Navigation by Revealing Trade-offs for Content-based Recommendations

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The continuation of the work on the initial CityRec System led me to investigate how prospective travelers can make informed decisions about the trade-offs involved in choosing one destination over another. This paper was published in the ENTER22 e-Tourism Conference and was a runner-up for the best paper award.

Tourism is a complex domain for recommender systems because of the high cost of recommending an unsuitable item and the absence of ratings to learn user preferences. Conversational recommender systems have been introduced to provide users with an opportunity to give feedback on items in a turn-based dialog until a final recommendation is accepted. In a scenario such as recommending a city to visit, conversational content-based recommendation may be well-suited since users often struggle to specify their preferences without concrete examples. However, critiquing item features comes with challenges. Users might request item characteristics during recommendation that do not exist in reality, for example, demanding very high item quality for a very low price. To tackle this problem, we present a novel conversational user interface that focuses on revealing the trade-offs of choosing one item over another. The recommendations are driven by a utility function that assesses the user’s preference toward item features while learning the importance of the features to the user. This enables the system to guide the recommendation through the search space faster and accurately over prolonged interaction. We evaluated the system in an online study with 600 participants and found that our proposed paradigm leads to improved perceived accuracy and fewer conversational cycles compared to unit critiquing.

Linus W. Dietz, Sameera Thimbiri Palage, and Wolfgang Wörndl. “Navigation by Revealing Trade-offs for Content-based Recommendations.” In: Information and Communication Technologies in Tourism. Ed. by Jason L. Stienmetz, Berta Ferrer-Rosell, and David Mas- simo. Cham: Springer, Jan. 2022, pp. 149–161. ISBN: 978-3-030-94751-4. DOI: 10.1007/978-3-030-94751-4_14