Leveraging Kernel Incorporated Matrix Factorization for Smartphone Application Recommendation

Chenyang Liu, Jian Cao, Jing He

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.

External IDs:dblp:conf/dasfaa/LiuCH17