@inproceedings{ubo_mods_00154785,
  author = {Naveed, Sidra and Loepp, Benedikt and Ziegler, Jürgen},
  title = {On the Use of Feature-based Collaborative Explanations: An Empirical Comparison of Explanation Styles},
  booktitle = {ExUM ’20: Proceedings of the International Workshop on Transparent Personalization Methods based on Heterogeneous Personal Data},
  year = {2020},
  publisher = {ACM},
  address = {New York},
  pages = {226–232},
  keywords = {User Experience},
  doi = {10.1145/3386392.3399303},
  url = {https://dl.acm.org/doi/10.1145/3386392.3399303?cid=87958660357},
  abstract = {Current attempts to explain recommendations mostly exploit a single type of data, i.e. usually either ratings provided by users for items in collaborative filtering systems, or item features in content-based systems. While this might be sufficient in straightforward recommendation scenarios, the complexity of other situations could require the use of multiple datasources, for instance, depending on the product domain. Even though hybrid systems have a long and successful history in recommender research, the connections between user ratings and item features have only rarely been used for offering more informative and transparent explanations. In previous work, we presented a prototype system based on a feature-weighting mechanism that constitutes an exception, allowing to recommend both items and features based on ratings while offering advanced explanations based on content data. In this paper, we empirically evaluate this prototype in terms of user-oriented aspects and user experience against to widely accepted baselines. Two user studies show that our novel approach outperforms conventional collaborative filtering, while a pure content-based system was perceived in a similarly positive light. Overall, the results draw a promising picture, which becomes particularly apparent from a user perspective when participants were specifically asked to use the explanations: they indicated in their qualitative feedback that they understood them and highly appreciated their availability.}
}


@inproceedings{ubo_mods_00139861,
  author = {Naveed, Sidra and Ziegler, Jürgen},
  title = {Feature-driven interactive recommendations and explanations with collaborative filtering approach},
  booktitle = {ComplexRec 2019: Proceedings of the Workshop on Recommendation in Complex Scenarios},
  year = {2019},
  volume = {2449},
  pages = {10–15},
  keywords = {Interactive recommendations},
  url = {http://ceur-ws.org/Vol-2449/paper2.pdf}
}


@inproceedings{ubo_mods_00139865,
  author = {Millecamp, Martijn and Verbert, Katrien and Naveed, Sidra and Ziegler, Jürgen},
  title = {To explain or not to explain: the effects of personal characteristics when explaining feature-based recommendations in different domains},
  booktitle = {IntRS 2019: Proceedings of the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems},
  year = {2019},
  volume = {2450},
  pages = {10–18},
  keywords = {User modelling},
  url = {http://ceur-ws.org/Vol-2450/paper2.pdf}
}


@inproceedings{ubo_mods_00114820,
  author = {Naveed, Sidra and Donkers, Tim and Ziegler, Jürgen},
  title = {Argumentation-based explanations in recommender systems: Conceptual framework and empirical results},
  booktitle = {UMAP 2018 - Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization},
  year = {2018},
  address = {New York, NY, USA},
  publisher = {ACM},
  pages = {293–298},
  keywords = {User-centered},
  isbn = {9781450357845},
  doi = {10.1145/3213586.3225240}
}


@inproceedings{10.1007/978-3-319-53676-7_2,
  author = {Jannach, Dietmar and Naveed, Sidra and Jugovac, Michael},
  editor = {Bridge, Derek and Stuckenschmidt, Heiner},
  title = {User Control in Recommender Systems: Overview and Interaction Challenges},
  booktitle = {E-Commerce and Web Technologies},
  year = {2017},
  publisher = {Springer International Publishing},
  pages = {21–33},
  abstract = {Recommender systems have shown to be valuable tools that help users find items of interest in situations of information overload. These systems usually predict the relevance of each item for the individual user based on their past preferences and their observed behavior. If the system’s assumption about the users’ preferences are however incorrect or outdated, mechanisms should be provided that put the user into control of the recommendations, e.g., by letting them specify their preferences explicitly or by allowing them to give feedback on the recommendations. In this paper we review and classify the different approaches from the research literature of putting the users into active control of what is recommended. We highlight the challenges related to the design of the corresponding user interaction mechanisms and finally present the results of a survey-based study in which we gathered user feedback on the implemented user control features on Amazon.}
}