research-article

AppJoy: personalized mobile application discovery

Authors:

Guanling ChenAuthors Info & Claims

MobiSys '11: Proceedings of the 9th international conference on Mobile systems, applications, and services

Pages 113 - 126

https://doi.org/10.1145/1999995.2000007

Published: 28 June 2011 Publication History

Abstract

The explosive growth of the mobile application market has made it a significant challenge for the users to find interesting applications in crowded App Stores. To alleviate this problem, existing industry solutions often use the users' application download history and possibly their ratings to recommend applications that might interest them, much like Amazon's book recommendations. However, the user downloading an application is a weak indicator of whether the user likes that application, particularly if the application is free and the user just wants to try it out. Using application ratings, on the other hand, suffers from tedious manual input and potential data sparsity problems.

In this paper, we present the AppJoy system that makes personalized application recommendations by analyzing how the user actually uses her installed applications. Based on all participants' application usage records, AppJoy employs an item-based collaborative filtering algorithm for individualized recommendations. We discuss AppJoy's design and implementation, and the evaluation shows that it consumes little resource on the off-the-shelf Google Android phones. AppJoy has been available in the Android Market and used by more than 4600 users. The AppJoy's prediction algorithm provided reasonably accurate usage estimate of the recommended applications after they were installed. We also found AppJoy to be effective as the users interacted with recommended applications longer than other applications.

References

[1]

AppBrain. http://www.appbrain.com/.

[2]

AppsFire. http://www.appsfire.com/.

[3]

Google Android Developers. http://developer.android.com/.

[4]

J. Bennett and S. Lanning. The Netflix Prize. In The ACM SIGKDD Cup and Workshop, 2007.

[5]

J. R. Bult and T. Wansbeek. Optimal selection for direct mail. Marketing Science, 14(4), 1995.

[6]

K. Church and B. Smyth. Understanding mobile information needs. In Proceedings of MobileHCI, 2008.

Digital Library

[7]

H. Falaki, R. Mahajan, S. Kandula, D. Lymberopoulos, R. Govindan, and D. Estrin. Diversity in smartphone usage. In Proceedings of MobiSys, 2010.

Digital Library

[8]

G. Holmberg and M. Torrens. Musicstrands: A platform for discovering and exploring music. University of Michigan Library, 2005.

[9]

J. Layton. How Pandora Radio works. 2006.

[10]

D. Lemire and A. Maclachlan. Slope one predictors for online rating-based collaborative filtering. In Proceedings of SIAM on Data Mining, 2005.

[11]

Why the mobile Web is disappointing end-users. Equation Research Report, Oct. 2009.

[12]

S. Perez. Mobile app marketplace. ReadWriteWeb.com, Mar. 2010.

[13]

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of WWW, 2001.

Digital Library

[14]

J. B. Schafer, J. Konstan, and J. Riedi. Recommender systems in E-commerce. In Proceedings of the 1st ACM Conference on Electronic Commerce (ACM-EC), 1999.

Digital Library

[15]

C. Shepard, C. Tossel, A. Rahmati, L. Zhong, and P. Kortum. LiveLab: Measuring wireless networks and smartphone users in the field. In Proceedings of HotMetrics, 2010.

Digital Library

[16]

T. Sohn, K. A. Li, W. G. Griswold, and J. D. Hollan. A diary study of mobile information needs. In Proceedings of CHI, 2008.

Digital Library

[17]

A. Toscher, M. Jahrer, and R. M. Bell. The BigChaos solution to the Netflix Grand Prize, 2009.

[18]

W. Woerndl, C. Schueller, and R. Wojtech. A hybrid recommender system for context-aware recommendations of mobile applications. In Proceedings of the IEEE ICDE, 2007.

Digital Library

Cited By

Niimi J(2024)Prediction of Behavioral Loyalty Using Different Dimensionality Data異なる次元数のデータを同時に投入した行動的ロイヤルティ推計手法の提案Ouyou toukeigaku10.5023/jappstat.53.1553:1(15-32)Online publication date: 2024
https://doi.org/10.5023/jappstat.53.15
Zheng QYu DWang DYan WFu Y(2024)App Recommendation Model Based on Heterogeneous Graph Collaborative Filtering2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650932(1-7)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650932
Zeng FLi YXiao JYang D(2024)DDHCN: Dual decoder Hyperformer convolutional network for Downstream-Adaptable user representation learning on app usageExpert Systems with Applications10.1016/j.eswa.2023.121564237(121564)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121564
Show More Cited By

Index Terms

AppJoy: personalized mobile application discovery
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

Personalized Recommendation Algorithm Using User Demography Information
WKDD '09: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining

