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    Contextualized Point-of-Interest Recommendation

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    Type
    Conference Paper
    Authors
    Han, Peng
    Li, Zhongxiao
    Liu, Yong
    Zhao, Peilin
    Li, Jing
    Wang, Hao
    Shang, Shuo
    KAUST Department
    Computer Science
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    King Abdullah University of Science and Technology
    Date
    2020-07
    Permanent link to this record
    http://hdl.handle.net/10754/667564
    
    Metadata
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    Abstract
    Point-of-interest (POI) recommendation has become an increasingly important sub-field of recommendation system research. Previous methods employ various assumptions to exploit the contextual information for improving the recommendation accuracy. The common property among them is that similar users are more likely to visit similar POIs and similar POIs would like to be visited by the same user. However, none of existing methods utilize similarity explicitly to make recommendations. In this paper, we propose a new framework for POI recommendation, which explicitly utilizes similarity with contextual information. Specifically, we categorize the context information into two groups, i.e., global and local context, and develop different regularization terms to incorporate them for recommendation. A graph Laplacian regularization term is utilized to exploit the global context information. Moreover, we cluster users into different groups, and let the objective function constrain the users in the same group to have similar predicted POI ratings. An alternating optimization method is developed to optimize our model and get the final rating matrix. The results in our experiments show that our algorithm outperforms all the state-of-the-art methods.
    Citation
    Han, P., Li, Z., Liu, Y., Zhao, P., Li, J., Wang, H., & Shang, S. (2020). Contextualized Point-of-Interest Recommendation. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. doi:10.24963/ijcai.2020/344
    Sponsors
    This work is supported by the National Natural Science Foundation of China (No. 61932004).
    Publisher
    International Joint Conferences on Artificial Intelligence
    Conference/Event name
    Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20)
    ISBN
    9780999241165
    DOI
    10.24963/ijcai.2020/344
    Additional Links
    https://www.ijcai.org/proceedings/2020/344
    https://www.ijcai.org/proceedings/2020/0344.pdf
    ae974a485f413a2113503eed53cd6c53
    10.24963/ijcai.2020/344
    Scopus Count
    Collections
    Conference Papers; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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