Abstract
The large volume and variety of apps pose a great challenge for people to choose appropriate apps. As a consequence, app recommendation is becoming increasingly important. Recently, app usage data which record the sequence of apps being used by a user have become increasingly available. Such data record the usage context of each instance of app use, i.e., the app instances being used together with this app (within a short time window). Our empirical data analysis shows that a user has a pattern of app usage contexts. More importantly, the similarity in the two users’ preferences over mobile apps is correlated with the similarity in their app usage context patterns. Inspired by these important observations, this paper tries to leverage the predictive power of app usage context patterns for effective app recommendation. To this end, we propose a novel neural approach which learns the embeddings of both users and apps and then predicts a user’s preference for a given app. Our neural network structure models both a user’s preference over apps and the user’s app usage context pattern in a unified way. To address the issue of unbalanced training data, we introduce several sampling methods to sample user-app interactions and app usage contexts effectively. We conduct extensive experiments using a large real app usage data. Comparative results demonstrate that our approach achieves higher precision and recall, compared with the state-of-the-art recommendation methods.
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Bu, N., Niu, S., Yu, L., Ma, W., Long, G.: Bridging semantic gap between app names: Collective matrix factorization for similar mobile app recommendation. In: WISE, pp. 324–339 (2016)
Candillier, L., Meyer, F., Fessant, F.: Designing specific weighted similarity measures to improve collaborative filtering systems. In: Industrial Conference on Data Mining, pp. 242–255 (2008)
Chen, W., Hsu, W., Lee, M.L.: Making recommendations from multiple domains. In: KDD, pp. 892–900 (2013)
Chen, H., Niu, D., Lai, K., Xu, Y., Ardakani, M.: Separating-plane factorization models: Scalable recommendation from one-class implicit feedback. In: CIKM, pp. 669–678 (2016)
Ganguly, S., Gupta, M., Varma, V., Pudi, V., et al.: Author2vec: Learning author representations by combining content and link information. In: WWW, pp. 49–50 (2016)
Grbovic, M., Radosavljevic, V., Djuric, N., Bhamidipati, N., Savla, J., Bhagwan, V., Sharp, D.: E-commerce in your inbox: Product recommendations at scale. In: KDD, pp. 1809–1818 (2015)
Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: KDD, pp. 855–864 (2016)
Gupta, V.S., Kohli, S.: Automated interestingness calculator for mobile app recommendationvICRITO, pp. 1–6 (2015)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)
Huawei mate 9. http://consumer.huawei.com/en/mobile-phones/mate9/index.htm (2017)
Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv:1412.6980 (2014)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Kullback–leibler divergence. https://en.wikipedia.org/wiki/Kullback (2017)
Li, H., Ai, W., Liu, X., Tang, J., Huang, G., Feng, F., Mei, Q.: Voting with their feet: Inferring user preferences from app management activities. In: WWW, pp. 1351–1362 (2016)
Li, H., Ge, Y., Zhu, H.: Point-of-interest recommendations: Learning potential check-ins from friends. In: KDD (2016)
Linden, G., Smith, B., York, J.: Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Liu, B., Fu, Y., Yao, Z., Xiong, H.: Learning geographical preferences for point-of-interest recommendation. In: KDD, pp. 1043–1051 (2013)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp. 3111–3119 (2013)
Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: ICDM, pp. 502–511 (2008)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: International Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)
Shi, K., Ali, K.: Getjar mobile application recommendations with very sparse datasets. In: KDD, pp. 204–212 (2012)
Srivastava, N., Salakhutdinov, R.R.: Multimodal learning with deep boltzmann machines. In: Advances in neural information processing systems, pp. 2222–2230 (2012)
Statista. https://www.statista.com/ (2017)
Tomar, A., Godin, F., Vandersmissen, B., De Neve, W., Van de Walle, R.: Towards twitter hashtag recommendation using distributed word representations and a deep feed forward neural network. In: ICACCI, pp. 362–368 (2014)
Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: KDD, pp. 1235–1244 (2015)
Woerndl, W., Schueller, C., Wojtech, R.: A hybrid recommender system for context-aware recommendations of mobile applications. In: Data Engineering Workshop, pp. 871–878 (2007)
Xia, X., Wang, X., Li, J., Zhou, X.: Multi-objective mobile app recommendation: A system-level collaboration approach. Comput. Electr. Eng. 40(1), 203–215 (2014)
Xiao, Y., Shi, Q.: Research and implementation of hybrid recommendation algorithm based on collaborative filtering and word2vec. In: ISCID, vol. 2, pp. 172–175 (2015)
Xie, M., Yin, H., Xu, F., Wang, H., Zhou, X.: Graph-based metric embedding for next poi recommendation. In: WISE, pp. 207–222 (2016)
Zhang, H., Yang, Y., Luan, H., Yang, S., Chua, T.S.: Start from scratch: Towards automatically identifying, modeling, and naming visual attributes. In: ACM MM, pp. 187–196 (2014)
Zhang, W., Wang, J.: A collective bayesian poisson factorization model for cold-start local event recommendation. In: KDD, pp. 1455–1464 (2015)
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: KDD, pp. 353–362 (2016)
Zhong, E., Liu, N., Shi, Y., Rajan, S.: Building discriminative user profiles for large-scale content recommendation. In: KDD, pp. 2277–2286 (2015)
Zhu, H., Chen, E., Yu, K., Cao, H., Xiong, H., Tian, J.: Mining personal context-aware preferences for mobile users. In: ICDM, pp. 1212–1217 (2012)
Acknowledgements
This research is supported in part by 973 Program (No. 2014CB340303), NSFC (No. 61772341, 61472254, 61170238, 61602297 and 61472241), and Singapore NRF (CREATE E2S2). This work is also supported by the Program for Changjiang Young Scholars in University of China, the Program for China Top Young Talents, and the Program for Shanghai Top Young Talents.
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This article belongs to the Topical Collection: Special Issue on Web and Big Data
Guest Editors: Junjie Yao, Bin Cui, Christian S. Jensen, and Zhe Zhao
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Xu, Y., Zhu, Y., Shen, Y. et al. Leveraging app usage contexts for app recommendation: a neural approach. World Wide Web 22, 2721–2745 (2019). https://doi.org/10.1007/s11280-018-0543-8
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DOI: https://doi.org/10.1007/s11280-018-0543-8