Recommendation System with Exploratory Data Analytics using Machine Learning

Panwar, Manu; Wadhwa, Amit; Pippal, Sanjeev Kumar

doi:10.1109/icac3n53548.2021.9725582

Cited by 2 publications

(2 citation statements)

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“…Techniques/Technologies Used [8] Probabilistic topic modeling, item and term similarity calculation [20][21][22] Unstructured reviews, structured semantic representation, semantic queries, additional ML analytics [28] AdaBoost algorithm [29] Clustering, decision trees, association rule mining [30] Difference Factor K-NN collaborative filtering (DF-KNN) [31] Agglomerative clustering algorithms [32] Support Vector Machines (SVM), decision trees, inductive logic programming [33] SVM, decision trees, inductive logic programming [34] Non-linear kernel techniques [35] Gibbs sampling based latent Dirichlet allocation classifier framework…”

Section: Studymentioning

confidence: 99%

“…They employ methods such as taxonomies, ontologies, machine learning, probabilistic topic modeling, and gradient-based learning algorithms to make accurate recommendations. The recent trend in the studies has shifted towards AI, specifically LSTM neural networks [34] and knowledge-based trees [35], to overcome the problem of context space. Additionally, conversational agents [36] have gained interest as they can offer both informative and entertaining interactions.…”

Section: Studymentioning

confidence: 99%

See 1 more Smart Citation

AI-Driven Recommendations: A Systematic Review of the State of the Art in E-Commerce

Necula

Păvăloaia

2023

Applied Sciences

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Electronic commerce has a strong connection with recommendation processes. There are various forms of recommendations, ranging from virtual assistants to online suggestions made in real time. Different algorithms and technologies are utilized for each form, and the choice of technique is dependent on the task at hand. For instance, artificial intelligence may utilize deep learning or machine learning techniques. The type of data also plays a role in determining the techniques used. Predictive modeling is applied to textual data, while image data requires image processing followed by AI algorithms for prediction. This study aimed to investigate the extent to which artificial intelligence is utilized in recommender systems for electronic commerce, as well as the current and future trends in the field. This was achieved through a systematic literature review of scientific articles from the past decade, using WosViewer for data collection and the Bibliometrix R package for analysis. The findings demonstrate that artificial intelligence works in conjunction with other technologies, such as blockchain, virtual reality, and augmented reality, to enhance the consumer experience throughout the e-commerce process.

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