Abstract
How many of your friends, with whom you enjoy spending some time, live close by? How many people are at your reach, with whom you could have a nice conversation? We introduce a measure of enjoyability that may be the basis for a new class of location-based services aimed at maximizing the likelihood that two persons, or a group of people, would enjoy spending time together. Our enjoyability takes into account both topic similarity between two users and the users’ tendency to connect to people with similar or dissimilar interest. We computed the enjoyability on two datasets of geo-located tweets, and we reasoned on the applicability of the obtained results for producing friend recommendations. We aim at suggesting couples of users which are not friends yet, but which are frequently co-located and maximize our enjoyability measure. By taking into account the spatial dimension, we show how 50 % of users may find at least one enjoyable person within 10 km of their two most visited locations. Our results are encouraging, and open the way for a new class of recommender systems based on enjoyability.
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Notes
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GPS coordinates bounding box: Rome (12.234498, 41.655642, 12.85576, 42.141028), San Francisco (\(-\)122.667, 36.8378, \(-\)121.2949, 38.0771).
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This work has been partially supported by the European Commission under the SMARTCITIES Project n. FP7-ICT-609042, PETRA.
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Guidotti, R., Berlingerio, M. (2016). Where Is My Next Friend? Recommending Enjoyable Profiles in Location Based Services. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds) Complex Networks VII. Studies in Computational Intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_5
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DOI: https://doi.org/10.1007/978-3-319-30569-1_5
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