APP Relationship Calculation: An Iterative Process

Today, plenty of apps are released to enable users to make the best use of their cell phones. Facing the large amount of apps, app retrieval and app recommendation become important, since users can easily use them to acquire their desired apps. To obtain high-quality retrieval and recommending resul...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2015-08, Vol.27 (8), p.2049-2063
Main Authors: Liu, Ming, Wu, Chong, Zhao, Xiang-Nan, Lin, Chin-Yew, Wang, Xiao-Long
Format: Article
Language:English
Subjects:
Cell phones
Context
Dictionaries
Excavation
Google
Judgments
Marine vehicles
Mathematical analysis
Recommender systems
Relations among complexity measures
Retrieval
Similarity
Similarity measures
Smart phones
Text processing
Thesauri
Transaction processing
Vectors
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Summary:Today, plenty of apps are released to enable users to make the best use of their cell phones. Facing the large amount of apps, app retrieval and app recommendation become important, since users can easily use them to acquire their desired apps. To obtain high-quality retrieval and recommending results, it needs to obtain the precise app relationship calculating results. Unfortunately, the recent methods are conducted mostly relying on user's log or app's description, which can only detect whether two apps are downloaded, installed meanwhile or provide similar functions or not. In fact, apps contain many general relationships other than similarity, such as one app needs another app as its tool. These relationships cannot be dug via user's log or app's description. Reviews contain user's viewpoint and judgment to apps, thus they can be used to calculate relationship between apps. To use reviews, this paper proposes an iterative process by combining review similarity and app relationship together. Experimental results demonstrate that via this iterative process, relationship between apps can be calculated exactly. Furthermore, this process is improved in two aspects. One is to obtain excellent results even with weak initialization. The other is to apply matrix product to reduce running time.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2015.2405557