Product recommendation using enhanced convolutional neural network for e-commerce platform

Latha, Yarasu Madhavi; Rao, B. Srinivasa

doi:10.1007/s10586-023-04053-3

Cited by 6 publications

(1 citation statement)

References 42 publications

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“…According to their experiments, they outperformed other popular models, achieving an accuracy of 96.2 %. In another recent paper, Latha and Rao [78] proposed an enhanced CNN model to analyze the customers' sentiments on the Amazon product review database.…”

Section: Deep Neural Networkmentioning

confidence: 99%

Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable Marketing

Stalidis,

Karaveli,

Diamantaras

et al. 2023

Sustainability

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In recent years, the interest in recommendation systems (RSs) has dramatically increased, as they have become main components of all online stores. The aims of an RS can be multifaceted, related not only to the increase in sales or the convenience of the customer, but may include the promotion of alternative environmentally friendly products or to strengthen policies and campaigns. In addition to accurate suggestions, important aspects of contemporary RSs are therefore to align with the particular marketing goals of the e-shop and with the stances of the targeted audience, ensuring user acceptance, satisfaction, high impact, and achieving sustained usage by customers. The current review focuses on RS related to retail shopping, highlighting recent research efforts towards enhanced e-shops and more efficient sustainable digital marketing and personalized promotion. The reported research was categorized by main approach, key methods, and specialized e-commerce problems addressed, while technological aspects were linked with marketing aspects. The increasing number of papers in the field showed that it has become particularly popular, following the explosive growth in e-commerce and mobile shopping. The problems addressed have expanded beyond the performance of the core algorithms to the business aspects of recommendation, considering user acceptance and impact maximization techniques. Technologies have also shifted from the improvement of classic filtering techniques to complex deep learning architectures, in order to deal with issues such as contextualization, sequence-based methods, and automatic feature extraction from unstructured data. The upcoming goals seem to be even more intelligent recommendations that more precisely adapt not only to users’ explicit needs and hidden desires but also to their personality and sensitivity for more sustainable choices.

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Section: Deep Neural Networkmentioning

confidence: 99%