<?xml version="1.0" standalone="yes"?>
<Paper uid="W03-1103">
  <Title>An Approach for Combining Content-based and Collaborative Filters</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
2 Related work
</SectionTitle>
    <Paragraph position="0"> Proposed approaches to hybrid system, which combines content-based and collaborative filters together, can be categorized into two groups.</Paragraph>
    <Paragraph position="1"> One group is the linear combination of results of collaborative and content-based filtering, such as systems that are described by Claypool (1999) and Wasfi (1999). ProfBuilder (Wasfi, 1999) recommends web pages using both content-based and collaborative filters, and each creates a recommendation list without combining them to make a combined prediction. Claypool (1999) describes a hybrid approach for an online newspaper domain, combining the two predictions using an adaptive weighted average: as the number of users accessing an item increases, the weight of the collaborative component tends to increase. But how to decide the weights of collaborative and content-based components is unclearly given by the author. The other group is the sequential combination of content-based filtering and collaborative filtering. In this system, firstly, content-based filtering algorithm is applied to find users, who share similar interests. Secondly, collaborative algorithm is applied to make predictions, such as RAAP (Delgado et al., 1998) and Fab filtering systems (Balabanovic and Shoham, 1990). RAAP is a content-based collaborative information filtering for helping the user to classify domain specific information found in the WWW, and also recommends these URLs to other users with similar interests. To decide the similar interests of users is using scalable Pearson correlation algorithm based on web page category. Fab system, which uses content-based techniques instead of user ratings to create profiles of users. So the quality of predictions is fully depended on the content-based techniques, inaccurate profiles result in inaccurate correlations with other users and thus make poor predictions.</Paragraph>
    <Paragraph position="2"> As for collaborative recommendation, there are two ways to calculate the similarity for clique recommendation - item-based and user-based. Sarwar (Sarwar et al, 2001) has proved that item-based collaborative filtering is better than user-based collaborative filtering at precision and computation complexity.</Paragraph>
    <Paragraph position="3"> Figure1. Overview of the our approach</Paragraph>
  </Section>
class="xml-element"></Paper>