Team

@inproceedings{Wagne_Neidhardt_2024_2,

title = {Can We Integrate Items into Models? Knowledge Editing to Align LLMs with Product Catalogs},

author = {Ahmadou Wagne and Julia Neidhardt},

editor = {Vito Walter Anelli and Pierpaolo Basile and Tommaso Di Noia and Francesco Maria Donini and Antonio Ferrara and Cataldo Musto and Fedelucio Narducci and Azzurra Ragone and Markus Zanker},

year  = {2024},

date = {2024-10-31},

urldate = {2024-10-31},

booktitle = {Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop @ RecSys 2024},

volume = {3817},

pages = {56-65},

publisher = {CEUR-WS.org},

address = {Bari, Italy},

abstract = {This study explores the potential of knowledge editing techniques to enhance Large Language Models (LLMs)

for Conversational Recommender Systems (CRS). While LLMs like GPT, Llama, and Gemini have advanced

conversational capabilities, they face challenges in representing dynamic, real-world item catalogs, often leading to

inaccuracies and hallucinations in recommendations. This research preliminarily investigates whether knowledge

editing can address these limitations by updating the internal knowledge of LLMs, thereby improving the

accuracy of product information without full model retraining. Using the open-source Llama2 model, we apply

two knowledge editing methods (GRACE and r-ROME) on a dataset of notebook listings. Our findings demonstrate

improvements in the model’s ability to accurately represent product features, with r-ROME achieving the highest

gain, while not decreasing model efficiency. The study highlights the perspective of utilizing knowledge editing

to enhance CRS and suggests future work to explore broader applications and impacts on recommender systems

performance},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wagne_Neidhardt_2024,

title = {What to compare? Towards understanding user sessions on price comparison platforms},

author = {Ahmadou Wagne and Julia Neidhardt},

editor = {Tommaso Di Noia and Pasquale Lops and Thorsten Joachims and Katrien Verbert and Pablo Castells and Zhenhua Dong and Ben London},

doi = {https://doi.org/10.1145/3640457.3691717},

isbn = {979-8-4007-1127-5},

year  = {2024},

date = {2024-10-08},

urldate = {2024-10-08},

booktitle = {RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems},

pages = {1158 - 1162},

publisher = {Association for Computing Machinery},

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

abstract = {E-commerce and online shopping have become integral to the lives of many, with various user behavior types historically identified. Beyond deciding what to buy, determining where to make a purchase has led to the importance of price comparison platforms. However, user behavior on these platforms remains underexplored. Furthermore, web analytics often struggle with tracking users over time and deriving meaningful user types from data. This paper addresses these gaps by defining session types through the analysis and clustering of user logs from a major price comparison platform. The study identifies six distinct session clusters: quick peek, major purchase, constraint-based browsing, knowledge seeking, search and browse and heavy browsing. These findings are intended to inform the design and development of a conversational recommender system (CRS). Often, CRS development occurs without adequate consideration of the existing system into which it will be integrated. The study’s findings, derived from both quantitative analysis and expert interviews, provide valuable contributions, including identified session clusters, their interpretation and indicators on which users might benefit from a CRS on these platforms.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wagne_Neidhardt_Kolb_2024,

title = {PopAut: An Annotated Corpus for Populism Detection in Austrian News Comments},

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

editor = {Nicoletta Calzolari and Min-Yen Kan and Veronique Hoste and Alessandro Lenci and Sakriani Sakti and Nianwen Xue},

url = {https://aclanthology.org/2024.lrec-main.1128},

year  = {2024},

date = {2024-05-01},

urldate = {2024-05-01},

booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},

pages = {12879–12892},

publisher = {ELRA and ICCL},

address = {Torino, Italy},

abstract = {Populism is a phenomenon that is noticeably present in the political landscape of various countries over the past decades. While populism expressed by politicians has been thoroughly examined in the literature, populism expressed by citizens is still underresearched, especially when it comes to its automated detection in text. This work presents the PopAut corpus, which is the first annotated corpus of news comments for populism in the German language. It features 1,200 comments collected between 2019-2021 that are annotated for populist motives anti-elitism, people-centrism and people-sovereignty. Following the definition of Cas Mudde, populism is seen as a thin ideology. This work shows that annotators reach a high agreement when labeling news comments for these motives. The data set is collected to serve as the basis for automated populism detection using machine-learning methods. By using transformer-based models, we can outperform existing dictionaries tailored for automated populism detection in German social media content. Therefore our work provides a rich resource for future work on the classification of populist user comments in the German language.},

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}

}