Open AccessResearch on Cosine Similarity and Pearson Correlation Based Recommendation Models
Author(s) -
Suja Cherukullapurath Mana,
T. Sasipraba
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1770/1/012014
Subject(s) - similarity (geometry) , collaborative filtering , cosine similarity , pearson product moment correlation coefficient , recommender system , computer science , field (mathematics) , product (mathematics) , artificial intelligence , information retrieval , data mining , machine learning , pattern recognition (psychology) , mathematics , statistics , geometry , pure mathematics , image (mathematics)
Recommendation models are based on data mining and machine learning techniques to suggest product and services to users. Advancement in the machine learning field results in the growth of more efficient recommendation models. Recommendation systems can contribute a lot to increase the business of internet based companies. Techniques for making recommendations generally fall into two categories namely collaborative filtering based models and content based recommendation models. The first techniques explores similarity based approaches like cosine similarity, Pearson correlation similarity etc. The second approaches uses predictions based on explicit and implicit ratings given by users. Recommendation systems try to suggest products to the users based on their previous purchases or general nature. A successful recommendation system is capable of improving the product sales to a large extend. This paper reviews about various recommendation models and performs a comparison of these models.