Using co-views information to learn lecture recommendations

Haibin Liu, Sujatha Das, Dongwon Lee, Prasenjit Mitra, C. Lee Giles

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


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 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.

Original languageEnglish (US)
Pages (from-to)71-82
Number of pages12
JournalCEUR Workshop Proceedings
StatePublished - Jan 1 2011
EventECML/PKDD Discovery Challenge Workshop 2011, DCW 2011 - Athens, Greece
Duration: Sep 5 2011Sep 5 2011

All Science Journal Classification (ASJC) codes

  • General Computer Science


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