  Personalization despite being an effective solution to the problem
information overload remains tricky on account of multiple dimensions to
consider. Furthermore, the challenge of avoiding overdoing personalization
involves estimation of a user's preferences in relation to different queries.
This work is an attempt to make inferences about when personalization would be
beneficial by relating observable user behavior to his/her social network usage
patterns and user-generated content. User behavior on a search system is
observed by means of team-draft interleaving whereby results from two retrieval
functions are presented in an interleaved manner, and user clicks are utilised
to infer preference for a certain retrieval function. This improves upon
earlier work which had limited usefulness due to reliance on user survey
results; our findings may aid real-time personalization in search systems by
detecting a user-related and query-related personalization signals.
