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CAMAR: a broad learning based context-aware recommender for mobile applications

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Abstract

The emergence of a large number of mobile apps brings challenges to locate appropriate apps for users, which makes mobile app recommendation an imperative task. In this paper, we first conduct detailed data analysis to show the characteristics of mobile apps which are different with conventional items (e.g., movies, books). Considering the specific property of mobile apps, we propose a broad learning approach for context-aware mobile app recommendation with tensor analysis (CAMAR). Specifically, we utilize a tensor-based framework to effectively integrate app category information and multi-view features on users and apps to facilitate the performance of app recommendation. The multi-dimensional structure is employed to capture the hidden relationships among the app categories and multi-view features. We develop an efficient factorization method which applies Tucker decomposition to jointly learn the full-order interactions among the app categories and features without physically building the tensor. Furthermore, we employ a group \(\ell _{1}\)-norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on two real-world datasets demonstrate the effectiveness of the proposed method.

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Notes

  1. https://play.google.com/store/apps.

  2. https://itunes.apple.com/us/genre/ios/id36?mt=8.

  3. Full-order interactions range from the first-order interactions (i.e., single-view features in each category) to the highest-order interactions (i.e., all combinations of features from multiple views and from different categories).

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Acknowledgements

This work is partially supported in part by the NSFC through Grants 61672453, 61672313, 61702568, Ministry of Education of China through Grant 2017PT18, the Zhejiang University Education Foundation through Grant K18-511120-004, K17-511120-017, and No. K17-518051-021, the National Key Research and Development Program through Grant 2017YFB0202200, the NSF through Grants IIS-1526499 and CNS-1626432, the Major Scientific Project of Zhejiang Lab under Grant No. 2018DG0ZX01, the Program for Guangdong Introducing Innovative and Entrepreneurial Teams through Grant 2017ZT07X355, and the Fundamental Research Funds for the Central Universities under Grant 17lgpy117.

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Tingting Liang

  2. Computer Science Department, University of Illinois at Chicago, Chicago, IL, USA

    Chun-Ta Lu & Philip S. Yu

  3. Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA

    Lifang He

  4. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China

    Liang Chen

  5. Institute for Data Science, Tsinghua University, Beijing, China

    Philip S. Yu

  6. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Haochao Ying & Jian Wu

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  1. Tingting Liang
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Correspondence to Jian Wu.

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Liang, T., He, L., Lu, CT. et al. CAMAR: a broad learning based context-aware recommender for mobile applications. Knowl Inf Syst 62, 3291–3319 (2020). https://doi.org/10.1007/s10115-020-01440-9

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