International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024
p-ISSN: 2395-0072
www.irjet.net
Optimizing Product Recommendations with Location-Based Data and Demographic Profiling Using CatBoost Kirubahari.R1,Sakthi Aashitha.N.S2,Sakthi Jothi.T.S3 1Associate Professor, Computer Science &Engineering, K.L.N. College of Engineering, Sivagangai,TamilNadu,India. 2,3 Student, Computer Science &Engineering, K.L.N. College of Engineering ,Sivagangai,Tamil Nadu,India,
---------------------------------------------------------------------***--------------------------------------------------------------------Additionally, location-based data adjusts Abstract - Delivering highly personalized and contextually
recommendations according to the user's geographic context, making them more relevant to their environment. The CatBoost algorithm enhances accuracy by efficiently handling complex and categorical data, ensuring that recommendations evolve in real-time to meet user preferences and market trends. Moreover, the system offers flexibility to businesses by allowing continuous customization of recommendation models based on specific product categories or seasonal trends. This comprehensive approach empowers e-commerce platforms to offer more relevant, personalized suggestions, thereby boosting user engagement and business success.
relevant product recommendations is essential for improving user satisfaction and business performance in today's competitive e-commerce environment. This work introduces an advanced product recommendation system that integrates content-based and collaborative filtering with location-based data, enhanced by the CatBoost gradient boosting algorithm. The system tailors recommendations based on individual user profiles, past interactions, and geographic context, ensuring alignment with user interests and preferences. By applying collaborative filtering, the system leverages the preferences of similar users to offer diverse and relevant product suggestions. Location-based data allows for recommendations that adapt to a user's geographic surroundings, offering location-specific products and services. CatBoost's gradient boosting capabilities improve recommendation accuracy by processing complex and categorical data, dynamically adjusting suggestions based on user feedback. This hybrid approach ensures that recommendations evolve with user preferences and contextual factors in real-time. The outcome is a personalized product recommendation pathway that significantly enhances user engagement and boosts business outcomes. This innovative system provides e-commerce platforms with a powerful tool for delivering highly targeted and effective product suggestions.
2. RETALED WORKS In their 2021 study, Mustafa et al. [1] introduced "OntoCommerce," a hybrid recommender system aimed at overcoming significant challenges in e-commerce, particularly the cold-start and data sparsity issues. The system integrates ontology to systematically represent knowledge about customers and products, allowing for a better understanding of customer preferences and improving the calculation of user similarities. This is paired with sequential pattern mining (SPM), which analyzes user behavior over time to detect trends in interactions. By applying SPM to the outputs of collaborative filtering, OntoCommerce generates more personalized recommendations that are relevant to users even when historical data is limited. Experimental results demonstrated that this hybrid approach significantly outperformed traditional recommendation methods, providing accurate and tailored suggestions that enhance user experience in e-commerce platforms. Overall, OntoCommerce exemplifies how the combination of advanced techniques can facilitate better product discovery for customers, leading to increased satisfaction and engagement.
Keywords: Personalized Recommendations, Location Adaptation, Content-Based Filtering, Collaborative Filtering, CatBoost, Gradient Boosting.
1.INTRODUCTION In today's competitive e-commerce landscape, personalized and context-aware product recommendations are essential for improving user satisfaction and business performance. This system integrates content-based filtering, collaborative filtering, location-based data, and the CatBoost algorithm to deliver highly tailored product suggestions. Content-based filtering analyzes individual user profiles and past interactions, ensuring that recommendations align with specific preferences, while collaborative filtering leverages insights from similar users to diversify suggestions.
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Jingyi Ding et al. [2] (2021) introduced a sales forecasting system in their paper titled "Sales Forecasting Based on CatBoost," which utilizes the CatBoost algorithm to enhance the accuracy of sales predictions in the retail sector. The authors trained their system using the
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