Abstract
The explosive growth in the number of smartphone applications (apps) available on the market poses a significant challenge to making personalized recommendations based on user preferences. The training data usually consists of sparse binary implicit feedback (i.e. user-app installation pairs), which results in ambiguities in representing the users interests due to a lack of negative examples. In this paper, we propose two kernel incorporated matrix factorization models to predict user preferences for apps by introducing the categorical information of the apps. The two models extends Probabilistic Matrix Factorization (PMF) by constraining the user and app latent features to be similar to their neighbors in the app-categorical space, and adopts Stochastic Gradient Decent (SGD)-based methods to learn the models. The experimental results show that our model outperforms the baselines, in terms of two ranking-oriented evaluation metrics.
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Acknowledgement
This work is supported by China National Science Foundation (Granted Number 61472253), Research Funds of Science and Technology Commission of Shanghai Municipality (Granted Number 15411952502) and Cross Research Fund of Biomedical Engineering of Shanghai JiaoTong University (YG2015MS61).
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Liu, C., Cao, J., He, J. (2017). Leveraging Kernel Incorporated Matrix Factorization for Smartphone Application Recommendation. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_29
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DOI: https://doi.org/10.1007/978-3-319-55753-3_29
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