Exploiting User Comments for Audio-visual Content Indexing and Retrieval

State-of-the-art content sharing platforms often require users to assign tags to pieces of media in order to make them easily retrievable. Since this task is sometimes perceived as tedious or boring, annotations can be sparse. Commenting on the other hand is a frequently used means of expressing user opinion towards shared media items. We propose the use of time series analyses in order to infer potential tags and indexing terms for audio-visual content from user comments. In this way, we mitigate the vocabulary gap between queries and document descriptors. Additionally, we show how large-scale encyclopedias such as Wikipedia can aid the task of tag prediction by serving as surrogates for high-coverage natural language vocabulary lists. Our evaluation is conducted on a corpus of several million real-world user comments from the popular video sharing platform YouTube, and demonstrates significant improvements in retrieval performance.

This work together with Wen Li and Arjen P. de Vries has been accepted for full oral presentation at the 35th European Conference on Information Retrieval (ECIR) in Moscow, Russia.

Designing Human-Readable User Profiles for Search Evaluation

Forming an accurate mental model of a user is crucial for the qualitative design and evaluation steps of many information-centric applications such as web search, content recommendation, or advertising. This process can often be time-consuming as search and interaction histories become verbose. We present and analyze the usefulness of concise human-readable user profiles in order to enhance system tuning and evaluation by means of user studies.

This work together with Kevyn Collins-Thompson, Paul Bennett and Susan Dumais has been accepted for poster presentation at the 35th European Conference on Information Retrieval (ECIR) in Moscow, Russia.