  Existing collaborative ranking based recommender systems tend to perform best
when there is enough observed ratings for each user and the observation is made
completely at random. Under this setting recommender systems can properly
suggest a list of recommendations according to the user interests. However,
when the observed ratings are extremely sparse (e.g. in the case of cold-start
users where no rating data is available), and are not sampled uniformly at
random, existing ranking methods fail to effectively leverage side information
to transduct the knowledge from existing ratings to unobserved ones. We propose
a semi-supervised collaborative ranking model, dubbed \texttt{S$^2$COR}, to
improve the quality of cold-start recommendation. \texttt{S$^2$COR} mitigates
the sparsity issue by leveraging side information about both observed and
missing ratings by collaboratively learning the ranking model. This enables it
to deal with the case of missing data not at random, but to also effectively
incorporate the available side information in transduction. We experimentally
evaluated our proposed algorithm on a number of challenging real-world datasets
and compared against state-of-the-art models for cold-start recommendation. We
report significantly higher quality recommendations with our algorithm compared
to the state-of-the-art.