Personalized recommendation systems are web-based systems that aim at predicting a user’s interest on available products and services by relying on previously rated items and dealing with the problem of information and product overload. User demography ...
Personalized rough-set-based recommendation by integrating multiple contents and collaborative information

In recent years, explosively-growing information makes the users confused in making decisions among various kinds of products such as music, movies, books, etc. As a result, it is a challenging issue to help the user identify what she/he prefers. To ...
Representation learning with collaborative autoencoder for personalized recommendation
Abstract
In the past decades, recommendation systems have provided lots of valuable personalized suggestions for the users to address the problem of information over-loaded. Collaborative Filtering (CF) is one of the most commonly applied and successful ...
Highlights
- Two different autoencoders are used to capture characteristics for users and items.
- Manifold regularization is integrated into autoencoder for user’s features learning.
- The comprehensive experiments evaluate the effectiveness of ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MobiSys '11: Proceedings of the 9th international conference on Mobile systems, applications, and services

June 2011

430 pages

ISBN:9781450306430

DOI:10.1145/1999995

General Chair:
Ashok Agrawala
University of Maryland, USA
,
Program Chairs:
Mark D. Corner
UMass Amherst, USA
,
David Wetherall
University of Wash. & Intel Labs, USA

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

In-Cooperation

SIGOPS: ACM Special Interest Group on Operating Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MobiSys'11

Sponsor:

SIGMOBILE

MobiSys'11: The 9th International Conference on Mobile Systems, Applications, and Services

June 28 - July 1, 2011

Maryland, Bethesda, USA

Acceptance Rates

Overall Acceptance Rate 274 of 1,679 submissions, 16%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

122
Total Citations
View Citations
1,861
Total Downloads

Downloads (Last 12 months)66
Downloads (Last 6 weeks)6

Reflects downloads up to 05 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Niimi J(2024)Prediction of Behavioral Loyalty Using Different Dimensionality Data異なる次元数のデータを同時に投入した行動的ロイヤルティ推計手法の提案Ouyou toukeigaku10.5023/jappstat.53.1553:1(15-32)Online publication date: 2024
https://doi.org/10.5023/jappstat.53.15
Zheng QYu DWang DYan WFu Y(2024)App Recommendation Model Based on Heterogeneous Graph Collaborative Filtering2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650932(1-7)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650932
Zeng FLi YXiao JYang D(2024)DDHCN: Dual decoder Hyperformer convolutional network for Downstream-Adaptable user representation learning on app usageExpert Systems with Applications10.1016/j.eswa.2023.121564237(121564)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121564
Adje KLetaifa AHaddad MHabachi O(2023)Smart City Based on Open Data: A SurveyIEEE Access10.1109/ACCESS.2023.328343611(56726-56748)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3283436
Liang YLiu LHuangfu LWang ZGuo B(2023)DeepApp: characterizing dynamic user interests for mobile application recommendationWorld Wide Web10.1007/s11280-023-01161-326:5(2623-2645)Online publication date: 2-May-2023
https://doi.org/10.1007/s11280-023-01161-3
Li TLi YHoque MXia TTarkoma SHui P(2022)To What Extent We Repeat Ourselves? Discovering Daily Activity Patterns Across Mobile App UsageIEEE Transactions on Mobile Computing10.1109/TMC.2020.302198721:4(1492-1507)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TMC.2020.3021987
Li TXia TWang HTu ZTarkoma SHan ZHui P(2022)Smartphone App Usage Analysis: Datasets, Methods, and ApplicationsIEEE Communications Surveys & Tutorials10.1109/COMST.2022.316317624:2(937-966)Online publication date: Oct-2023
https://doi.org/10.1109/COMST.2022.3163176
Gupta SRavipati SKumar RVani V(2022)Enhancing Security Mechanism in Smart Phones Using CrowdsourcingCyber Warfare, Security and Space Research10.1007/978-3-031-15784-4_24(303-310)Online publication date: 28-Aug-2022
https://doi.org/10.1007/978-3-031-15784-4_24
Prashar SGupta PParsad CVijay T(2021)Predicting Shoppers' Continuous Buying Intention Using Mobile AppsResearch Anthology on E-Commerce Adoption, Models, and Applications for Modern Business10.4018/978-1-7998-8957-1.ch029(538-555)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-8957-1.ch029
Ganchev IJi Z(2021)Smart Real-Time Recommendation of Mobile ServicesWSEAS TRANSACTIONS ON SYSTEMS AND CONTROL10.37394/23203.2021.16.6016(655-667)Online publication date: 20-Dec-2021
https://doi.org/10.37394/23203.2021.16.60
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents