
2012
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28. Publication Analysis of the Formal Concept Analysis Community

Author

Doerfel, Stephan, Jäschke, Robert, Stumme, Gerd, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Domenach, Florent, editor, Ignatov, Dmitry I., editor, and Poelmans, Jonas, editor

Published
2012
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29. A Comparison of Content-Based Tag Recommendations in Folksonomy Systems

Author

Illig, Jens, Hotho, Andreas, Jäschke, Robert, Stumme, Gerd, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Wolff, Karl Erich, editor, Palchunov, Dmitry E., editor, Zagoruiko, Nikolay G., editor, and Andelfinger, Urs, editor

Published
2011
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30. Social Tagging Recommender Systems

Author

Marinho, Leandro Balby, Nanopoulos, Alexandros, Schmidt-Thieme, Lars, Jäschke, Robert, Hotho, Andreas, Stumme, Gerd, Symeonidis, Panagiotis, Ricci, Francesco, editor, Rokach, Lior, editor, Shapira, Bracha, editor, and Kantor, Paul B., editor

Published
2011
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31. Academic Publication Management with PUMA – Collect, Organize and Share Publications

Author

Benz, Dominik, Hotho, Andreas, Jäschke, Robert, Stumme, Gerd, Halle, Axel, Gerlach Sanches Lima, Angela, Steenweg, Helge, Stefani, Sven, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Lalmas, Mounia, editor, Jose, Joemon, editor, Rauber, Andreas, editor, Sebastiani, Fabrizio, editor, and Frommholz, Ingo, editor

Published
2010
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35. Tag Recommendations in Folksonomies

Author

Jäschke, Robert, Marinho, Leandro, Hotho, Andreas, Schmidt-Thieme, Lars, Stumme, Gerd, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Kok, Joost N., editor, Koronacki, Jacek, editor, Lopez de Mantaras, Ramon, editor, Matwin, Stan, editor, Mladenič, Dunja, editor, and Skowron, Andrzej, editor

Published
2007
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37. Mining Association Rules in Folksonomies

Author

Schmitz, Christoph, Hotho, Andreas, Jäschke, Robert, Stumme, Gerd, Bock, H. -H., editor, Gaul, W., editor, Vichi, M., editor, Arabie, Ph., editor, Baier, D., editor, Critchley, F., editor, Decker, R., editor, Diday, E., editor, Greenacre, M., editor, Lauro, C., editor, Meulman, J., editor, Monari, P., editor, Nishisato, S., editor, Ohsumi, N., editor, Opitz, O., editor, Ritter, G., editor, Schader, M., editor, Weihs, C., editor, Batagelj, Vladimir, editor, Bock, Hans-Hermann, editor, Ferligoj, Anuška, editor, and Žiberna, Aleš, editor

Published
2006
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38. Trend Detection in Folksonomies

Author

Hotho, Andreas, Jäschke, Robert, Schmitz, Christoph, Stumme, Gerd, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Avrithis, Yannis, editor, Kompatsiaris, Yiannis, editor, Staab, Steffen, editor, and O’Connor, Noel E., editor

Published
2006
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39. Information Retrieval in Folksonomies: Search and Ranking

Author

Hotho, Andreas, Jäschke, Robert, Schmitz, Christoph, Stumme, Gerd, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sure, York, editor, and Domingue, John, editor

Published
2006
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40. Semantic Network Analysis of Ontologies

Author

Hoser, Bettina, Hotho, Andreas, Jäschke, Robert, Schmitz, Christoph, Stumme, Gerd, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sure, York, editor, and Domingue, John, editor

Published
2006
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41. Content Aggregation on Knowledge Bases Using Graph Clustering

Author

Schmitz, Christoph, Hotho, Andreas, Jäschke, Robert, Stumme, Gerd, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sure, York, editor, and Domingue, John, editor

Published
2006
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42. “Der Frank Sinatra der Wettervorhersage”: Cross-Lingual Vossian Antonomasia Extraction

Author

Abbas, Mourad, Freihat, Abed Alhakim, Schwab, Michel, Jäschke, Robert, Fischer, Frank, Abbas, Mourad, Freihat, Abed Alhakim, Schwab, Michel, Jäschke, Robert, and Fischer, Frank

Abstract

We present a cross-lingual approach for the extraction of Vossian Antonomasia, a stylistic device especially popular in newspaper articles. We evaluate a zero-shot transfer learning approach and two approaches that use machinetranslated training and test data. We show that our proposed models achieve strong results on all test datasets in the target language. As annotated data is sparse, especially in the target language, we generate additional test data to evaluate our models and conclude with a robustness study on real-world data.

