Authors:
Jadna Almeida da Cruz
1
;
Frederico Durão
2
and
Rosaldo J. F. Rossetti
1
Affiliations:
1
Artificial Intelligence and Computer Science Lab (LIACC) of the Faculty of Engineering, University of Porto, Porto, Portugal
;
2
Federal University of Bahia, Salvador, Bahia, Brazil
Keyword(s):
Diversification, Group, Recommendation, Points of Interest.
Abstract:
With the massive availability and use of the Internet, the search for Points of Interest (POI) is becoming an arduous task. POI Recommendation Systems have, therefore, emerged to help users search for and discover relevant POIs based on their preferences and behaviors. These systems combine different information sources and present numerous research challenges and questions. POI recommender systems traditionally focused on providing recommendations to individual users based on their preferences and behaviors. However, there is an increasing need to recommend POIs to groups of users rather than just individuals. People often visit POIs together in groups rather than alone. Thus, some studies indicate that the further users travel, the less relevant the POIs are to them. In addition, the recommendations belong to the same category, without diversity. This work proposes a POI Recommendation System for a group using a diversity algorithm based on members’ preferences and their locations.
The evaluation of the proposal involved both online and offline experiments. Accuracy metrics were used in the evaluation, and it was observed that the level at which the results were analyzed was relevant. For the top 3, recommendations without diversity performed better, but diversification positively impacted the results at the top 5 and 10 levels.
(More)