Personalizing Image Search Results on Flickr

TL;DR

This paper presents a method to personalize image search results on Flickr by leveraging user metadata such as contacts and tags, significantly improving search relevance through filtering and probabilistic topic modeling.

Contribution

It introduces a novel approach that combines social network filtering with probabilistic topic models to enhance personalized image search relevance.

Findings

Filtering by contacts improves search precision.

Latent topic discovery aligns results with user interests.

Personalization reduces irrelevant search results.

Abstract

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.