Daniel Herzog
ABSTRACT
Most recommender systems (RSs) predict the preferences of individual users; however, in certain scenarios, recommendations need to be made for a group of users. Tourism is a popular domain for group recommendations because people often travel in groups and look for point of interest (POI) sequences for their visits during a trip. In this study, we present different strategies that can be used to recommend POI sequences for groups. In addition, we introduce novel approaches, including a strategy called Split Group, which allows groups to split into smaller groups during a trip. We compared all strategies in a user study with 40 real groups. Our results proved that there was a significant difference in the quality of recommendations generated by using the different strategies. Most groups were willing to split temporarily during a trip, even when they were traveling with persons close to them. In this case, Split Group generated the best recommendations for different evaluation criteria. We use these findings to propose improvements for group recommendation strategies in the tourism domain.
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Index Terms
- User-centered evaluation of strategies for recommending sequences of points of interest to groups
Recommendations
Recommending a Sequence of Points of Interest to a Group of Users in a Mobile Context
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender SystemsRecommender systems (RSs) recommend points of interest (POIs), such as restaurants, museums or monuments, to users. In practice, tourists often travel in groups and want to visit a sequence of POIs along an enjoyable route. Recommending such a sequence ...
Tourrec: a tourist trip recommender system for individuals and groups
RecSys '18: Proceedings of the 12th ACM Conference on Recommender SystemsIn this demo paper, we present TourRec, amobile Recommender System (RS) for tourist trips, sequences of points of interest (POIs) along enjoyable routes. The core of TourRec is a modular, multi-tier architecture facilitating the development and ...
A New Effective Collaborative Filtering Algorithm Based on User's Interest Partition
ISCSCT '08: Proceedings of the 2008 International Symposium on Computer Science and Computational Technology - Volume 01Traditional collaborative filtering algorithms all suffer from inaccurate recommendation and bad scalability. In this paper, we propose a new collaborative filtering algorithm based on user’s interest partition. We divides user’s whole interest into ...
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