Cold-start recommendations in Collective Matrix Factorization
This work addresses cold-start recommendations in recommender systems, which is an incremental improvement for users and items lacking interaction data.
The paper tackled the problem of making recommendations for new users or items with side information but no interaction data in collective matrix factorization, proposing a new formulation that improved cold-start recommendation quality over non-personalized methods, though with a trade-off in warm-start performance.
This work explores the ability of collective matrix factorization models in recommender systems to make predictions about users and items for which there is side information available but no feedback or interactions data, and proposes a new formulation with a faster cold-start prediction formula that can be used in real-time systems. While these cold-start recommendations are not as good as warm-start ones, they were found to be of better quality than non-personalized recommendations, and predictions about new users were found to be more reliable than those about new items. The formulation proposed here resulted in improved cold-start recommendations in many scenarios, at the expense of worse warm-start ones.