A Scalable Collaborative Filtering Based

Today it is almost impossible to retrieve information with a keyword search when the information is spread over several pages. The Semantic Web is an extension of the current web in which information is given well-defined meaning. Web personalization is the one application of semantic web usage mining. In this report we have explored comparison of various collaborative filtering techniques. Those techniques are memory based, model based and hybrid collaborative filtering. Our study shows that the performance of hybrid collaborative filtering technique is better than memory based and model based collaborative filtering technique. We have introduced normalization step, which will improve accuracy of traditional collaborative filtering techniques.

International Journal for Research in Applied Science and Engineering Technology, 2018

The way in which the growth in the available digital information and the number of users on the Internet have created a challenge of data overload causes timely access to item of interest on the Internet. Many of the information retrieval systems try to solve this problem but prioritization of item and user were absent in most of the systems. The purpose of this paper is to develop a recommender system using collaborative filtering technique and Fuzzy C-mean (FCM). Collaborative filtering is the most successful algorithm in the recommender system's field. Recommender systems are the systems for filtering the information that handle with the problem of overloaded information by filtering important information fragment out of huge amount of dynamically generated information according to user's interest, preferences or observed behaviour about them.. This paper considers m users, n number of items and present an approach based on FCM clustering to produce a recommendation for the active user by a new approach. Here FCM clustering algorithm to categorize users based on their interests.

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 Memorybased 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.

International Journal of Knowledge-based and Intelligent Engineering Systems, 2019

The heightening in the available information in the form of digital data and the number of users on the Internet have engendered a challenge of overburden of data which obstructs access to interested item on the Internet timely. There are many information retrieval systems which try to solve the problem of information overloading but in their cases prioritization and personalization of information were absent. The main aim is to develop a recommender system using item based collaborative filtering technique and K-means. The most popular algorithm in the recommender system's field is the collaborative filtering technique. Recommender systems are the filtering systems for information that concerned with the problem of information overburden by filtering essential information fragment out of enormous dynamically promoted information according to person's attentiveness, taste and distinguished behavior about them. We are considering m users, n items (in numbers) and presenting a model to fabricate a recommendation for the mobile user by a new approach.

Proceedings of The 6th International Conference on Information Technology for Education (IT@EDU2010), 2010

Collaborative filtering (CF) is popular algorithm for recommender systems. Therefore items which are recommended to users are determined by surveying their communities. CF has good perspective because it can cast off limitation of recommendation by discovering more potential items hidden under communities. Such items are likely to be suitable to users and they should be recommended to users. There are two main approaches for CF: memory-based and model-based. Memory-based algorithm loads entire database into system memory and make prediction for recommendation based on such in-line memory database. It is simple but encounters the problem of huge data. Model-based algorithm tries to compress huge database into a model and performs recommendation task by applying reference mechanism into this model. Model-based CF can response user's request instantly. This paper surveys common techniques for implementing model-based algorithms. We also give a new idea for model-based approach so as to gain high accuracy and solve the problem of sparse matrix by applying evidence-based inference techniques.

Abstract— Recommender System predicts user preferences for the purpose of suggesting items to purchase or examine and Recommender systems using collaborative filtering are a popular technique for reducing information overload. Recommendation techniques that are having many classes but this paper focused on some filtering approaches like Content Based Filtering, Hybrid Recommendation and Collaborative Filtering approaches. Analysis, review of Recommender System, highlighting the architectural principles, key concepts, and state-of-the-art of implementation as well as the research challenges are the high lights of this paper. The important research directions and to provide better understanding of the design challenges of Collaborative Filtering are also focused in this paper.

Collaborative filtering based recommender systems help online users in choosing the right products based on the purchase history of the user and his most similar users. Scalability is one of the major issues in designing effective recommender system. In this paper, we have studied different ways of increasing scalability by applying clustering algorithms on three types collaborative filtering algorithms-user based, item based and slope one. Finally we have analyzed the relationship between scalability and accuracy for different number of clusters and neighborhoods.

Advances in Artificial Intelligence, 2009

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

2006