A cloud-based intelligent TV program recommendation system

Chang, Jui‐Hung; Lai, Chin-Feng; Wang, Ming-Shi; Wu, Tin-Yu

doi:10.1016/j.compeleceng.2013.04.025

Cited by 12 publications

(3 citation statements)

References 20 publications

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“…The TF-IDF algorithm reflects that the attribute word weight is closely related to its occurrence frequency and the number of comment items (Chang et al, 2013;Zhang et al, 2011).…”

Section: Attribute Feature Extractionmentioning

confidence: 99%

“…Duong et al (2017) developed a novel collaborative filtering approach based on fuzzy neural network to perform the video recommendation concerning the users' behaviors. Chang et al (2013) proposed an intelligent recommendation system for digital TV program using cloud computing technique. To make the personalized recommendation for forum users, a novel learning-to-rank decision-making framework combining content-based and collaborative features was introduced and applied in real industrial applications (Bach et al, 2016).…”

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confidence: 99%

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What attracts vehicle consumers’ buying

Zhou

Lim

et al. 2019

IMDS

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Purpose The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

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“…The TF-IDF algorithm reflects that the attribute word weight is closely related to its occurrence frequency and the number of comment items (Chang et al, 2013;Zhang et al, 2011).…”

Section: Attribute Feature Extractionmentioning

confidence: 99%

mentioning

confidence: 99%

What attracts vehicle consumers’ buying

Zhou

Lim

et al. 2019

IMDS

View full text Add to dashboard Cite

show abstract

“…Moreover, most people watch TV programs on a daily basis. They now have a great many more TV programs to choose [2]. Hence, selecting the best program project to keep the audiences staying on plays an important role for TV stations.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Program Projects Selection Model for Nonprofit TV Stations

Chang

2015

Mathematical Problems in Engineering

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This study develops a hybrid multiple criteria decision making (MCDM) model to select program projects for nonprofit TV stations on the basis of managers’ perceptions. By the concept of balanced scorecard (BSC) and corporate social responsibility (CSR), we collect criteria for selecting the best program project. Fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Next, considering the interdependence among the selection criteria, analytic network process (ANP) is then used to obtain the weights of them. To avoid calculation and additional pairwise comparisons of ANP, technique for order preference by similarity to ideal solution (TOPSIS) is used to rank the alternatives. A case study is presented to demonstrate the applicability of the proposed model.

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