Pattern Matching Based Metric for Recommending Ordered Items
- Resource Type
- Conference
- Authors
- Rahman, M Tareq; Stephi, Zanifer Afsana; Rahman, Moqsadur
- Source
- 2021 3rd International Conference on Electrical & Electronic Engineering (ICEEE) Electrical & Electronic Engineering (ICEEE), 2021 3rd International Conference on. :113-116 Dec, 2021
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Measurement
Machine learning algorithms
Correlation
Machine learning
Predictive models
Prediction algorithms
Pattern matching
Rating Prediction
Machine Learning
Deviation
Longest Common Subsequence
System’s Actual Accuracy
- Language
At present, we all know that the Recommendation System is essential. Our lives are continuously impacted by the Recommendation System. Its main objective is to suggest a relevant item or item list as per the user’s requirement. In many cases, it recommends the user’s desired item or item list based on rating prediction, and this prediction accuracy is considered to be the system’s actual accuracy. But can the rating prediction accuracy be considered the system’s true accuracy in ordered items prediction? Rating prediction system even after predicting a near-exact rating, there could be a difference between the actual item list and the predicted item list. We attempted to find answers to these issues by working with the College Recommendation System. We have used different machine learning-based models in our work for rating prediction. And we have measured the correlation between the actual item list and the predicted item list using the Longest Common Subsequence algorithm. Our analysis showed that the rating prediction accuracy does not always reflect the system’s actual accuracy in the scenario of ordered items prediction. The accuracy of the system should be verified by how closely the predicted item list matches the actual item list when recommending ordered items. A pattern-matching algorithm like - Longest Common Subsequence can be considered as an accuracy metric in this context.