Personalisation in web computing and informatics: Theories, techniques, applications, and future research

Gao, Min; Liu, Kecheng; Wu, Zhaocong

doi:10.1007/s10796-009-9199-3

Cited by 117 publications

(69 citation statements)

References 98 publications

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“…This taxonomy is the same used by Del Aguila and Padilla (2008), Chong et al (2009) and Gao, Liu and Wu (2010) CA_PCL2 was renamed as CA_PSK1, CA_PCL3 as CA_PSK2, and CA_PFO1 as CA_PSK3, CA_PFO2 as CA_PSK4, and CA_PFO3 as CA_PSK5.…”

Section: Exploratory Factor Analysis and Scale Cleaningmentioning

confidence: 99%

Strategic Antecedents and Consequents for the Performance of E-Business Companies

Souza¹,

Batista²

2017

BBR

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This article aims to examine the influence of the strategic background and of the business model elements on its value proposition, and to investigate the relationship between the value proposition and the performance of e-business. The research is supported theoretically by the Technological, Organizational and Environmental framework (TOE), derived from the Diffusion of Innovation Theory (DIT), for the Strategic Background (IFINEDO, 2011), by the Business Model theory (OSTERWALDER; PIGNEUR; 2010; SOUZA; BATISTA, 2014) and by the business performance literature (RAMANUJAN; VENKATRAMAN, 1987). We tested the proposed model with a sample of 252 observations, using the structural equation modeling. The main results indicate a partial influence of the strategic background in the value proposition through the constructs management positioning, organizational readiness, relative advantage and pressure from stakeholders. Relating to the business model, the constructs of market segmentation, customer relationships, capabilities, partnerships and revenue model influence the value proposition, which in turn affects positively the performance of e-business companies.

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Section: Exploratory Factor Analysis and Scale Cleaningmentioning

confidence: 99%

Strategic Antecedents and Consequents for the Performance of E-Business Companies

Souza¹,

Batista²

2017

BBR

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“…Yu, Pan, & Li, 2011). Many efforts have been made previously that provided well organized and detailed surveys about personalisation in the web, and user profiling (Anand & Mobasher, 2005;Gao, Liu, & Wu, 2010;Gauch, Speretta, Chandramouli, & Micarelli, 2007). In this survey, we will focus on the latest trends in user modelling research related to social media and we will provide a view for future research in this area.…”

Section: User Profiling In Social Mediamentioning

confidence: 99%

A Survey of User Modelling in Social Media Websites

Abdel-Hafez¹,

Xu²

2013

CIS

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With the widespread of social media websites in the internet, and the huge number of users participating and generating infinite number of contents in these websites, the need for personalisation increases dramatically to become a necessity. One of the major issues in personalisation is building users' profiles, which depend on many elements; such as the used data, the application domain they aim to serve, the representation method and the construction methodology. Recently, this area of research has been a focus for many researchers, and hence, the proposed methods are increasing very quickly. This survey aims to discuss the available user modelling techniques for social media websites, and to highlight the weakness and strength of these methods and to provide a vision for future work in user modelling in social media websites.

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“…Other significant limitations are the high computation cost that goes linear with the increase number of users and items and the sparsity of the dataset. Similarity indexing, dimensionality reduction and offline clustering have been proposed to remedy these weaknesses (Gao et al, 2009).…”

Section: Collaborativementioning

confidence: 99%

“…Based on the customer preferences, It helps to find the products they would like to purchase by providing recommendations and is particularly useful in ecommerce sites that offer millions of products for sale (Kim et al, 2005). According to (Gao et al, 2009) there are four filtering approaches for making recommendations, namely, rule-based filtering, content-based filtering, collaborative filtering and hybrid filtering.…”

Section: Introductionmentioning

confidence: 99%

IPACT: Improved Web Page Recommendation System Using Profile Aggregation Based On Clustering of Transactions

Almurtadha¹,

Sulaiman²,

Mustapha³

et al. 2011

American Journal of Applied Sciences

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Problem statement: Recently, Web usage mining techniques have been widely used to build recommendation systems especially for anonymous users. Approach: Assigning the current user to the best web navigation profile with similar navigation activities will improve the ability of the prediction engine to produce a recommendation list then introduce it to the user. This study presents iPACT an improved recommendation system using Profile Aggregation based on Clustering of Transactions (PACT). Results: iPACT shows better prediction accuracy than the previous methods PACT and Hypergraph. Conclusion: The users' interests change over time; hence an incremental and adaptive web navigation profiling is a key feature for the future works.

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Personalisation in web computing and informatics: Theories, techniques, applications, and future research

Cited by 117 publications

References 98 publications

Strategic Antecedents and Consequents for the Performance of E-Business Companies

Strategic Antecedents and Consequents for the Performance of E-Business Companies

A Survey of User Modelling in Social Media Websites

IPACT: Improved Web Page Recommendation System Using Profile Aggregation Based On Clustering of Transactions

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