Intention-Based Online Consumer Classification for Recommendation and Personalization

Shi, Fanjuan; Ghédira, Chirine

doi:10.1109/hotweb.2016.15

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Exact outcomes recommend that the proposed demonstrate can characterize aims correctly. [7] Arranging customized visit agendas is a perplexing and testing errand for the two people and PCs. Doing it physically is tedious; moving toward it as an advancement issue is computationally NP hard.…”

Section: Literature Surveymentioning

confidence: 99%

Travel Package Recommendation System based on Package Locations and Rating

Dipali¹,

Pragati²,

Pratik³

et al. 2017

IJCA

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Throughout the decades, travelling has encountered constant development and extending expansion to wind up noticeably one of the quickest developing monetary segments on the planet. Among the current travelling applications, just a modest bunch encourage the capacity to design a visit which is totally in light of client inclinations, while offering a top to bottom take a gander at the coveted goal. Hence, this examination concentrates on coordinating semantic innovations, cooperative sifting into the area of travelling and give client arranged visit designs with superlative client fulfilment. Visit arranging and the method for investigating wanted courses, real stops or attractions en route by means of virtual reality 360 view understanding. Moreover, business associations can utilize the electronic dashboard to keep up their administrations, offers, bundles, spending report and acquire business diagnostic based changes.

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Section: Literature Surveymentioning

confidence: 99%

Travel Package Recommendation System based on Package Locations and Rating

Dipali¹,

Pragati²,

Pratik³

et al. 2017

IJCA

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“…They developed a probabilistic generative model to identify search patterns and validated that model on the dataset released by Alibaba. Shi and Ghedira (2016) proposed a prediction model in order to predict customer's online shopping intention. They used unsupervised (clustering) and supervised learning techniques (classification).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Shopping intention prediction using decision trees

Šebalj¹,

Franjković²,

Hodak³

2017

mill

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Introdução: O preço é um elemento negligenciado na literatura em marketing devido à complexidade da sua gestão e sensibilidade dos clientes sobre as mudanças de preços. Consequentemente, o processo de tomada de decisões de compra pode ser muito desafiador para o cliente. Objetivo: Criar um modelo para prever a intenção de compra e segmentar os clientes em uma das duas categorias, dependendo da intenção de comprar ou não. Métodos: A amostra é composta por 305 entrevistados, pessoas com idade superior a 18 anos envolvidas na compra de mantimentos para sua casa. A pesquisa foi realizada em fevereiro de 2017. Para criar um modelo, o método decision trees foi usado com os seus vários algoritmos de classificação. Resultados: Todos os modelos, exceto onde foi usado o algoritmo RandomTree, alcançaram uma taxa de classificação relativamente alta (acima dos 80%). A classificação com maior precisão foi de 84,75% com algoritmos J48 e RandomForest. Como não há diferença estatisticamente significativa entre esses dois algoritmos, os autores decidiram escolher o algoritmo J48 e criar uma árvore de decisão. Conclusões: O valor monetário e o nível de preços na loja foram as variáveis mais significativas para a classificação da intenção de compra. No futuro pretende-se comparar esse modelo com algumas outras técnicas de data mining, como redes neurais ou support vector machines, uma vez que essas técnicas alcançaram uma precisão elevada em estudos anteriores neste campo. Palavras-chaves: Modelo; Compra, Árvore de decisão. ABSTRACT Introduction:The price is considered to be neglected marketing mix element due to the complexity of price management and sensitivity of customers on price changes. It pulls the fastest customer reactions to that change. Accordingly, the process of making shopping decisions can be very challenging for customer. Objective: The aim of this paper is to create a model that is able to predict shopping intention and classify respondents into one of the two categories, depending on whether they intend to shop or not. Methods: Data sample consists of 305 respondents, who are persons older than 18 years involved in buying groceries for their household. The research was conducted in February 2017. In order to create a model, the decision trees method was used with its several classification algorithms. Results: All models, except the one that used RandomTree algorithm, achieved relatively high classification rate (over the 80%). The highest classification accuracy of 84.75% gave J48 and RandomForest algorithms. Since there is no statistically significant difference between those two algorithms, authors decided to choose J48 algorithm and build a decision tree. Conclusions: The value for money and price level in the store were the most significant variables for classification of shopping intention. Future study plans to compare this model with some other data mining techniques, such as neural networks or support vector machines since these techniques achieved very good accuracy in some previous research in this fie...

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