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@article{210613,
        author = {Krushna Peherkar and Tejas Mahajan and Prathmesh Tarte and Prof.S.R.Yendole},
        title = {E-Commerce Portal with Intelligent Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {6},
        pages = {1496-1504},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=210613},
        abstract = {Online retailers must help shoppers find relevant products among very large catalogues while keeping the storefront fast, secure and easy to administer. This paper presents the design and implementation of an E-Commerce Portal with an Intelligent Recommendation System built on the MERN stack (MongoDB, Express.js, React.js and Node.js). The portal provides user registration and login using password hashing and JSON Web Tokens (JWT), a searchable and filterable product catalogue, cart and checkout, inventory control, order management and a role-based administration panel. An interaction-tracking module records views, clicks, ratings, cart additions and purchases in MongoDB, and a recommendation service applies collaborative filtering with relevance scoring and ranking to produce personalised product suggestions, while popular and category-based fallbacks serve new users. A Redis caching layer with time-to-live (TTL) policies and cache invalidation reduces database workload and response time. The system was tested with Postman across authentication, shopping, caching and recommendation scenarios and prepared for deployment on hosted services. The work demonstrates a scalable, modular approach for combining core commerce functions with data-driven personalisation},
        keywords = {E-commerce, recommendation system, collaborative filtering, MERN stack, Redis caching, JWT authentication},
        month = {November},
        }