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@article{171876,
        author = {Mayank Sharma and Mohit Singh Yadav and Pulkit Saraswat and Sahil Kandwal and Nischay Vats},
        title = {SHOPEASE E-COMMERCE APPLICATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {3555-3560},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171876},
        abstract = {The tourism industry significantly contributes to economic development and can greatly benefit from the utilization of recommendation systems. These computer-based tools aim to predict and suggest items of high interest to users from a vast pool, facilitating personalized choices aligned with their preferences and interests. By leveraging user and item attributes alongside specific algorithms, recommendation systems address the challenge of data overload on the World Wide Web. Their primary purpose is to offer users a curated selection of products or content, eliminating the need to sift through a massive number of web pages. However, the tourism industry currently lacks a platform that provides personalized information about tourist attractions. To bridge this gap, we propose a hybrid approach that combines content and collaborative filtering methods to develop a personalized travel recommendation system. This system takes into account user preferences, profiles, and past experiences to recommend the best attractions in a specific location. By analyzing the preferences and behaviors of users, as well as their appreciation of previously visited places, the system generates accurate and tailored recommendations. Our research focuses on building a robust recommendation system for the tourism industry, aiming to enhance the overall tourist experience. The system goes beyond simple location-based suggestions by considering individual preferences and interests. With the ability to recommend not only attractions but also local dining and shopping options, the system provides comprehensive support for travelers, making their trip planning process more efficient and enjoyable. In conclusion, our personalized travel recommendation system utilizes a hybrid approach, leveraging content and collaborative filtering techniques, to offer accurate and tailored suggestions to tourists. By providing personalized information about local attractions and facilitating choices aligned with user preferences, our system aims to enhance the tourist experience and contribute to the growth of the tourism industry. Keywords: Recommendation systems, Personalized travel recommendation system, Machine Learning, Cosine similarity, SVD algorithm Keywords: Recommendation systems, Personalized travel recommendation system, Machine Learning, Cosine similarity, SVD algorithm},
        keywords = {E-commerce, user-friendly interface, real-time inventory, personalized recommendations, secure payment, search algorithms, order tracking, product filters, customer reviews, multi-payment options, sales analytics, customer support, mobile shopping, secure transactions, vendor dashboard, digital wallet},
        month = {April},
        }