research-article

Structural Analysis of User Choices for Mobile App Recommendation

Authors:

Bin Liu,

Yao Wu,

Neil Zhenqiang Gong,

Junjie Wu,

Hui Xiong,

Martin EsterAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 2

Article No.: 17, Pages 1 - 23

https://doi.org/10.1145/2983533

Published: 15 November 2016 Publication History

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Abstract

Advances in smartphone technology have promoted the rapid development of mobile apps. However, the availability of a huge number of mobile apps in application stores has imposed the challenge of finding the right apps to meet the user needs. Indeed, there is a critical demand for personalized app recommendations. Along this line, there are opportunities and challenges posed by two unique characteristics of mobile apps. First, app markets have organized apps in a hierarchical taxonomy. Second, apps with similar functionalities are competing with each other. Although there are a variety of approaches for mobile app recommendations, these approaches do not have a focus on dealing with these opportunities and challenges. To this end, in this article, we provide a systematic study for addressing these challenges. Specifically, we develop a structural user choice model (SUCM) to learn fine-grained user preferences by exploiting the hierarchical taxonomy of apps as well as the competitive relationships among apps. Moreover, we design an efficient learning algorithm to estimate the parameters for the SUCM model. Finally, we perform extensive experiments on a large app adoption dataset collected from Google Play. The results show that SUCM consistently outperforms state-of-the-art Top-N recommendation methods by a significant margin.

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Structural Analysis of User Choices for Mobile App Recommendation
1. Information systems

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Reviews

Reviewer: Amos O Olagunju

Mobile apps are rapidly evolving due to ongoing improvements in smartphone technology. However, the use of mobile devices introduces some obstacles for rookie users. How should novice users locate suitable apps from a hierarchy of apps to accomplish different tasks How should rival mobile apps with comparable operational services be effectively recommended to users In efforts to improve app recommendation based on the interests and proclivities of users, Liu and colleagues present a structural user choice model (SUCM) for ascertaining the well-organized classification and viable associations between apps. In the SUCM, a user first decides on the type of apps to use prior to selecting the suitable app category/subcategory. The model requires the user to choose from the root of mobile apps, and then proceed through levels of categories and subcategories of apps until the user's preferences are optimally satisfied. A probabilistic algorithm called the softmax function is used to examine the relationships among competing nodes of apps, in efforts to minimize the processing times for identifying the user's preferences from numerous alternative app categories. This reliable algorithm is predicated on the theoretical foundations axioms of user preferences and choices. The authors compellingly present the log likelihood algorithms for understanding the dynamic parameters that are applied to effectively select among competing apps for the users. The authors uniquely illustrate how SUCM works with a tree of alternative choices of apps from Google Play (music and audio, finance, sports, entertainment, games, and so on). In an effort to assess the effectiveness of the SUCM, experiments were performed with data derived from the marketplace-Google Play data-which contained several apps. Clearly, compared to the reputable currently available algorithms for ascertaining user-behavioral choices in the literature, the authors present more reliable results for distributing the apps to users based on their preferences. I encourage all statisticians and artificial intelligence advocates to read the exciting ideas in this paper. Online Computing Reviews Service

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Information & Contributors

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 11, Issue 2

May 2017

419 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3017677

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

Issue’s Table of Contents

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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Publication History

Published: 15 November 2016

Accepted: 01 August 2016

Revised: 01 May 2016

Received: 01 August 2015

Published in TKDD Volume 11, Issue 2

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Natural Science Foundation of China
National Natural Science Foundation of China
Department of Electrical and Computer Engineering
Fundamental Research Funds for the Central Universities
College of Engineering at Iowa State University
Rutgers 2015 Chancellor's Seed Grant Program
National Science and Engineering Research Council of Canada
National High Technology Research and Development Program of China

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https://doi.org/10.1109/TSC.2020.3037224
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