CBRecSys 2016 - 2016 3rd Workshop on New Trends in Content-Based Recommender Systems

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. In recent years, competitions like the Netflix Prize, CAMRA, and the Yahoo! Music KDD Cup 2011 have spurred on advances in collaborative filtering and how to utilize ratings and usage data. However, there are many domains where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data.
For some domains, such as movies the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as e-learning, jobs, health, books, news, scientific articles and Web pages we do not know if and how these data sources should be combined to provided the best recommendation performance. Aspects such as the order in which items are created, recommended and consumed, in e.g. news, e-learning and jobs, and possibilities for interactivity are challenges for content-based recommender systems.
The CBRecSys 2016 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation. This would include both recommendation in domains where textual content is abundant (e.g., books, news, scientific articles, jobs, educational resources, Web pages, etc.) as well as dedicated comparisons of content-based techniques with collaborative filtering in different domains. Other relevant topics related to content-based recommendations could include opinion mining for text/book recommendation, semantic recommendation, content-based recommendation to alleviate cold-start problems, as well as serendipity, diversity and cross-domain recommendation.
Topics of interest
Processing and Representing Content
Estimating (implicit) ratings associated with text reviews
Opinion mining and sentiment analysis of text reviews to support content-based recommendation
Multilingual Content representation
Exploiting Deep Learning approaches for content representation
Exploiting semantic technologies for processing and representing content
Extracting user personality traits and factors from text reviews for
recommendation
Exploiting user generated contents
Social tag-based recommender systems
Exploiting Semantic Web and Linked Open Data in content-based recommender systems
User Profiling based on Big, Social and Linked Data
Mining microblogging data in content-based recommender systems
Mining contextual data from content
Extraction of contextual signals from text contents for recommendation
Considering the time dimension in content-based recommendation
Mood-based recommender systems
Addressing limitations of recommender system
Addressing the cold-start problem with content-based recommendation approaches
Increasing diversity in content-based recommendations
Providing novelty in content-based recommendations
Developing novel recommendation approaches
Hybrid strategies combining content-based and collaborative filtering recommendations
Content-based approaches to cross-system and cross-domain recommendation
Latent factor models for content-based and hybrid recommendation