Using co-views information to learn lecture recommendations

Content-based methods are commonly adopted for addressing the cold-start problem in recommender systems. In the cold-start scenario, usage information regarding an item and/or item preference information of a user is unavailable since the item or the user is new in the system. Thus collaborative filtering strategies cannot be employed but instead item-specific attributes or the user profile information are used to make recommendations. We focus on lecture recommendations for the data in videolectures.net that was made available as part of the ECML/PKDD Discovery Challenge. We propose the use of co-view information based on previously seen lecture pairs for learning the weights of lecture attributes for ranking lectures for the cold-start recommendation task. Co-viewed triplet and pair information is also used to estimate the probability that a lecture would be seen, given a set of previously seen lectures. Our results corroborate the effectiveness of using co-view information in learning lecture recommendations.