TY - GEN
T1 - "please, not now!" A model for timing recommendations
AU - Betzalel, Nofar Dali
AU - Shapira, Bracha
AU - Rokach, Lior
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/9/16
Y1 - 2015/9/16
N2 - Proactive recommender systems push recommendations to users without their explicit request whenever a recommendation that suits a user is available. These systems strive to optimize the match between recommended items and users' preferences. We assume that recommendations might be reflected with low accuracy not only due to the recommended items' suitability to the user, but also because of the recommendations' timings. We therefore claim that it is possible to learn a model of good and bad contexts for recommendations that can later be integrated in a recommender system. Using mobile data collected during a three week user study, we suggest a two-phase model that is able to classify whether a certain context is at all suitable for any recommendation, regardless of its content. Results reveal that a hybrid model that first decides whether it should use a personal or a non-personal timing model, and then classifies accordingly whether the timing is proper for recommendations, is superior to both the personal or non-personal timing models.
AB - Proactive recommender systems push recommendations to users without their explicit request whenever a recommendation that suits a user is available. These systems strive to optimize the match between recommended items and users' preferences. We assume that recommendations might be reflected with low accuracy not only due to the recommended items' suitability to the user, but also because of the recommendations' timings. We therefore claim that it is possible to learn a model of good and bad contexts for recommendations that can later be integrated in a recommender system. Using mobile data collected during a three week user study, we suggest a two-phase model that is able to classify whether a certain context is at all suitable for any recommendation, regardless of its content. Results reveal that a hybrid model that first decides whether it should use a personal or a non-personal timing model, and then classifies accordingly whether the timing is proper for recommendations, is superior to both the personal or non-personal timing models.
KW - Data mining
KW - Mobile
KW - Personalization
KW - Proactivity
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=84962892976&partnerID=8YFLogxK
U2 - 10.1145/2792838.2799672
DO - 10.1145/2792838.2799672
M3 - Conference contribution
AN - SCOPUS:84962892976
T3 - RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems
SP - 297
EP - 300
BT - RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems
PB - Association for Computing Machinery, Inc
T2 - 9th ACM Conference on Recommender Systems, RecSys 2015
Y2 - 16 September 2015 through 20 September 2015
ER -