A large-scale analysis of query logs for assessing personalization opportunities

Abstract

Query logs, the patterns of activity left by millions of users, contain a wealth of information that can be mined to aid personalization. We perform a large-scale study of Yahoo! search engine logs, tracking 1.35 million browser-cookies over a period of 6 months. We define metrics to address questions such as 1) How much history is available?, 2) How do users' topical interests vary, as reflected by their queries?, and 3) What can we learn from user clicks? We find that there is significantly more expected history for the user of a randomly picked query than for a randomly picked user. We show that users exhibit consistent topical interests that vary between users. We also see that user clicks indicate a variety of special interests. Our findings shed light on user activity and can inform future personalization efforts.

Personalized news recommendation based on click behavior

“…Wedig and Madani [21] performed a large-scale analysis of Yahoo! search engine query logs to determine the consistency of user interests and answer other questions related to personalization.…”

Large scale query log analysis of re-finding

“…Wedig and Madani [25] found that topics for a user are consistent over time and different from one another, and that some users repeat clicks over long time periods. Teevan et al [21] showed that re-finding and repeat queries were very prevalent, and explored how queries used to refind changed and how well future clicks could be predicted following repeat queries.…”

Mining various semantic relationships from unstructured user-generated web data

“…The number of submitted queries and clicked URLs occurring in search logs are usually in power-law distribution [28,29,30]. Only a few queries or URLs have high submitted or clicked numbers while many queries and URLs have low numbers.…”