Recommendation System Based on User Searching...

Recommendation System Based on User Searching Query on Web

Author Name : Prof. Vipul Gamit, Mr. Manoj Kamber

ABSTRACT

Before a decade, the Audience used to give their review based on their experience orally or hand-written, to judge them was quite complex and time-consuming. In order to overcome that, one can give recommendations online and this recommendation system can judge preferences by latest search, rating and reviews. We see the use of recommendation systems all around us. However, one of the oldest and largest domains with a major need for recommender systems is, of course, e-commerce. The information overload problem in e-commerce sites is becoming increasingly serious. It is difficult for people to obtain their own needs from the massive item information quickly. One solution to this information overload problem is the use of recommender systems that use different mechanisms and algorithms for information filtering. Recommender systems are being used by an ever-increasing number of E- commerce sites to help consumers find products to purchase. In this paper, an explanation of how recommender systems help E-commerce sites increase sales, the types of recommender system and there limitations and advantages, the various method of collaborative filtering and the challenges of recommendation system such as Cold-start, Data sparsity, Scalability,Privacy, Long tail effect, unidentified users, Lack of ratings etc. have been discussed.

Keywords: Recommender Systems in NLP, E-Commerce, Collaborative filtering, content base, challenges of RS, feedback types