e-SimNet: a visual similar product recommender system for  E-commerce

Addagarla, Ssvr Kumar; Amalanathan, Anthoniraj

doi:10.11591/ijeecs.v22.i1.pp563-570

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…from images or semantics from natural text. A representative application of CNN is the research proposed by Addagarla and Amalanathan [77] to perform top-N recommendations in e-shopping platforms based on the visual similarity of products. They trained a CNN model in order to extract image features, and then generated image embeddings and built an index tree using the approximate nearest neighbors oh yeah (ANNOY) algorithm.…”

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%

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

Stalidis,

Karaveli,

Diamantaras

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…The system finally generated a pleasant experience for both customers and marketing staff. Likewise, a system for online product suggestion to buyers [23] was developed, using visual feature learning techniques. A 96% accuracy rate was obtained, being evidence that the use of these approaches and technologies are better.…”

Section: Related Literaturementioning

confidence: 99%

Digital platform based on geomarketing as an improvement in micro and small enterprises

Crisóstomo-Berrocal¹,

Sierra-Liñan

Cabanillas-Carbonell

2022

IJEECS

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After the situation generated by the pandemic caused by COVID-19, micro and small enterprises (MSEs) faced a complex reality, having to cope with business uncertainty. This research proposes a digital platform based on geomarketing as a growth and support strategy for MSEs, with the objective of improving their labor and capital productivity, through the incorporation of the technological factor, which will have a great impact on them, helping them to continue operating and not having to close their businesses. The platform was developed under the agile Scrum methodology because it is adaptable to the constant changes in the mobile application development process, having as indicators labor productivity and capital productivity. Finally, the results revealed that labor productivity increased by 30.86 percent, meaning that, for every hour worked per person, more sales were made. As for capital productivity, it decreased by 1.47 percent, meaning that investment decreased for each value added of each product sold.

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“…computer vision, natural language processing, and also provides a new method for recommendation systems. With the powerful characterization ability of DL technology, we learn the hidden vector representation of users and items, mine the historical behavior data of users, the diverse data of products, and the contextual scene information, capture the potential preferences of users, and generate a more accurate personalized recommendation list for users [6][7].…”

Section: Introductionmentioning

confidence: 99%

Deep learning techniques in e-commerce recommender systems and their impact on business marketing strategies

Tang,

Zhong,

Liu

2024

Proceedings of the 5th International Conference on E-Commerce and Internet Technology, ECIT 2024, March 15–17, 2024, Changsha,

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Recommender systems could mitigate the problem of "information overload", understand the additional value of data, provide the specific information to costumer, and make information fully used. The integration of the characterization capability of deep learning (DL) with the recommendation system assists to deeply explore custumer requirements and offer details and specific recommendation services. The formulation of enterprise marketing strategy is a systematic problem that integrates various factors. DL technology can help enterprises take in opinions from their customers, optimize their marketing strategies, and improve their marketing results. This paper first introduce the merits and dismerits of conventional recommendation systems, and then review the latest achievement of DL system. At the same time, DL technology is applied to the formulation of enterprise marketing strategy. Finally, it analyzes and mention the future trend direction of intelligent recommendation systems.

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e-SimNet: a visual similar product recommender system for E-commerce

Cited by 11 publications

References 21 publications

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

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

Digital platform based on geomarketing as an improvement in micro and small enterprises

Deep learning techniques in e-commerce recommender systems and their impact on business marketing strategies

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