2nd Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning - RecSysKTL 2018, Vancouver (Canadá). 06-07 octubre 2018
Resumen:
Finding the next venue to be visited by a user in a specific city is an interesting, but challenging, problem. Different techniques have been proposed, combining collaborative, content, social, and geographical signals; however it is not trivial to decide which technique works best, since this may depend on the data density or the amount of activity logged for each user or item. At the same time, cross-domain strategies have been exploited in the recommender systems literature when dealing with (very) sparse situations, such as those inherently arising when recommendations are produced based on information from a single city.
In this paper, we address the problem of venue recommendation from a novel perspective: applying cross-domain recommendation techniques considering each city as a different domain. We perform an experimental comparison of several recommendation techniques in a temporal split under two conditions: single-domain (only information from the target city is considered) and crossdomain (information from many other cities is incorporated into the recommendation algorithm). For the latter, we have explored
two strategies to transfer knowledge from one domain to another: testing the target city and training a model with information of the k cities with more ratings or only using the k closest cities.
Our results show that, in general, applying cross-domain by proximity increases the performance of the majority of the recommenders in terms of relevance. This is the first work, to the best of our knowledge, where so many domains (eight) are combined in the tourism context where a temporal split is used, and thus we expect these results could provide readers with an overall picture of what can be achieved in a real-world environment.
Fecha de publicación: 2018-10-06.
Cita:
P. Sánchez, A. Bellogín, A novel approach for venue recommendation using cross-domain techniques, 2nd Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning - RecSysKTL 2018, Vancouver (Canadá). 06-07 octubre 2018.
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