“An Improved Hybrid Filtering Approach for Recommender System”

Authors: Nitika Kadam, Shraddha Kumar

Published in: IJRRETAS, Volume 2, Issue 3, pp. 1-7 (01 Mar 2016)

Impact Factor: 2.3 (2016)

Certificate: View Certificate

Abstract

Recommender System is a subclass of information filtering system. It identifies similarity among users or items. It can be used as information filtering tool in online social network. Collaborative filtering recommendations are based on similarity of users or items, all data should be compared with each other in order to calculate this similarity. Due to large amount of data in dataset, too much time is required for this calculation, and in these systems, scalability problem is observed. It is better to group data, and each data should be compared with data in the same group. Content based filtering recommends items to users according to users history and items he liked in the past. To improve the working and quality of recommender system, a Hybrid approach by combining content based filtering and collaborative filtering, which includes Memory based (K-Nearest Neighbor) and Model based (clustering and rule based techniques),is presented in our proposed methodology. Using Hybrid approach, we get advantages from each other while drawbacks of both methods won’t take into account. Keywords: Recommender system, content based filtering, collaborative filtering, similarity function.

Introduction

Recommender system is a system which filters the informationfor recommending some items to its users; it filters the data and recommends the items. It is commonly used in movie lens, book-crossing, jester, wiki-lens that uses a collaborative filtering to present information on items and products that are likely to be of interest to the consumers. User’s interest in the past is seen and analyzed for the recommendation of any items. While presenting the recommendations, the recommender system uses details of the account of the registered user's profile, behavior, preferences and habit of their whole group of users and comparison of the information to present the recommendations. It much relies on similarity calculation. Types of Recommender System Recommendation System can be classified into 3 different categories based on technique used; they are content based method, collaborative method, and hybrid method.

Conclusion

Recommender systems are a powerful new technology for extracting additional value for a business from its user databases. These systems help users find items they want to buy from a business. Recommender systems benefit users by enabling them to find items they like. Conversely, they help the business by generating more sales. Recommender systems are rapidly becoming a crucial tool in E-commerce on the Web. Recommender systems are being stressed by the huge volume of user data in existing corporate databases, and will be stressed even more by the increasing volume of user data available on the Web. New technologies are needed that can dramatically improve the quality and scalability of recommender systems.