The Challenges (and Joys!) of Personalizing Web Search

The personalization of search results has long been seen as an important strategic direction for search engines. However, it was not until the past few years where personalized search was possible at scale. In this talk, I will talk about the algorithms that made web-scale personalized search possible. I will discuss some special characteristics of the structure of the web (and the eigenstructure of the web matrix), and describe the computational techniques used to exploit this structure to compute of PageRank much faster than by standard methods. The resulting speed allowed the computation of an individualized ranking for every user on the web, resulting in a dramatic improvement in search relevance.

Sep Kamvar is a consulting professor of Computational and Mathematical Engineering at Stanford University. His research focuses on personal and social models for search.

From 2003 to 2007, Sep was the head of personalization at Google. Prior to Google, he was founder and CEO of Kaltix, a personalized search company that was acquired by Google in 2003.

Sep is the author of two books and over 40 technical publications and patents in the fields of search and social computing. He is on the technical advisory boards of several companies, including Aardvark, Etsy, and Hunch. His artwork has been exhibited at the Museum of Modern Art in New York, the Victoria and Albert Musem in London, and the National Museum of Contemporary Art in Athens.