E-commerce Recommendation System Using Improved Probabilistic Model

Gaikwad, Rahul; Udmale, Sandeep S.; Sambhe, Vijay K.

doi:10.1007/978-981-10-3920-1_28

Cited by 16 publications

(2 citation statements)

References 13 publications

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“…Also, they used the Amazon dataset to show the more accurate performance of the proposed method than the customary slope one algorithm. Gaikwad et al [80] proposed a model-based CF recommender system depending on the eventual model with the utilization of developed Naive Bayes algorithm. They used query time, search query, and click time as the aspects of the model of the Naive Bayes algorithm.…”

Section: Reviewing Selected Collaborative Filtering Mechanismsmentioning

confidence: 99%

A Systematic Study on the Recommender Systems in the E-Commerce

et al. 2020

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Electronic commerce or e-commerce includes the service and good exchange through electronic support like the Internet. It plays a crucial role in today's business and users' experience. Also, e-commerce platforms produce a vast amount of information. So, Recommender Systems (RSs) are a solution to overcome the information overload problem. They provide personalized recommendations to improve user satisfaction. The present article illustrates a comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of the e-commerce recommender systems. We reviewed the selected papers to identify the gaps and significant issues of the RSs' traditional methods, which guide the researchers to do future work. So, we provided the traditional techniques, challenges, and open issues concerning traditional methods of the field of review based on the selected papers. This review includes five categories of the RSs' algorithms, including Content-Based Filtering (CBF), Collaborative Filtering (CF), Demographic-Based Filtering (DBF), hybrid filtering, and Knowledge-Based Filtering (KBF). Also, the salient points of each selected paper are briefly reported. The publication time of the selected papers ranged from 2008 to 2019. Also, we provided a comparison table of important issues of the selected papers as well as the tables of advantages and disadvantages. Moreover, we provided a comparative table of metrics and review issues for the selected papers. And finally, the conclusions can, to a great extent, provide valuable guidelines for future studies.

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Section: Reviewing Selected Collaborative Filtering Mechanismsmentioning

confidence: 99%

A Systematic Study on the Recommender Systems in the E-Commerce

et al. 2020

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“…Naïve Bayes adalah algoritma yang banyak digunakan dalam pengembangan bidang RS. Algoritma ini bekerja berdasarkan fitur-fitur item [14]. Dalam beberapa tahun terakhir, RS berdasarkan konten biasanya digunakan untuk merekomendasikan dokumen, halaman web, publikasi, lelucon, atau berita [13], seperti Syskill & Webert, yang merekomendasikan halaman web [15] atau PTV [16], yang merekomendasikan program TV.…”

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SPARRING: Sistem Rekomendasi Peneliti Terintegrasi Google Scholar via SerpAPI dan Latent Dirichlet Allocation pada Konteks Perguruan Tinggi

Ma'ady,

Rizaldy,

Satria

et al. 2023

JTMI

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The researcher partner recommendation system plays a crucial role in fostering academic collaboration in universities, where a challenge for new users is finding suitable research partners. In addressing the limitations of Naïve Bayes classifiers, this article introduces an innovative approach in the form of a non-linear sigmoid activation function. We highlight the urgency of this solution, detail its implementation steps, and describe its substantial contribution to research partner recommendations. This article not only identifies existing obstacles but also proposes revolutionary solutions to enhance the effectiveness of consultation systems in academic environments. A gap in this research is the manual input method for data retrieval, creating weaknesses, susceptibility to human errors, and reduced efficiency in collecting journal data. We propose SPARRING, a researcher recommendation system connected to Google Scholar, in the context of higher education. This approach uses a dataset of faculty members from the Faculty of Information Technology and Business at a private university in Indonesia. The results from Google Scholar extraction, with topic keywords determined by Latent Dirichlet Allocation, are then classified using the Naïve Bayes algorithm. Additionally, we integrate web scraping tools, particularly SerpAPI, to access data from Google Scholar. Through the integration of SerpAPI, the proposed web-based system is capable of providing more accurate recommendations, especially for new users with limited collaboration experience. By incorporating SerpAPI, the proposed web-based system can offer more accurate recommendations, particularly for new users without extensive collaboration experience.

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DF E-Commerce: A Deep Learning Integrated Ontology Driven Model for E-Commerce Product Recommendation for Improved Machine Intelligence

Vijayakumar

Deepak

2022

Digital Technologies and Applications

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E-commerce Recommendation System Using Improved Probabilistic Model

Cited by 16 publications

References 13 publications

A Systematic Study on the Recommender Systems in the E-Commerce

A Systematic Study on the Recommender Systems in the E-Commerce

SPARRING: Sistem Rekomendasi Peneliti Terintegrasi Google Scholar via SerpAPI dan Latent Dirichlet Allocation pada Konteks Perguruan Tinggi

DF E-Commerce: A Deep Learning Integrated Ontology Driven Model for E-Commerce Product Recommendation for Improved Machine Intelligence

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