Welcome to the E-commerce Product Analysis and Recommendation System project! This system aims to enhance user engagement and streamline product discovery in the realm of online retail. By leveraging cutting-edge natural language processing (NLP) techniques, advanced clustering algorithms, and sophisticated topic modeling approaches, this project offers a comprehensive solution tailored to elevate the user experience within an e-commerce platform.
In the dynamic landscape of e-commerce, inefficient categorization and utilization of product descriptions often hinder effective decision-making. Users frequently encounter challenges in discovering relevant products and navigating extensive product catalogues. This project addresses these issues by clustering similar products, developing a recommender system, and extracting latent topics from product descriptions.
Cluster similar products: Utilize clustering algorithms to group similar products based on their descriptions. Develop a recommender system: Provide personalized product recommendations to users based on their interactions with a particular product. Extract latent topics: Employ topic modeling techniques to extract latent topics from product descriptions, enhancing categorization and search functionalities. Dataset The dataset comprises 500 stock keeping units (SKUs) from an outdoor apparel brand's product catalog, obtained from Kaggle.
The methodology involves comparing DBSCAN and K-Means for clustering similar products, employing Truncated SVD for extracting latent topics from TF-IDF matrices, generating word clouds to visualize frequent terms within clusters, and implementing a recommendation system to provide personalized product suggestions based on clustering results.
The code implementation encompasses data pre-processing, TF-IDF matrix creation, clustering using DBSCAN and K-Means, recommendation system development, and topic modeling visualization.
DBSCAN and K-Means clustering methods were evaluated based on silhouette scores. K-Means was chosen for its superior performance. The recommender system accurately provides personalized recommendations, while topic modeling effectively extracts meaningful topics from product descriptions.
DBSCAN vs. K-Means: Silhouette scores indicated K-Means' superior performance in grouping similar products. Recommendation System: The system accurately provides personalized recommendations leveraging clustering results. Topic Modeling: Meaningful topics were extracted from product descriptions, enriching understanding of product themes and attributes. Conclusion The E-commerce Product Analysis and Recommendation System offer a promising solution for enhancing user engagement and streamlining product discovery in online retail. Despite limitations, including data representativeness and scalability concerns, the system demonstrates effectiveness in grouping similar products, generating personalized recommendations, and extracting meaningful topics. Addressing these limitations and refining algorithms will be pivotal for maximizing utility and adaptability across diverse e-commerce platforms and product domains.