Improved Product Ranking For Recommendation Systems
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 2
Abstract
Abstract: In the new area of information technology, the most important and recognition machine learning technique is Recommender engine. Vast numbers of knowledge discovery techniques are applied by recommender system to the problem of making personalized recommendation for product, information and services during a live interaction [5]. The users which have no sufficient ability to evaluate the, potentially overwhelming, numbers of alternatives, at that time a recommender system is very helpful to those users.Recommender systems provide a personalized and items which are included in ranked list by predicting what the most suitable items are, based on the user’s history, preferences and constraints [2]. RS are k –nearest item basedcollaborative filtering approach. They are obtaining on web which is intelligent. Vast amount of information is available on the web and many visitors which are visit the website that possess the challenges for recommender systems in recent years. The challenges are: They generate high quality recommendations, accomplish lots of recommendations per second for millions of users and items and achieving high data sparsity [5]. The Recommendation Engine Provides the functionality to Ecommerce portal. Where the items are added to shopping cart and similar items are recommended to the user based on their purchase patterns. Recommendation Engine will analyze huge Data sets of User purchase pattern and recommend items to online users based on Machine Learning Algorithms.
Authors and Affiliations
Tanha Joshi
Keywords
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