Report on RecSys 2014

Abstract

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.

Boosting Memory-Based Collaborative Filtering Using Content-Metadata

“…Researchers have suggested many recommendation methods using the content-metadata [7][8][9][10][11] that are typically provided in the form of textual descriptions of content features. Such recommendation methods usually construct each user's profile or predictor using metadata from all content the user rates and then estimate the rating score of content using the user's profile or predictor.…”