International Journal of Computer Science & Communication Vol. 1, No. 2, July-December 2010, pp. 429-432 Improvement of Personalized Recommendation Algorithm based on Hybrid Collaborative Filtering Boddu Raja Sarath Kumar 1 , Barre John Ratnam 2 & Maddali Surendra Prasad Babu 3 1 CSE Department, Lenora College of Engineering, Rampachodavaram-533288, Andhra Pradesh, India 2 Lenora College of Engineering, Rampachodavaram-533288, Andhra Pradesh, India 3 CS&SE Department, Andhra University College of Engineering, Visakhapatnam, India Email: iamsarathphd@gmail.com ABSTRACT The explosive growth and availability of data on the internet has caused information overload. Searching for a query is not easy in the sources of information available for the interest of an individual user. Collaborative filtering systems recommend items based upon opinions of people with similar tastes. Collaborative filtering overcomes some difficulties faced by traditional information filtering by eliminating the need for computers to understand the content of the items. Further, collaborative filtering can also recommend articles that are not similar in content to items rated in the past as long as like-minded users have rated the items. Collaborative filtering (CF) is one of the most frequently used techniques in personalized recommendation systems. But currently used CF techniques are based on item rating prediction. We proposed an improved personalized recommended CF algorithm. Hybrid recommender systems or content-boosted technologies are quickly produce high quality recommendations. We have explored content-boosted CF technique which analyzes the user-item matrix to identify relationships between different items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze different Memory - based CF and Model-based CF techniques. Finally, we experimentally evaluate our results and compare them. The testing results show that in most cases, the improved algorithm that we put forward can improve recommendation quality. Keywords: Collaborative Filtering, Personalized Recommendation Algorithm, Content-boosted Filtering 1. INTRODUCTION Recent years have seen the explosive growth in the amount of information available through internet. www has created the world as global village. Collaborative filtering is a general approach to personalized information filtering and automates the process of recommending items to a user based upon the opinions of people with similar tastes. In most cases, the filtering system determines which users have similar tastes by using standard formulae for computing statistical correlations. Many collaborative filtering techniques use a form of weighted average to determine a prediction for a user. These techniques use the correlation as the weights. Every user receives a prediction for all items and submits a rating of how well she likes an item after reading it. This feedback given by her is used along with similar feedback from other users to calculate a rating prediction. CF systems work by collecting user feedback in the form of ratings for items in a given domain and exploit similarities and differences among profiles of several users in determining how to recommend an item. On the other hand, content-based methods provide recommendations by comparing representations of content contained in an item to representations of content that interests the user. Content-based methods can uniquely characterize each user, but CF still has some key advantages. In this paper, we present the framework for this new hybrid approach, Content-Boosted Collaborative Filtering or Hybrid Collaborative Filtering (HCF). We apply this framework in the domain of movie recommendation and show that our approach performs better than both pure CF and pure content-based systems. In traditional collaborative filtering algorithms include user-based collaborative filtering algorithm and collaborative filtering algorithm based on item rating prediction. User-based collaborative filtering algorithm produces recommendation list for object user according to the view of other users. It is based on these assumptions: if the ratings of some items rated by some users are similar, the rating of other items rated by these users will also be similar. Collaborative filtering recommendation system uses statistical techniques to search the nearest neighbors of the object user and then basing on the item rating rated by the nearest neighbors to predict the item rating rated by the object user, and then produce corresponding recommendation list.