Leveraging User-Specified Metadata to Personalize Image Search

Leveraging User-Specified Metadata to Personalize Image Search

Kristina Lerman (USC Information Sciences Institute, USA) and Anon Plangprasopchok (USC Information Sciences Institute, USA)
DOI: 10.4018/978-1-60566-384-5.ch017
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The social media sites, such as Flickr and del.icio.us, allow users to upload content and annotate it with descriptive labels known as tags, join special-interest groups, and so forth. We believe user-generated metadata expresses user’s tastes and interests and can be used to personalize information to an individual user. Specifically, we describe a machine learning method that analyzes a corpus of tagged content to find hidden topics. We then these learned topics to select content that matches user’s interests. We empirically validated this approach on the social photo-sharing site Flickr, which allows users to annotate images with freely chosen tags and to search for images labeled with a certain tag. We use metadata associated with images tagged with an ambiguous query term to identify topics corresponding to different senses of the term, and then personalize results of image search by displaying to the user only those images that are of interest to her.
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Traditionally, personalization techniques fall in one of two categories: collaborative-filtering or profile-based. The first, collaborative filtering (Breese, 1998; Schafer, 2007), aggregates opinions of many users to recommend new items to like-minded users. In these systems, users are asked to rate items on a universal scale. The system then analyses ratings from many users to identify those sharing similar opinions about items and recommends new items that these users liked. Netflix uses collaborative filtering to recommend movies to its subscribers. Amazon uses a similar technology to display other products that users who purchased a given product were also interested in. Since users are asked to rate items on a universal scale, the questions of how to design the rating system and how to elicit high quality ratings from users are very important. Despite the early concern that users lack incentives for making recommendations and, therefore, will be reluctant to make the extra effort, there is new evidence (Schafer, 2007) that this does not appear to be the case. It appears that, at the very least, users find value in a collaborative rating system as an extension of their memory.

Key Terms in this Chapter

Social media: A term that defines activities by which users create and publish content on the Web. Examples include Flickr, del.icio.us, Digg and many others.

Tag: A freely-chosen keyword or term associated with content by the user

Metadata: ‘Data about data’

Personalization: Algorithms and techniques that tailor content to individual users

Image search: A type of Web search that returns images matching a given (text) query

Social Web: An umbrella term that includes social media and social networking sites, like Facebook and MySpace.

Machine Learning: A subfield of artificial intelligence that is concerned with algorithms and techniques for allowing computers to learn from data.

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