Conference Paper

A Collaborative Filtering Algorithm with Phased Forecast.

DOI: 10.1007/978-3-642-02962-2_88 Conference: Rough Sets and Knowledge Technology, 4th International Conference, RSKT 2009, Gold Coast, Australia, July 14-16, 2009. Proceedings
Source: DBLP

ABSTRACT Collaborative filtering (CF) algorithms predict interests of an active user in order to deal with the overload of information.
Usually, changes of her interests have been ignored in traditional algorithms, which take user’s interest as static data and
product rating in different phase with same weight. So when users’ interests have changed as time goes on, unneeded items
may be recommended. In order to solve above problem, we propose a new item-based collaborative filtering algorithm in this
paper. In this algorithm, named PFCF, we firstly divide users’ rating history into several periods, then users’ interests
distributing in these periods are analyzed by a phrased forecast method, which is used to find user’s different type interests.
The proposed algorithm is strictly tested on the MovieLens data set. The experimental results show its good precision against
other traditional item-based collaborative filtering algorithms.

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    ABSTRACT: Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.
    IEEE Internet Computing 02/2003; · 2.00 Impact Factor