@inproceedings{kleemann2023,
  abstract = {Today’s e-commerce websites often provide many different components, such as filters and conversational product advisors, to help users find relevant items. However, filters and advisors are often presented separately and treated as independent entities so that the previous input is discarded when users switch between them. This leads to memory loads and disruptions during the search process. In addition, the reasoning behind the advisors’ results is often not transparent. To overcome these limitations, we propose a novel approach that exploits a graph structure to create an integrated system that allows a seamless coupling between filters and advisors. The integrated system utilizes the graph to suggest appropriate filter values and items based on the user’s answers in the advisor. Moreover, it determines follow-up questions based on the filter values set by the user. The interface visualizes and explains the relationship between a given answer and its relevant features to achieve increased transparency in the guidance process. We report the results of an empirical user study with 120 participants that compares the integrated system to a system in which the filtering and advisory mechanisms operate separately. The findings indicate that displaying recommendations and explanations directly in the filter component can increase acceptance and trust in the system. Similarly, combining the advisor with the filters along with the displayed explanations leads to significantly higher levels of knowledge about the relevant product features.},
  address = {Cham},
  author = {Kleemann, Timm and Ziegler, Jürgen},
  series = {Lecture Notes in Computer Science},
  booktitle = {Human-Computer Interaction – INTERACT 2023 : 19th IFIP TC13 International Conference, York, UK, August 28 – September 1, 2023, Proceedings, Part III},
  editor = {Abdelnour Nocera, José and Kristı́n Lárusdóttir, Marta and Petrie, Helen and Piccinno, Antonio and Winckler, Marco},
  isbn = {9783031422850},
  issn = {0302-9743},
  volume = {14144},
  doi = {10.1007/978-3-031-42286-7_8},
  pages = {137–159},
  publisher = {Springer Nature Switzerland},
  title = {Blending Conversational Product Advisors and Faceted Filtering in a Graph-Based Approach},
  url = {https://doi.org/10.1007/978-3-031-42286-7_8},
  note = {10.1007/978-3-031-42286-7_8},
  language = {en},
  keywords = {Search interfaces; explanations; knowledge graph},
  year = {2023},
  month = {aug},
  day = {25},
  month_numeric = {8}
}


@inproceedings{ubo_mods_00139552,
  author = {Kleemann, Timm and Ziegler, Jürgen},
  title = {Integration of Dialog-based Product Advisors into Filter Systems},
  booktitle = {Proceedings of the Conference on Mensch und Computer},
  series = {ACM International Conference Proceeding Series},
  year = {2019},
  publisher = {ACM Press},
  address = {New York},
  pages = {67–77},
  keywords = {Dialogbasierte Produktberater, Filtersysteme},
  isbn = {978-1-4503-7198-8},
  doi = {10.1145/3340764.3340786},
  abstract = { Different techniques such as search functions or recommendation components are used today to support the often complex product search on the Internet. Faceted filter systems that successively limit the result set according to the set filter settings have proven to be quite successful. However, this method requires clear objectives and domain knowledge on the part of the users. As an alternative, conversational product advisors who select suitable products on the basis of a sequence of questions have gained more importance in recent times, whereby the questions are based more on the tasks and application scenarios of the users than on the technical properties of the products. However, there is currently a lack of approaches that integrate filter systems and conversational advisors in a meaningful and closely coupled way. In this paper an integrated approach is presented, where users can switch between filter systems and advisory dialogues, whereby selection actions in one component have a consistent and transparent effect on the other component and can be further adjusted there. The aim is to better support users with different levels of knowledge of the product type concerned. We describe the requirements for such integrated systems resulting from our approach and report on a user study in which the user behavior and the subjective evaluation were examined in a prototypical implementation.}
}


@inproceedings{ubo_mods_00116566,
  author = {Loepp, Benedikt and Donkers, Tim and Kleemann, Timm and Ziegler, Jürgen},
  title = {Impact of Item Consumption on Assessment of Recommendations in User Studies},
  booktitle = {Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18)},
  year = {2018},
  publisher = {ACM},
  address = {New York, NY, USA},
  pages = {49–53},
  keywords = {User Studies},
  isbn = {978-1-4503-5901-6},
  doi = {10.1145/3240323.3240375},
  url = {https://dl.acm.org/doi/10.1145/3240323.3240375?cid=87958660357},
  abstract = {In user studies of recommender systems, participants typically cannot consume the recommended items. Still, they are asked to assess recommendation quality and other aspects related to user experience by means of questionnaires. Without having listened to recommended songs or watched suggested movies, however, this might be an error-prone task, possibly limiting validity of results obtained in these studies. In this paper, we investigate the effect of actually consuming the recommended items. We present two user studies conducted in different domains showing that in some cases, differences in the assessment of recommendations and in questionnaire results occur. Apparently, it is not always possible to adequately measure user experience without allowing users to consume items. On the other hand, depending on domain and provided information, participants sometimes seem to approximate the actual value of recommendations reasonably well.}
}