Visible to the public Machine Learning Based Recommendation System

TitleMachine Learning Based Recommendation System
Publication TypeConference Paper
Year of Publication2020
AuthorsGanguli, Subhankar, Thakur, Sanjeev
Conference Name2020 10th International Conference on Cloud Computing, Data Science Engineering (Confluence)
KeywordsCalculators, Clustering algorithms, Collaboration, composability, cyber physical security, cyber physical systems, Entropy, Filtering, Motion pictures, pubcrawl, recommendation, recommender systems, resilience, Resiliency, similarity, Training, Trustworthy Systems, trustworthy users
AbstractRecommender system helps people in decision making by asking their preferences about various items and recommends other items that have not been rated yet and are similar to their taste. A traditional recommendation system aims at generating a set of recommendations based on inter-user similarity that will satisfy the target user. Positive preferences as well as negative preferences of the users are taken into account so as to find strongly related users. Weighted entropy is usedz as a similarity measure to determine the similar taste users. The target user is asked to fill in the ratings so as to identify the closely related users from the knowledge base and top N recommendations are produced accordingly. Results show a considerable amount of improvement in accuracy after using weighted entropy and opposite preferences as a similarity measure.
DOI10.1109/Confluence47617.2020.9058196
Citation Keyganguli_machine_2020