Name
Abstract | ||
|---|---|---|
The research topic is to investigate how recommender systems can help people develop new preferences and goals. Recommender systems nowadays typically use historical user data to predict users' current preferences. However, users might want to develop new preferences. Traditional recommendation approaches would fail in this situation as these approaches typically provide users with recommendations that match their current preference. In addition, users are not always aware of preference development due to the issue of filter bubbles. In this case, recommender systems could also be there to help them step away from their bubbles by suggesting new preferences for them to develop. The research will take a multidisciplinary approach in which insights from psychology on decision making and habit formation are paired with new approaches to recommendation that included preference evolution, interactive exploration methods and goal-directed approaches. Moreover, when evaluating the success of such algorithms, (longitudinal) experiments combining objective behavioral data and subjective user experience will be required to fine-tune and optimize recommendation approaches.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3298689.3347054 | Proceedings of the 13th ACM Conference on Recommender Systems |
Keywords | Field | DocType |
behavior change, preference developing, recommender system, user goals, user-centric evaluation | Recommender system,World Wide Web,Computer science,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6243-6 | 0 | 0.34 |
References | Authors | |
0 | 1 | |