Electronic Commerce Product Recommendation using Enhanced Conjoint Analysis

Osmond, Andrew Brian; Hidayat, Fadhil; Supangkat, Suhono Harso

doi:10.14569/ijacsa.2021.0121176

Cited by 3 publications

(3 citation statements)

References 21 publications

(26 reference statements)

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“…The main processes of preprocessing include removing irrelevant words, handling duplicates to ensure data integrity, normalizing data to a consistent format, handling outliers to prevent skewed analysis, and performing feature extraction to reveal underlying patterns in the dataset [20] [21] [22]. By performing these preprocessing steps, researchers create a cleaner, more standardized dataset that becomes the basis for accurate and in-depth data analysis.…”

Section: B Processing Data 1) Preprocessingmentioning

confidence: 99%

Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning

Abdul Fadlil,

Riadi,

Andrianto

2024

IJCDS

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The rapid growth of the online market, particularly in the digital realm, has spurred the need for in-depth studies regarding marketing strategies through public opinion, especially on platforms like Twitter. The sentiments expressed in customer tweets hold significant insights into their satisfaction or dissatisfaction levels with a service. Therefore, the use of ML algorithms in sentiment analysis is imperative to detect whether such comments lean towards positivity or negativity regarding a service. This research focuses on sentiment analysis towards three major e-commerce platforms in Indonesia: Tokopedia, Shopee, and Lazada, through the utilization of Twitter. The classification process involves various stages, including preprocessing, feature extraction and selection, data splitting for classification, and evaluation. The selection of both linear and non-linear SVM models as the focus of this research is based on their ability to handle large and complex datasets. The linear kernel is chosen for its proficiency in cases with a linear relationship between features and class labels, while the non-linear SVM provides flexibility in dealing with complex and non-linear relationships. Based on the evaluation results of the SVM model on the dataset, it is found that the polynomial kernel provides the highest accuracy value of 93%, with a training data share of 85%. This model features strong prediction capabilities with a precision of 93% for negative and 93% for positive labels. Although the linear kernel and other kernels showed solid performance, the polynomial kernel provided the most optimal results in the context of online marketplace sentiment analysis using data from Twitter

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Section: B Processing Data 1) Preprocessingmentioning

confidence: 99%

Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning

Abdul Fadlil,

Riadi,

Andrianto

2024

IJCDS

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“…This preprocessing process involves cleaning and preparing the data for further analysis. This includes removing special characters, addressing missing or duplicate data, and converting the tweet text into a format that can be used by modelling algorithms such as SVM [13]- [14]. Preprocessing can also involve text normalization and removal of irrelevant words.…”

Section: B Data Preprocessingmentioning

confidence: 99%

Non-linear Kernel Optimisation of Support Vector Machine Algorithm for Online Marketplace Sentiment Analysis

Fadlil,

Riadi,

Andrianto

2024

JUITA

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Twitter is a social media platform that is very important in the digital world. Fast communication and interaction make Twitter a vital information center in sentiment analysis. The purpose of this research is to classify public opinion about the presence of marketplaces in Indonesia, both positive and negative sentiments, using a Non-linear SVM algorithm based on 1276 tweets. This research involves the stages of data pre-processing, labeling, feature extraction using TF-IDF, and data division into three scenarios: 80% training data and 20% test data, 50% training data and 50% test data scenario, and 20% training data and 80% test data scenario. The last process, GridSearchCV, combines cross-validation and non-linear SVM parameters for model evaluation using a confusion matrix. The best SVM model resulting from the scenario was 80% training and 20% test data, with hyperparameters Gamma = 100 and C = 0.01, achieving 89% accuracy. When tested on never-before-seen data, the accuracy increased to 90%, with an f1-score of 91%, precision of 88%, and recall of 95% on negative sentiments. In conclusion, evaluating the performance of non-linear SVM kernels with a combination of hyperparameter values can improve accuracy, especially on public response information about online marketplaces and public sentiment.

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“…The authors suggested in [19] an approach of collecting and pre-processing real-time data from multiple e-commerce platforms, which generate user data, and gathering all personalized user data to prepare for the next data mining, and then using the data mining technology in big data to automatically recommend personalized products to satisfy the personalized needs and tastes of customers. In [20], in order to identify customer preferences, the authors proposed an improved conjoint analysis method. To evaluate their method, they compared it with other prediction algorithms namely: generalized linear model, decision tree, random forest, gradient boosted trees, and support vector machine.…”

Section: Related Workmentioning

confidence: 99%

Machine learning based recommender system for e-commerce

Loukili

Messaoudi

Ghazi

2023

IJ-AI

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<span>Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.</span>

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Electronic Commerce Product Recommendation using Enhanced Conjoint Analysis

Cited by 3 publications

References 21 publications

Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning

Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning

Non-linear Kernel Optimisation of Support Vector Machine Algorithm for Online Marketplace Sentiment Analysis

Machine learning based recommender system for e-commerce

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