Abstract
With increasing popularity of location-based social networks, POI recommendation has received much attention recently. Unlike most of the current studies which provide recommendations from perspective of users, in this paper, we focus on the perspective of Point-of-Interest (POI) for predicting potential users for a given POI. We propose a novel vector representation model for the prediction. Many current matrix factorization-based methods only pay attention to combining new information and basic matrix factorization, while in our model, we improve the matrix factorization model itself by replacing dot product with cosine similarity. We also address the problem of randomness of user’s check-in behavior by applying deep neural network to modeling the relationships between the user’s current check-in and context information of current check-in. Extensive experiments conducted on two real-world datasets demonstrate the superior performance of our proposed model and the effectiveness of the factors incorporated in our model.
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Acknowledgement
This work is partially supported by JSPS KAKENHI Grant Number JP15H05708 and the Chongqing Nature Science Foundation under contract number cstc2016jcyjA0398.
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Peng, S., Xie, X., Mine, T., Su, C. (2018). Vector Representation Based Model Considering Randomness of User Mobility for Predicting Potential Users. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. Lecture Notes in Computer Science(), vol 11224. Springer, Cham. https://doi.org/10.1007/978-3-030-03098-8_5
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