Aalborg Universitet Report on RecSys 2014 Workshop on New Trends in Content-Based Recommender Systems ( CBRecSys 2014 )

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. However, there are many recommendation domains and applications 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 books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2014 workshop aimed to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommender systems. 1 Motivation and Goals While content-based recommendation has been applied successfully in many different domains [9], 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 [5] have spurred on advances in collaborative filtering and how to utilize ratings and usage data from a community of users. However, there are many recommendation domains and applications where item 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 (e.g. [12]), but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. On top of that, in certain contexts of search, content-based recommendation plays a role as well. Personalised IR http://www.netflixprize. com/ http://www.dai-labor.de/camra2010/ ACM SIGIR Forum 20 Vol. 49 No. 1 June 2015 is strongly related to recommendation, but recent research in book search also identified search scenarios that combine aspects of retrieval and recommendation [8]. The CBRecSys 2014 workshop aimed to address this by providing a venue for papers dedicated to all aspects and new trends of content-based recommender systems. This would include both recommendation in domains where textual content is abundant (e.g. books, news, scientific articles, jobs, educational resources, and Web pages) as well as dedicated comparisons and combinations of content-based techniques with collaborative filtering approaches. 2 Workshop Focus & Format Authors were encouraged to submit papers on a variety of topics related to Content-Based Recommendation (CBR), including: • Developing novel recommendation approaches • Exploiting user-generated content for recommendation • Processing text reviews • Mining contextual data from content • Addressing limitations of recommender systems In particular, papers submitted to the the workshop focused on the following topics. Several papers presented hybrid systems combining collaborative filtering and content-based recommendation, finding them complementary, with content-based recommendation components especially suitable for tackling the cold-start problem. Other papers investigated how different content features can be used for similarity measures, and explored ways to identify which features are the most relevant for a given context. Some papers presented approaches to mine textual reviews for inferring user preferences on specific attributes of items, essentially deriving more structured feature information from unstructured text. Finally, several papers looked at semantic frameworks and Linked Open Data to measure item similarity across different domains. A total of 16 papers were submitted, of which 10 were accepted for an oral presentation (acceptance rate of 63%). All papers were reviewed by at least 3 reviewers.