Pre-processing of user behaviour for e-commerce

Asha, K. N.; Rajkumar, R.

doi:10.1109/smarttechcon.2017.8358464

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The data needed for the recommender system is both structured and unstructured. And, the timeline should also be taken into consideration while making recommendations [13]. In some categories, old items should not be recommended and in some cases, old items may be considered.…”

Section: Pre-processing the Datamentioning

confidence: 99%

Data Science and Deep Learning Applications in the E-Commerce Industry: A Survey

Pradeep¹,

M²,

Satyanarayana³

2020

INDJCSE

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The E-Commerce industry is a new way of shopping. Most manufacturers prefer to sell their products on E-commerce websites because of low maintenance and high returns. This paper highlights the application of data science and deep learning methodologies to drive the E-commerce business. The first part of the paper explains the role of data science to identify potential customers, items recommendations and fraud detections. Different stages of building E-commerce websites and the role of proper handling of data is then highlighted. The second part of the paper is about the use of deep learning approaches like Recurrent neural networks in analysing textual information is presented with a demonstration of three architectures. At last, the importance of convolutional neural networks in the E-commerce industry is presented. Fashion related images like dresses, shoes and bags are classified using two separate convolution neural network architectures.

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Section: Pre-processing the Datamentioning

confidence: 99%

Data Science and Deep Learning Applications in the E-Commerce Industry: A Survey

Pradeep¹,

M²,

Satyanarayana³

2020

INDJCSE

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“…The mainstream research on entrepreneurship and information retrieval (IR) so far predominantly focuses on the creation and development of technical systems and interfaces, such as an efficient Web portal for entrepreneurs by analysing the search mechanism or interactive IR systems for entrepreneurs' e-commerce store [1][2]. While there is limited research addressing the entrepreneurs' IR process or activities, Lea et al [3] highlighted a need for further analysis of the personalised IR system for entrepreneurs.…”

Section: Introductionmentioning

confidence: 99%

Entrepreneurs’ Information Retrieval: The Role of Affective Aspects through the Media Richness Theory

Orrensalo

Nikou

2021

2021 IEEE International Conference on Technology and Entrepreneurship (ICTE)

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A narrative review of the literature on the importance of affective factors in the information retrieval (IR) behaviours of entrepreneurs is presented in this paper. Through the lens of Media Richness Theory, we examine the importance of the richness of information and medium to the success of the IR process. The results show that IR system does not only serve entrepreneurs in regard to their information needs but also their emotional needs. The richness of the IR system, referring to the accessibility, is an essential factor for entrepreneurs' preference and use of the IR medium. This paper contributes to literature by showing how affective attributes are associated with other factors, in addition to their effect on the IR behaviours. The findings reveal that the affective characteristics are both an individual need and a determining component in the process.

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Pre-processing of user behaviour for e-commerce

Cited by 2 publications

References 4 publications

Data Science and Deep Learning Applications in the E-Commerce Industry: A Survey

Data Science and Deep Learning Applications in the E-Commerce Industry: A Survey

Entrepreneurs’ Information Retrieval: The Role of Affective Aspects through the Media Richness Theory

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