Program Recommendation System for Students or Coder through View Histories and Feedback Systems
Sangram A. Patil, Mahesh Bhosale, Rajesh M. Kamble · 2020
Now a day's most of the students/coders tries to get help of social media for most of their problems. So the recommendation system is also growing very fast for students/coders also. So these days social media also takes helps of recommendation system to catch the attention of every user. Some drawbacks are there in the existing recommendation systems and insufficient support to the current situations. These issues we try to by proposing the Hybrid FB (Feed Back) technique takes help of current MI system. Existing system does not provide accuracy and flexibility so to address this issue we proposed Hybrid FB technique. The methods used by existing recommendation systems are failed to achieve the accuracy and flexibility and proper recommendations. To overcome these problems recently MI method extends ROSE but it doesn't consider satisfaction of the end user which gives scope for improvement in accuracy as per end users requirement. Here we are presenting HFB (Hybrid FB) method in which we are improving the accuracy by taking relevant feedback over the time again and again and also maintains log of feedbacks based on end users. Our system uses this feedback because of which it can generate more precise recommendation next time for same query with less time for others.