Published
2022

43. “The Rodney Dangerfield of Stylistic Devices”: End-to-End Detection and Extraction of Vossian Antonomasia Using Neural Networks

Author

Schwab, Michel, Jäschke, Robert, Fischer, Frank, Schwab, Michel, Jäschke, Robert, and Fischer, Frank

Abstract

Vossian Antonomasia (VA) is a well-known stylistic device based on attributing a certain property to a person by relating them to another person who is famous for this property. Although the morphological and semantic characteristics of this phenomenon have long been the subject of linguistic research, little is known about its distribution. In this paper, we describe end-to-end approaches for detecting and extracting VA expressions from large news corpora in order to study VA more broadly. We present two types of approaches: binary sentence classifiers that detect whether or not a sentence contains a VA expression, and sequence tagging of all parts of a VA on the word level, enabling their extraction. All models are based on neural networks and outperform previous approaches, best results are obtained with a fine-tuned BERT model. Furthermore, we study the impact of training data size and class imbalance by adding negative (and possibly noisy) instances to the training data. We also evaluate the models' performance on out-of-corpus and real-world data and analyze the ability of the sequence tagging model to generalize in terms of new entity types and syntactic patterns., Peer Reviewed

Published
2022

44. Timeout Reached, Session Ends?

Author

Petras, Vivien, Jäschke, Robert, Dietz, Florian, Petras, Vivien, Jäschke, Robert, and Dietz, Florian

Abstract

Die Identifikation von Sessions zum Verständnis des Benutzerverhaltens ist ein Forschungsgebiet des Web Usage Mining. Definitionen und Konzepte werden seit über 20 Jahren diskutiert. Die Forschung zeigt, dass Session-Identifizierung kein willkürlicher Prozess sein sollte. Es gibt eine fragwürdige Tendenz zu vereinfachten mechanischen Sessions anstelle logischer Segmentierungen. Ziel der Dissertation ist es zu beweisen, wie unterschiedliche Session-Ansätze zu abweichenden Ergebnissen und Interpretationen führen. Die übergreifende Forschungsfrage lautet: Werden sich verschiedene Ansätze zur Session-Identifizierung auf Analyseergebnisse und Machine-Learning-Probleme auswirken? Ein methodischer Rahmen für die Durchführung, den Vergleich und die Evaluation von Sessions wird gegeben. Die Dissertation implementiert 135 Session-Ansätze in einem Jahr (2018) Daten einer deutschen Preisvergleichs-E-Commerce-Plattform. Die Umsetzung umfasst mechanische Konzepte, logische Konstrukte und die Kombination mehrerer Mechaniken. Es wird gezeigt, wie logische Sessions durch Embedding-Algorithmen aus Benutzersequenzen konstruiert werden: mit einem neuartigen Ansatz zur Identifizierung logischer Sessions, bei dem die thematische Nähe von Interaktionen anstelle von Suchanfragen allein verwendet wird. Alle Ansätze werden verglichen und quantitativ beschrieben sowie in drei Machine-Learning-Problemen (wie Recommendation) angewendet. Der Hauptbeitrag dieser Dissertation besteht darin, einen umfassenden Vergleich von Session-Identifikationsalgorithmen bereitzustellen. Die Arbeit bietet eine Methodik zum Implementieren, Analysieren und Evaluieren einer Auswahl von Mechaniken, die es ermöglichen, das Benutzerverhalten und die Auswirkungen von Session-Modellierung besser zu verstehen. Die Ergebnisse zeigen, dass unterschiedlich strukturierte Eingabedaten die Ergebnisse von Algorithmen oder Analysen drastisch verändern können., The identification of sessions as a means of understanding user behaviour is a common research area of web usage mining. Different definitions and concepts have been discussed for over 20 years: Research shows that session identification is not an arbitrary task. There is a tendency towards simplistic mechanical sessions instead of more complex logical segmentations, which is questionable. This dissertation aims to prove how the nature of differing session-identification approaches leads to diverging results and interpretations. The overarching research question asks: will different session-identification approaches impact analysis and machine learning tasks? A comprehensive methodological framework for implementing, comparing and evaluating sessions is given. The dissertation provides implementation guidelines for 135 session-identification approaches utilizing a complete year (2018) of traffic data from a German price-comparison e-commerce platform. The implementation includes mechanical concepts, logical constructs and the combination of multiple methods. It shows how logical sessions were constructed from user sequences by employing embedding algorithms on interaction logs; taking a novel approach to logical session identification by utilizing topical proximity of interactions instead of search queries alone. All approaches are compared and quantitatively described. The application in three machine-learning tasks (such as recommendation) is intended to show that using different sessions as input data has a marked impact on the outcome. The main contribution of this dissertation is to provide a comprehensive comparison of session-identification algorithms. The research provides a methodology to implement, analyse and compare a wide variety of mechanics, allowing to better understand user behaviour and the effects of session modelling. The main results show that differently structured input data may drastically change the results of algorithms or analysis.

Published
2022

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