Customising WAP-based information services on mobile networks

Lee, Wei‐Po; Lu, Cheng-Che

doi:10.1007/s00779-003-0247-6

Cited by 9 publications

(8 citation statements)

References 15 publications

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“…Accuracy ranks high as well with 49 occurrences, mainly because of its intuitive nature when evaluating ML algorithms. Movie 31 (Banerjee et al, 2012;Bar et al, 2013;Biancalana et al, 2011;Bjelica, 2010;Braida et al, 2015;Das et al, 2013;Degemmis et al, 2007;Devi & Venkatesh, 2013;Gedikli et al, 2011;Halder et al, 2014;Hofmann, 2003Hofmann, , 2004Jun, 2005;Karahodza & Donko, 2015;W.-P. Lee & Lu, 2003;Liang et al, 2014;Q. Liu et al, 2014;Marović et al, 2011;McLaughlin & Herlocker, 2004;Nie et al, 2013;Pessiot et al, 2007;Roh et al, 2003;Takács et al, 2008;Takáes et al, 2009;Wang et al, 2012;Yeh & Wu, 2010;Zahra et al, 2015;S.-Z.…”

Section: Performance Metricsmentioning

confidence: 99%

“…Zhao & Pan, 2015) Academic 12 (Das et al, 2013;Hernández del Olmo et al, 2009;Huang & Nikulin, 2014;Krohn-Grimberghe et al, 2011;Luong, Huynh, Gauch, Do, & Hoang, 2012;Middleton et al, 2004;Montañés et al, 2009;Oyama et al, 2012;Pecli et al, 2015;Vialardi et al, 2011;Xin et al, 2014) News 11 (Bellogín et al, 2011;Brouard & Pomot, 2016;Buabin, 2012;Das et al, 2013;Z. Fan et al, 2016; W.-P. Lee & Lu, 2003;Leopairote et al, 2013;Q.-C. Li et al, 2008;Nguyen et al, 2016;Nie et al, 2013;Nicol et al, 2014) E-commerce 10 ( Anaissi & Goyal, 2015;Bauer & Nanopoulos, 2014;Buettner, 2016;Castro-Herrera et al, 2009;R. Zhang & Tran, 2010;Nie et al, 2013;Pecli et al, 2015;Wei et al, 2011;Q.…”

Section: Performance Metricsunclassified

“…Fan & Chang, 2010;Ghazarian & Nematbakhsh, 2015;Hofmann, 2004;Jung & Lee, 2004;Karahodza & Donko, 2015;Q. Liu et al, 2014;McLaughlin & Herlocker, 2004;Moreno et al, 2012;Takáes et al, 2009;Wang et al, 2012;Wei et al, 2011;Yan et al, 2013;Zahra et al, 2015 Elmongui et al, 2015;Kao & Fahn, 2013;Tsapatsoulis et al, 2015) Accuracy 49 26 (Alemeye & Getahun, 2015;Banerjee et al, 2012;Bellogín et al, 2011;Bjelica, 2010;Cai et al, 2012;Castro-Herrera et al, 2009;Halder et al, 2014;Hussain et al, 2015;Jin et al, 2005;Kong et al, 2013;Krzywicki et al, 2015; W.-P. Lee & Lu, 2003; T.-J. Lee & Tseng, 2012;Q.-C. Li et al, 2008;T.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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The use of machine learning algorithms in recommender systems: A systematic review

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Section: Performance Metricsmentioning

confidence: 99%

Section: Performance Metricsunclassified

Section: Accepted Manuscriptmentioning

confidence: 99%

See 1 more Smart Citation

The use of machine learning algorithms in recommender systems: A systematic review

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“…Data selfadaptation is a complex work in the wireless environment. Lee and Lu discussed how to customize the WAP-based information on mobile networks in [13]. In our previous work [9], we also have studied the data self-adaptation in the wireless environment.…”

Section: Data Self-adaptive Strategiesmentioning

confidence: 99%

The Design and Implementation of Multiterminal Based Proactive Information Delivery System

Xia

Yuan

Zhang

2013

Mathematical Problems in Engineering

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Currently, the development of various communication terminal devices has greatly promoted people’s daily life, while information using efficiency with these devices decreases rapidly due to the information overload. To solve this problem, a multiterminal based proactive information delivery system (MPIDS) is designed and implemented in this paper. Firstly, users’ interests are computed comprehensively from the historical data, taking full consideration of users’ behaviors when visiting web pages. Secondly, a proactive information monitor service is introduced to monitor users’ data requirements and their interest changes, with which, user data are pushed to their online device automatically according to the strategies. Finally, a data self-adapter is given to encode and transform the data according to users’ online parameters and a series of data self-adaptive strategies. The experimental results show that MPIDS provides richly featured, secure, and robust personalized functions, reduces the running cost, and promotes the end-user experience and business efficiency.

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“…However, recommendation made by the authors in paper [3], was based on user context, and ontology knowledge was used. In the Wap field, the existing problems and deficiencies while visiting Wap site has been noticed by authors in paper [4], and optimized the data transferred though wireless environment, but has not combined with Web usage.…”

Section: Introductionmentioning

confidence: 99%

Study on Wap Self-Adapt Based on Web Usage Mining

Xia

Yuan

Zhang

et al. 2008

2008 ISECS International Colloquium on Computing, Communication, Control, and Management

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Smart terminal equipments，such as mobile phone, PDA etc., develop quickly at present. At the same time, there is huge information with various formats in the society, such as image, text, table ect. How to obtain information with Wap (Wireless Application Protocol) is an emergency problem nowadays. In allusion to this issue, a study on Wap self-adapt based on web usage logs is proposed in the paper. Under the background of the application of data mining on the web, the user interest algorithm and the Wap page rebuild algorithm with retrieval and mining of user's usage logs data are put forward., user personalize data sharing in the Wap pages is implemented combining with the relevant user content interest algorithm.

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Customising WAP-based information services on mobile networks

Cited by 9 publications

References 15 publications

The use of machine learning algorithms in recommender systems: A systematic review

The use of machine learning algorithms in recommender systems: A systematic review

The Design and Implementation of Multiterminal Based Proactive Information Delivery System

Study on Wap Self-Adapt Based on Web Usage Mining

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