  The social media site Flickr allows users to upload their photos, annotate
them with tags, submit them to groups, and also to form social networks by
adding other users as contacts. Flickr offers multiple ways of browsing or
searching it. One option is tag search, which returns all images tagged with a
specific keyword. If the keyword is ambiguous, e.g., ``beetle'' could mean an
insect or a car, tag search results will include many images that are not
relevant to the sense the user had in mind when executing the query. We claim
that users express their photography interests through the metadata they add in
the form of contacts and image annotations. We show how to exploit this
metadata to personalize search results for the user, thereby improving search
performance. First, we show that we can significantly improve search precision
by filtering tag search results by user's contacts or a larger social network
that includes those contact's contacts. Secondly, we describe a probabilistic
model that takes advantage of tag information to discover latent topics
contained in the search results. The users' interests can similarly be
described by the tags they used for annotating their images. The latent topics
found by the model are then used to personalize search results by finding
images on topics that are of interest to the user.
