Version-sensitive mobile App recommendation

被引:32
作者
Cao, Da [1 ]
Nie, Liqiang [2 ]
He, Xiangnan [3 ]
Wei, Xiaochi [4 ]
Shen, Jialie [5 ]
Wu, Shunxiang [1 ]
Chua, Tat-Seng [3 ]
机构
[1] Xiamen Univ, Dept Automat, Xiamen 361005, Fujian, Peoples R China
[2] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China
[3] Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore
[4] Beijing Inst Technol, Sch Comp Sci, Beijing 100081, Peoples R China
[5] Singapore Management Univ, Sch Informat Syst, Singapore 178902, Singapore
基金
新加坡国家研究基金会; 中国国家自然科学基金;
关键词
Mobile App recommendation; Version progression; Data sparsity problem; Cold-start problem; Plug-in component; Online environment; FACTORIZATION;
D O I
10.1016/j.ins.2016.11.025
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Being part and parcel of the daily life for billions of people all over the globe, the domain of mobile Applications (Apps) is the fastest growing sector of mobile market today. Users, however, are frequently overwhelmed by the vast number of released Apps and frequently updated versions. Towards this end, we propose a novel version-sensitive mobile App recommendation framework. It is able to recommend appropriate Apps to right users by jointly exploring the version progression and dual-heterogeneous data. It is helpful for alleviating the data sparsity problem caused by version division. As a byproduct, it can be utilized to solve the in-matrix and out-of-matrix cold-start problems. Considering the progression of versions within the same categories, the performance of our proposed framework can be further improved. It is worth emphasizing that our proposed version progression modeling can work as a plug-in component to be embedded into most of the existing latent factor-based algorithms. To support the online learning, we design an incremental update strategy for the framework to adapt the dynamic data in real-time. Extensive experiments on a real-world dataset have demonstrated the promising performance of our proposed approach with both offline and online protocols. Relevant data, code, and parameter settings are available at http://apprec.wixsite.comiversion. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码:161 / 175
页数:15
相关论文
共 48 条
[1]  
Ali K., 2012, KDD 12 P 18 ACM SIGK, P204
[2]  
[Anonymous], 2013, P 7 ACM C RECOMMENDE
[3]  
[Anonymous], P 39 INT ACM SIGIR C
[4]  
[Anonymous], 2013, ADV NEURAL INF PROCE
[5]  
[Anonymous], 2007, Proceedings of the 16th International Conference on World Wide Web
[6]  
Bao Y, 2014, AAAI CONF ARTIF INTE, P2
[7]  
Bhandari Upasna, 2013, Information Retrieval Technology. 9th Asia Information Retrieval Societies Conference, AIRS 2013. Proceedings: LNCS 8281, P440, DOI 10.1007/978-3-642-45068-6_38
[8]   Latent Dirichlet allocation [J].
Blei, DM ;
Ng, AY ;
Jordan, MI .
JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) :993-1022
[9]   Recommender systems survey [J].
Bobadilla, J. ;
Ortega, F. ;
Hernando, A. ;
Gutierrez, A. .
KNOWLEDGE-BASED SYSTEMS, 2013, 46 :109-132
[10]  
Bohmer M., 2013, P INT C INT US INT, P267, DOI 10.1145/2449396.2449431