Abstract

When using traditional search engines, users have to formulate queries to describe their information need. This paper discusses a different approach to Web searching where the input to the search process is not a set of query terms, but instead is the URL of a page, and the output is a set of related Web pages. A related Web page is one that addresses the same topic as the original page. For example, www.washingtonpost.com is a page related to www.nytimes.com, since both are online newspapers.

We describe two algorithms to identify related Web pages. These algorithms use only the connectivity information in the Web (i.e., the links between pages) and not the content of pages or usage information. We have implemented both algorithms and measured their runtime performance. To evaluate the effectiveness of our algorithms, we performed a user study comparing our algorithms with Netscape's ‘What's Related' service (http://home.netscape.com/escapes/related/). Our study showed that the precision at 10 for our two algorithms are 73% better and 51% better than that of Netscape, despite the fact that Netscape uses both content and usage pattern information in addition to connectivity information.

Author Keywords: Search engines; Related pages; Searching paradigms

Article Outline

1. Introduction

2. Related page algorithms

2.1. Companion algorithm

2.1.1. Step 1: building the vicinity graph

2.1.2. Step 2: duplicate elimination

2.1.3. Step 3: assign edge weights

2.1.4. Step 4: compute hub and authority scores

2.2. Cocitation algorithm

2.3. Netscape's approach

3. Implementation

4. Evaluation

4.1. Experimental setup

4.2. User study results

4.3. Run-time performance

5. Related work

6. Conclusion

Acknowledgements

References

Vitae

¹This work was done while the author was at the Compaq Western Research Laboratory.

^*Corresponding author. Present address: mySimon, Inc., Santa Clara, CA, USA. E-mail: jdean@mysimon.com

²E-mail: monika@pa.dec.com