Multimedia recommendation: technology and techniques

In recent years, we have witnessed a rapid growth in the availability of digital multimedia on various application platforms and domains. Consequently, the problem of information overload has become more and more serious. In order to tackle the challenge, various multimedia recommendation technologies have been developed by different research communities (e.g., multimedia systems, information retrieval, machine learning and computer version). Meanwhile, many commercial web systems (e.g., Flick, YouTube, and Last.fm) have successfully applied recommendation techniques to provide users personalized content and services in a convenient and flexible way. When looking back, the information retrieval (IR) community has a long history of studying and contributing recommender system design and related issues. It has been proven that the recommender systems can effectively assist users in handling information overload and provide high-quality personalization. While several courses were dedicated to multimedia retrieval in the recent decade, to the best of our knowledge, the tutorial is the first one specifically focusing on multimedia recommender systems and their applications on various domains and media contents. We plan to summarize the research along this direction and provide an impetus for further research on this important topic