Distributed Focused Web Crawling for Context Aware Recommender System using Machine Learning and Text Mining Algorithms

In today’s world, Recommender System (RS) is the most effective means used to manage huge amount of multimedia content available on internet. RS learns user preferences and relationships among users items. It helps discover new interesting items make use different media types such as text, audio, video images. can act an information filtering model which overcome issues related over-fitting excess information. this work, a distributed framework named DAE-SR (Deep AutoEncoder based Softmax Regression) introduced for context-aware recommender systems focus user-item interaction offers personalized recommendations. The proposed implemented in PYTHON platform. dataset experimentation Foursquare dataset. performance beneficial both service providers. Its decision making process offer relevant recommendations users. evaluated terms various metrics accuracy, recall, precision so on. From implementation outcomes, strategy achieved good accuracy (98.33%), (98%), run time (1.43 ms) recall (98.1%). Thus, it proved that classifier performs better compared other models dependable