Multimedia Computing Group
The Multimedia Computing (MMC) Group develops algorithms for enriching, accessing, and searching large quantities of data. Such algorithms lie at the core of tomorrows’ search engines and large-scale recommender systems. The group sets its focus on developing systems that are oriented to the needs of users, and that solve the challenges faced by large-scale online content and service providers. Multimedia data analytics also has applications in the full range of fields that benefit from data science, including health, telecom, and geosciences.
The group has a track record of developing technologies that make possible optimized interaction with large collections of multimedia data (e.g., images, video, and music) in real-world contexts (e.g., within social networks). Our work requires a combination of mathematical models, machine learning techniques, and practical skills in algorithm development and evaluation. The members of the MMC group share expertise in multimedia information retrieval, recommender systems, multimedia signal processing, social network analysis, human computation (crowdsourcing) and quality of experience. Collaborations include joint work with researchers from Yahoo Labs, Telefónica Research, Microsoft Research and Google.
The work being carried out in the MMC Group encompasses a broad palette of research directions including:
- Multimedia content analysis and search
- Semantics extraction from multimedia data
- Multi-modal query expansion
- Multi-source search result reranking
- Multimedia information retrieval in a social network context
- Modeling information propagation and relationships in social networks
- Collaborative recommender systems
- Social recommendation
- Interaction with multimedia content
- (Affective) User profiling
- User (search/uploader) intent
- Query failure prediction
- Quality of multimedia experience
- Multimedia content management
- Multimedia databases and dataspaces
- Entity retrieval
Kan jouw ‘like’ op Facebook de samenleving ontwrichten?
Online informatie zoeken doen we massaal via Google, we sharen en liken van alles op sociale media, waarna algoritmes ons meer van hetzelfde aanbieden. Critici waarschuwen voor de gevaren van deze filterbubbels en echokamers.
Top Grant Module 2 for Huijuan Wang
Huijuan Wang will receive an NWO Top Grant Module for "Interaction Spreading Processes on Interdependent Social Networks".
Veni awarded to Cynthia Liem
NWO has announced the Veni laureates for 2018. Among them are seven scientists from TU Delft. The Veni's are intended to allow PhD researchers to develop their ideas for a further three years.