International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 02 Issue: 07 | Oct-2015 www.irjet.net p-ISSN: 2395-0072 © 2015, IRJET ISO 9001:2008 Certified Journal Page 1279 HYBRID BASED RECOMMENDATION ENGINE: The Art of Matching Items to User Divya Vasal 1 , Preksha Shukla 2 , Vaibhav Vyas 3 1 Student, AIM & ACT, Banasthali Vidyapeeth, Rajasthan, India 2 Student, AIM & ACT, Banasthali Vidyapeeth, Rajasthan, India 3 Asst. Professor, AIM & ACT, Banasthali Vidyapeeth, Rajasthan, India ----------------------------------------------------------------------------------------------------------------------------------------- Abstract: The paper focuses on the goals of the recommendation engine which is to generate meaningful recommendations to a collection of users for items or products that might interest them. Recommendation engine have changed the way people find products, information and even other users. It is a study of behavioral patterns for different users to recommend them different items from a collection of things. The design of such recommendation engine depends on the domain and the data available. The traditional approaches are:- 1. Content-based filtering-Recommend items similar to the items the user has preferred in the past. 2. Collaborative filtering- Recommend items the users with similar tastes preferred in the past. To overcome the limitations of the above mentioned approaches the recent evolving approach is the hybridization of both. A hybrid approach has the potential of delivering enhanced results by exploring the best of both the conventional approaches. Recommendation engine have become trivial tool that helps the developers to generate an algorithms for the prediction of items which a user may prefer among the other list of given items. In recent years, they have been applied in a variety of applications like Pandora, NETFLIX, YouTube, FACEBOOK, LinkedIn etc. The proposed Hybrid Approach provides improvements in addressing two major challenges of recommendation engine: accuracy of the engine and sparsity of data by simultaneously incorporating correlation between items and users. The evaluation of the system shows superiority of the solution compared to stand-alone collaborative and content based approaches. This can be considered very important, especially when dealing with a large quantity and diversity of resources. Key Words: Recommendation engine ,Content-Based Filtering, Collaborative Filtering, and Hybrid Approach. 1. INTRODUCTION With the overloading of information and increase in the number of variety of products, a new need in technology emerged -RECOMMENDATION ENGINE. Recommendation engine is a subclass of information filtering system that seek to predict the ‘rating’ or ‘ preference’ that user would give to an item. They provide personalized suggestions to the user. Manyon- line stores provide recommending services; example- AMAZON, IMDB, NETFLIX, YouTube, FACEBOOK, LinkedIn and so forth. Three basic steps for a conventional recommendation engine [4]:- An input is provided by the user to the engine. These inputs can be stated clearly or implied. The ratings given by the user for a particular item are readily observable whereas the various websites that the user visits that is the click-through rate are inherited for the future recommendations.