Transferring User Interests Across Websites with Unstructured Text for Cold-Start Recommendation
Yu-Yang Huang, Shou-De Lin · 2016
In this work, we investigate the possibility of cross-website transfer learning for tackling the cold-start problem.To address the coldstart issues commonly present in a collaborative ltering (CF) system, most existing crossdomain CF models require auxiliary rating data from another domain; nevertheless, under the cross-website scenario, such data is often unobtainable.Therefore, we propose the nearest-neighbor transfer matrix factorization (NT-MF) model, where a topic model is applied to the unstructured user-generated content in the source domain, and the similarity between users in the latent topic space is utilized to guide the target-domain CF model.Specically, the latent factors of the nearestneighbors are regarded as a set of pseudo observations, which can be used to estimate the unknown parameters in the model.Improvement over previous methods, especially for the cold-start users, is demonstrated with experiments on a real-world cross-website dataset.1