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Knowledge and Information Systems
Published in: Knowledge and Information Systems 8/2020

14-03-2020 | Regular Paper

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

Authors: Tingting Liang, Lifang He, Chun-Ta Lu, Liang Chen, Haochao Ying, Philip S. Yu, Jian Wu

Published in: Knowledge and Information Systems | Issue 8/2020

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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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Footnotes
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).
 
Literature
1.
Argyriou A, Evgeniou T, Pontil M (2007) Multi-task feature learning. In: Proceedings 21st international conference neural information processing systems, pp 41–48
2.
Argyriou A, Evgeniou T, Pontil M (2008) Convex multi-task feature learning. Mach Learn 73(3):243–272CrossRef
3.
Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th annual conference on computational learning theory, pp 92–100
4.
Cao B, He L, Kong X, Yu PS, Hao Z, Ragin AB (2014) Tensor-based multi-view feature selection with applications to brain diseases. In: Proceedings of the 14th IEEE international conference international of data mining, pp 40–49
5.
Cao B, Zhou H, Li G, Yu PS (2016) Multi-view machines. In: Proceedings of the 9th ACM international conference Web Search data mining, pp 427–436
6.
Cao D, He X, Nie L, Wei X, Hu X, Wu S, Chua TS (2017) Cross-platform app recommendation by jointly modeling ratings and texts. ACM Trans Inform Syst (TOIS) 35(4):37
7.
Chao G, Sun S (2016) Consensus and complementarity based maximum entropy discrimination for multi-view classification. Inform Sci 367:296–310CrossRef
8.
Cichocki A, Mørup M, Smaragdis P, Wang W, Zdunek R (2008) Advances in nonnegative matrix and tensor factorization. Comput Intell Neurosci
9.
Diao Q, Qiu M, Wu CY, Smola AJ, Jiang J, Wang C (2014) Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars). In: Proceedings of the 20th ACM SIGKDD international conference on knowledge. Discovery Data Mining, pp 193–202
10.
Ding C, Zhou D, He X, Zha H (2006) R 1-pca: rotational invariant l 1-norm principal component analysis for robust subspace factorization. In: Proceedings of the 23rd international conference on machine learning, pp 281–288
11.
Ding Z, Fu Y (2014) Low-rank common subspace for multi-view learning. In: Proceedings of the fourteenth international conference on data mining, pp 110–119
12.
Gunawardana A, Shani G (2009) A survey of accuracy evaluation metrics of recommendation tasks. J Mach Learn Res 10(Dec):2935–2962MathSciNetMATH
13.
He L, Lu CT, Ding H, Wang S, Shen L, Yu PS, Ragin AB (2017) Multi-way multi-level kernel modeling for neuroimaging classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 356–364
14.
Huang H, Ding C (2008) Robust tensor factorization using r 1 norm. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–8
15.
Jaakkola T, Meila M, Jebara T (2000) Maximum entropy discrimination. In: Proceedings of the 14th international conference on neural information processing systems, pp 470–476
16.
Karatzoglou A, Baltrunas L, Church K, Böhmer M (2012) Climbing the app wall: enabling mobile app discovery through context-aware recommendations. In: 21st ACM international conference on information and knowledge management, pp 2527–2530
17.
18.
Kotsia I, Patras I (2011) Support tucker machines. In: Proceedings of the IEEE conference on computer vision and pattern recognition , pp 633–640
19.
Li Y, Nie F, Huang H, Huang J (2015) Large-scale multi-view spectral clustering via bipartite graph. In: Proceedings of the AAAI, pp 2750–2756
20.
Liang T, Chen L, Ying X, Philip SY, Wu J, Zheng Z (2017) Mobile application rating prediction via feature-oriented matrix factorization. In: Proceedings of the 24th IEEE international conference on web services, pp 261–268
21.
Lin J, Sugiyama K, Kan MY, Chua TS (2013) Addressing cold-start in app recommendation: latent user models constructed from twitter followers. In: Proceedings of the 36th annual international ACM conference on research and development in information retrieval, pp 283–292
22.
Liu B, Kong D, Cen L, Gong NZ, Jin H, Xiong H (2015) Personalized mobile app recommendation: Reconciling app functionality and user privacy preference. In: Proceedings of the eighth ACM international conference on web search and data mining, pp 315–324
23.
Liu B, Wu Y, Gong NZ, Wu J, Xiong H, Ester M (2016) Structural analysis of user choices for mobile app recommendation. ACM Trans Knowl Discov Data (TKDD) 11(2):17
24.
Lu CT, He L, Ding H, Yu PS (2017) Learning from multi-view structural data via structural factorization machines. arXiv preprint arXiv:​1704.​03037
25.
Lu CT, He L, Shao W, Cao B, Yu PS (2017) Multilinear factorization machines for multi-task multi-view learning. In: Proceedings of the tenth ACM international conference on web search and data mining, pp 701–709
26.
Lu CT, Xie S, Shao W, He L, Yu PS (2016) Item recommendation for emerging online businesses. In: Proceedings of the 25th international joint conference on artificial intelligence, pp 3797–3803
27.
Mao L, Sun S (2016) Soft margin consistency based scalable multi-view maximum entropy discrimination. In: Proceedings of the 25th international joint conference on artificial intelligence, pp 1839–1845
28.
