1. Using Local Information for Personalized Search Haward Jie CS 290C

2. Introduction Many search engines rely on the keywords entered by the user. Average user get the relevant documents by using one or two keywords as the query. The average number of keywords used was 1.5 in 1997 and 1.7 in 2005 [INAN06].

3. Google Statistics

4. Semantic Network Formalism Figure II.1. Semantic Network Formalism representation of q = peace in World War II filetype:pdf site:www.cnn.com OR site:www.bbc.co.uk with implicit meta words of lr:10 and ss:false

5. Graphical Model Figure II.2. Graphical representation of a user’s query and a set of tokens site, file type, language (lr), and safe search (ss).

6. Token Independencies

7. Conclusion Using local information, the approach can effectively capture the user’s interest to form a query expansion. For broader queries, the approach performs better than the commercial search engines, because it has additional information with which to filter out unrelated documents. In the case of ambiguous keywords that have multiple meanings, the approach eliminates confusion by instructing the search engine to search at the particular sites where the user has indicated an interest.

8. NOTE Partial of the slides have been removed because the author is the middle of publishing a related paper.

9. Questions?