Page 552 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Motivation
People who are constantly looking for the best deal on online products to buy are what drive the E-commerce product price
tracker. Online shoppers are mostly drawn to prices and discounts. The expansion of internet purchasing is also a result of
price. Additionally, this will contribute to the growth of small businesses, online retailers, etc.The development of a system that
uses the renowned dynamic pricing algorithm to automatically identify the convenient price of a chosen product is another
goal. The system will shorten the user's search time and alert them when prices drop. The main driving factors include need,
preference, cost, and many more factors. That helps customers, marketers, and product developers to advertise better
products, boost corporate profits, offer quality services, and easily fill orders with suppliers using the standard quality system.
Objectives
1. To give clients a platform to locate the best pricing for a product while saving them time.
2. Researching the internet buying habits of consumers, market upward and downward trends, etc.
3. To offer a new algorithm that is comparable to the dynamic pricing algorithm in terms of effectiveness.
4. To create a quality website utilising web scraping and improve alerting skills using emails.
2. RELATED WORK:
Numerous platforms and tools exist to assist consumers in online shopping, ranging from basic price comparison websites to
sophisticated recommendation systems. Platforms like Google Shopping and PriceGrabber aggregate prices from various
sellers, providing users with a consolidated view. However, these platforms often lack personalized features and fail to cater to
niche or emerging markets.
Research in recommendation systems has demonstrated the value of machine learning algorithms in understanding consumer
behavior and delivering tailored suggestions. Techniques such as collaborative filtering, content-based filtering, and hybrid
approaches have been successfully employed in domains like entertainment (e.g., Netflix) and retail (e.g., Amazon). However,
integrating such features into price comparison systems remains underexplored.
Recent studies emphasize the need for combining real-time data aggregation with advanced analytics to enhance user
experience. By incorporating dynamic filtering options and adaptive recommendation algorithms, systems can offer more
precise results tailored to individual preferences. The effectiveness of such integrations has been proven in sectors like
hospitality and travel but remains underutilized in e-commerce price comparison.
By combining the strengths of price comparison tools and advanced recommendation systems, the proposed platform seeks to
fill this gap. It leverages existing technologies while addressing their limitations, particularly in providing real-time data and
personalized recommendations for diverse consumer needs.
3. PROPOSED WORK:
The Product Price Comparison and Recommendation Engine comprises the following key components:

Figure 2: Illustration of Web-Based Price Tacker Process System

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 542