

Flizon: Product Price Tracker and Shopping Companion
Abstract
In the rapidly evolving landscape of e-commerce, there is a growing demand for tools that simplify decision-making and enhance the overall shopping experience for consumers. This paper introduces a ground breaking online shopping application designed to address these needs through real-time pricing insights and intelligent assistance. The platform tracks product prices across various online marketplaces, instantly notifying users of price drops and fluctuations. Additionally, it incorporates a chatbot feature for personalized assistance, offering insights into price history trends, comparison of reviews, and validating product warranties. Users can also create curated carts for their favorite products, streamlining the shopping process. The project's primary objective is to revolutionize the online shopping experience by leveraging data-driven insights and cutting-edge technology. Through advanced algorithms, the platform empowers consumers to make informed purchasing decisions with confidence. Whether users are searching for the best deals, ensuring product authenticity, or seeking personalized recommendations, the platform offers a comprehensive solution to meet their diverse needs. Real-time conversations with the chatbot further enhance user confidence by addressing queries and concerns promptly. By combining simplicity, convenience, and advanced features, this platform aims to enhance the overall shopping experience, ultimately empowering consumers to navigate the complexities of online shopping with ease. Through continuous innovation and user-centric design, the platform sets a new standard for e-commerce, placing control and convenience firmly in the hands of consumers.
References
Damanpour, Faramarz, Jamshid Ali Damanpour. Ebusiness e-commerce evolution: perspective and strategy Managerial finance. 2001; 27(7):16-33.
Moe, Wendy W, Peter Fader S. "Dynamic conversion behavior at e-commerce sites." Management Science. 2004; 50(3):326335.
Lee, Matthew KO, Efraim Turban, “A trust model for consumer internet shopping”, International Journal of electronic commerce. 2001; 6(1):75-91.
Mahadevan, Balasubramaniam. "Business models for Internet-based e-commerce: An anatomy." California Management review. 2000; 42(4):55-69.
Miyazaki, Anthony D, Ana Fernandez. "Consumer perceptions and of privacy and security risks for online shopping." Journal of Consumer affairs. 2001; 35(1):27-44.
Chen, Sandy C, Gurpreet Dhillon S,”Interpreting dimensions of consumer trust in e-commerce.” Information Technology and Management. 2003; 4(2-3):303-318.
Zhu, Q., Cao, S., Ding, J., & Han, Z. “Research on the Price Forecast without Complete Data Based on Web Mining”. 10th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, 120-123 (2011).
Amaresh, V., Singh, R. R., Kamal, R., & Kulkarni, A. (2022). Linear Regression Models based Housing Price Forecasting. International Conference on Industry 4.0 Technology (I4Tech) (pp. 1-5) 2022
Dr. G Madhusudhan, Nitin Gopalakrishna Bhat, Sahana Venkatraman Patgar, Chandan N A, Bharath S V. "E-COMMERCE PRODUCT PRICE TRACKER.", JETIR June 2021, Volume 8, Issue 6, 2021.
Monburinon, N., P. Chertchom, T. Kaewkiriya, S. Rungpheung, S. Buya, and P. Boonpou. Prediction of prices for used car by using regression models, 5th International Conference on Business and Industrial Research (ICBIR), Bangkok, Thailand, 2018, pp. 115-119.
doi: 10.1109/ICBIR.2018.8391177.
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