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.

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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
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
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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

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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]

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

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SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2011

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MobiSys'11

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SIGMOBILE

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

June 28 - July 1, 2011

Maryland, Bethesda, USA

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

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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
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
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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
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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
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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
YILDIRIM YBAYRAKTAR A(2021)THE EFFECT OF MOBILE PROMOTIONS AND APPLICATION QUALITY ON CONSUMERS’ MOBILE BUYING IMPULSIVENESS: A CASE OF COMPARISON BETWEEN ZUBIZU AND HOPIMOBİL PROMOSYONLARIN VE MOBİL UYGULAMA KALİTESİNİN TÜKETİCİLERİN MOBİL ANLIK SATIN ALMA DAVRANIŞI ÜZERİNE ETKİSİ: ZUBİZU VE HOPİ KARŞILAŞTIRMASI ÖRNEĞİÖneri Dergisi10.14783/maruoneri.80413416:56(825-851)Online publication date: 31-Jul-2021
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