Meng J, Zheng Z, Tao G, Liu X (2016) User-specific rating prediction for mobile applications via weight-based matrix factorization. In: International conference on web services (ICWS), pp 728–731. IEEE
29.
30.
Nigam K, Ghani R (2000) Analyzing the effectiveness and applicability of co-training. In: Proceedings of the 9th ACM international conference on information and knowledge management, pp 86–93
31.
Van den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. In: Proceedings of the 27th international conference neural information processing systems, pp 2643–2651
32.
Ren Z, Zhang C, Li T, Hu Z (2017) Taxonomy-induced matrix factorization for inferring preference of mobile telecom users. In: 18th IEEE international conference on mobile data management (MDM), pp 328–331. IEEE
33.
Rendle S (2010) Factorization machines. In: Proceedings of the 10th IEEE international conference on data mining, pp 995–1000
34.
Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. Springer, BerlinCrossRef
35.
Salakhutdinov R, Mnih A (2007) Probabilistic matrix factorization. In: Proceedings of the 21st international conference on neural information processing systems, vol 1, pp 2–1
36.
Shalev-Shwartz S, Ben-David S (2014) Understanding machine learning: from theory to algorithms. Cambridge University Press, CambridgeCrossRef
37.
Shao W, He L, Lu Ct, Philip SY (2016) Online multi-view clustering with incomplete views. In: Proceedings of the IEEE international conference on big data, pp 1012–1017
38.
Shao W, He L, Lu CT, Wei X, Yu PS (2016) Online unsupervised multi-view feature selection. In: Proceedings of the 16th IEEE international conference on data mining, pp 1203–1208
39.
Shao W, He L, Yu PS (2015) Clustering on multi-source incomplete data via tensor modeling and factorization. In: Proceedings of the 19th Pacific-Asia conference knowledge discovery data mining, pp 485–497
40.
Shao W, He L, Yu PS (2015) Multiple incomplete views clustering via weighted nonnegative matrix factorization with \(l\_ \{2, 1\}\) regularization. In: Proceedings of the ECML PKDD European conference on machine learning and principles and practice of knowledge discovery in databases, pp 318–334. Springer
41.
Sun S, Chao G (2013) Multi-view maximum entropy discrimination. In: Proceedings of the 22nd international joint conference on artificial intelligence, pp 1706–1712
42.
Sun S, Shawe-Taylor J (2010) Sparse semi-supervised learning using conjugate functions. J Mach Learn Res 11(Sep):2423–2455MathSciNetMATH
43.
Sun S, Xie X, Yang M (2016) Multiview uncorrelated discriminant analysis. IEEE Trans Cybern 46(12):3272–3284CrossRef
44.
Wang H, Nie F, Huang H, Yan J, Kim S, Risacher S, Saykin A, Shen L (2012) High-order multi-task feature learning to identify longitudinal phenotypic markers for alzheimer’s disease progression prediction. In: Proceedings of the 26th international conference on neural information processing, pp 1277–1285
45.
Wong WK, Lai Z, Xu Y, Wen J, Ho CP (2015) Joint tensor feature analysis for visual object recognition. IEEE Trans Cybern 45(11):2425–2436CrossRef
46.
Xie X, Sun S (2015) Multi-view twin support vector machines. Intell Data Anal 19(4):701–712CrossRef
47.
Yan B, Chen G (2011) Appjoy: personalized mobile application discovery. In: Proceedings of the 9th international conference on mobile systems, applications, and services, pp 113–126
48.
Yang P, Gao W (2013) Multi-view discriminant transfer learning. In: Proceedings of the twenty-second international joint conference on artificial intelligence, pp 1848–1854
49.
Yao Y, Zhao WX, Wang Y, Tong H, Xu F, Lu J (2017) Version-aware rating prediction for mobile app recommendation. ACM Trans Inf Syst (TOIS) 35(4):38CrossRef
50.
Yin P, Luo P, Lee WC, Wang M (2013) App recommendation: a contest between satisfaction and temptation. In: Proceedings of the sixth ACM international conference on web search and data mining, pp 395–404
51.
Yu S, Krishnapuram B, Rosales R, Rao RB (2011) Bayesian co-training. J Mach Learn Res 12:2649–2680MathSciNetMATH
52.
Zhao L, Lu Z, Pan SJ, Yang Q (2016) Matrix factorization+ for movie recommendation. In: Proceedings of the 25th international joint conference on artificial intelligence, pp 3945–3951
53.
Zheng L, Noroozi V, Yu PS (2017) Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the tenth ACM international conference on web search and data mining, pp 425–434
54.
Zhu H, Liu C, Ge Y, Xiong H, Chen E (2015) Popularity modeling for mobile apps: a sequential approach. IEEE Trans Cybern 45(7):1303–1314CrossRef
55.
Zhu H, Xiong H, Ge Y, Chen E (2014) Mobile app recommendations with security and privacy awareness. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 951–960
56.
Zhu H, Xiong H, Ge Y, Chen E (2015) Discovery of ranking fraud for mobile apps. IEEE Trans Knowl Data Eng 27(1):74–87CrossRef
57.
Zong L, Zhang X, Zhao L, Yu H, Zhao Q (2017) Multi-view clustering via multi-manifold regularized non-negative matrix factorization. Neural Netw 88:74–89CrossRef
Metadata
Title
CAMAR: a broad learning based context-aware recommender for mobile applications
Authors
Tingting Liang
Lifang He
Chun-Ta Lu
Liang Chen
Haochao Ying
Philip S. Yu
Jian Wu
Publication date
14-03-2020
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 8/2020
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-020-01440-9

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