DYNAMIC WEB SERVICE SUGGESTION MODEL LEVERAGING USER PREFERENCES AND COLLABORATIVE LEARNING

Abstract

In order to narrow down an extensive range of possible system objects to the ones that users desire, recommender systems employ a number of data mining techniques and algorithms. In contrast to a stripped-down paradigm where users may simply search for and purchase products, recommender systems entice users by providing a more comprehensive experience. By considering a user's past searches, purchases, and actions, among other things, recommender systems can provide more personalized choices. The algorithm takes into account the user's historical data as well as the data of other users to make predictions about the user's preferences, and then it suggests products that match those tastes. This recommender system study primarily deals with precision, scalability, setup time, and data insufficiency. What the user does determines the suggestions made by content-based filtering. Comparable to Collaborative Filtering, long-term models display consumer decision profiles. A significant improvement in efficiency can be achieved by modifying the precedence profile.