@inproceedings{ubo_mods_00089097,
  author = {Feuerbach, Jan and Loepp, Benedikt and Barbu, Catalin-Mihai and Ziegler, Jürgen},
  title = {Enhancing an Interactive Recommendation System with Review-based Information Filtering},
  booktitle = {Proceedings of the 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS ’17)},
  series = {CEUR workshop proceedings},
  year = {2017},
  volume = {1884},
  pages = {2–9},
  keywords = {User Reviews},
  abstract = {Integrating interactive faceted filtering with intelligent recommendation techniques has shown to be a promising means for increasing user control in Recommender Systems. In this paper, we extend the concept of blended recommending by automatically extracting meaningful facets from social media by means of Natural Language Processing. Concretely, we allow users to influence the recommendations by selecting facet values and weighting them based on information other users provided in their reviews. We conducted a user study with an interactive recommender implemented in the hotel domain. This evaluation shows that users are consequently able to find items fitting interests that are typically difficult to take into account when only structured content data is available. For instance, the extracted facets representing the opinions of hotel visitors make it possible to effectively search for hotels with comfortable beds or that are located in quiet surroundings without having to read the user reviews.},
  url = {http://ceur-ws.org/Vol-1884/paper1.pdf}
}


@inproceedings{ubo:72486,
  author = {Barbu, Catalin-Mihai},
  chapter = {},
  title = {Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources},
  year = {2016},
  address = {New York, NY, USA},
  publisher = {ACM},
  pages = {447–450},
  keywords = {recommender systems},
  abstract = {Current recommender systems mostly do not take into account as well as they might the wealth of information available in social media, thus preventing the user from obtaining a broad and reliable overview of different opinions and ratings on a product. Furthermore, there is a lack of user control over the recommendation process–which is mostly fully automated and does not allow the user to influence the sources and mechanisms by which recommendations are produced–as well as over the presentation of recommended items. Consequently, recommendations are often not transparent to the user, are considered to be less trustworthy, or do not meet the user’s situational needs. This work will investigate the theoretical foundations for user-controllable, interactive methods of recommending, will develop techniques that exploit social media data in conjunction with other sources, and will validate the research empirically in the area of e-commerce product recommendations. The methods developed are intended to be applicable in a wide range of recommending and decision support scenarios.},
  isbn = {978-1-4503-4035-9},
  doi = {10.1145/2959100.2959104},
  url = {https://dl.acm.org/citation.cfm?id=2959104},
  booktitle = {Proceedings of the 10th ACM Conference on Recommender Systems}
}