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

Abstract

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