Browsing by Author "Muhammad Saeed Ahmad"

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    (UMT, Lhr, 2023-06-20) Muhammad Junaid Ashraf; Muhammad Saeed Ahmad; Muhammad Farrukh Sabir; Muhammad Ahsan
    The most obvious commercial use of the World Wide Web is the business-to-consumer part of electronic commerce (e-commerce). Selling products and services online is the main objective of an e-commerce website. A web-based shopping system is being developed for an existing store. This project aims to give clients of physical stores the benefits of online shopping. Using a website makes it easier to purchase goods from a store wherever you are via the internet. The paper begins by introducing the concept of recommendation systems and their pivotal role in e-commerce platforms. It elaborates on the two main types of recommendation systems - collaborative filtering and content-based filtering - explaining their underlying principles and approaches to generating recommendations. Furthermore, hybrid recommendation systems, which combine both approaches, are discussed, emphasizing their effectiveness in capturing diverse user preferences.Subsequently, the paper delves into the significance of personalized recommendations. It elucidates the methodologies employed for capturing user data, such as browsing history, purchase behavior, and social interactions, which are used to create user profiles. This process enables the recommendation systems to deliver highly tailored suggestions, thereby enhancing user engagement and fostering brand loyalty.Moreover, the challenges associated with recommendation systems in e-commerce are outlined. Privacy concerns, data security, and potential biases in the recommendation algorithms are addressed, shedding light on the importance of ethical and transparent practices. Additionally, the issue of the "filter bubble" is explored, which refers to the potential limitation of users' exposure to diverse content due to personalized recommendations.Lastly, the paper concludes by emphasizing the future prospects of recommendation systems in e-commerce. As technology continues to evolve, novel methods, such as reinforcement learning and context-aware recommendations, are anticipated to redefine the landscape of personalized shopping experiences.In conclusion, recommendation systems have transformed the e-commerce industry, enabling businesses to offer personalized shopping experiences to customers while driving business growth. This paper serves as a valuable resource for researchers, e-commerce professionals, and stakeholders interested in understanding the intricacies and potential of recommendation systems in the context of e-commerce.MongoDB is used as database while NodeJS and Express as back-end language, and a web browser serving as the front-end client making up the client-server implementation. JavaScript and React JS is being used in this project for front end development.