Abstract
Nowadays, Recommender Systems (RS) have been applied in most of Location-Based Social Networks (LBSNs). In general, these RSs aim to provide the best points-of-interest (POIs) to users, encouraging them to visit new places or explore more of their preferences. Despite the researches advances in this scenario, there is an opportunity for improvements in the recommendation task. The main reason behind it is related to specific characteristics of this scenario, such as the geolocation of users. In general, most users are not interested in POIs located far from their home or work area. In this sense, we address a new research perspective in the POI Recommendation field, proposing a re-ordering method to be applied after any RS and improve the POIs located nearby from the users’ geolocation. Our assumption is that POIs located on the sub-areas with more activity of a user are more interesting than POIs from new sub-areas. For this reason, we propose to measure the activity level of users in subareas of a city and use it to re-order the POIs recommended before. We evaluate our proposal considering six traditional RSs and three datasets from Yelp, achieving gains up to 15% of precision.
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This work was partially funded by the Brazilian National Institute of Science and Technology for the Web - INWeb, MASWeb, CAPES, CNPq, Finep, Fapesp and Fapemig.
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Chaves, L., Silva, N., Carvalho, R., Pereira, A.C.M., Dias, D.R.C., Rocha, L. (2020). How to Improve the Recommendation’s Accuracy in POI Domains?. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12249. Springer, Cham. https://doi.org/10.1007/978-3-030-58799-4_41
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DOI: https://doi.org/10.1007/978-3-030-58799-4_41
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