Nima Mirbakhsh
Abstract
Making accurate recommendations for cold-start users is a challenging yet important problem in recommendation systems. Including more information from other domains is a natural solution to improve the recommendations. However, most previous work in cross-domain recommendations has focused on improving prediction accuracy with several severe limitations. In this article, we extend our previous work on clustering-based matrix factorization in single domains into cross domains. In addition, we utilize recent results on unobserved ratings. Our new method can more effectively utilize data from auxiliary domains to achieve better recommendations, especially for cold-start users. For example, our method improves the recall to 21% on average for cold-start users, whereas previous methods result in only 15% recall in the cross-domain Amazon dataset. We also observe almost the same improvements in the Epinions dataset. Considering that it is often difficult to make even a small improvement in recommendations, for cold- start users in particular, our result is quite significant.
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Index Terms
- Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information
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Reviews
A. Squassabia
Collaborative recommender systems often provide disappointing suggestions to new users who volunteered very few or no ratings of their own for processing: this is known as the cold-start problem. Mitigating such shortcomings with cross-domain information helps; this paper builds on previous work by the authors to introduce incremental improvements to the cold-start problem. The authors previously published an approach to single-domain recommendations based on clustering of latent factors using matrix factorization and k -means. Here, they translate the same principle into seeding cold-start recommendations with cross-domain information, exploiting multiple domains where each domain is endowed with shared users, hence with some measure of user overlap. Validation was carried out using two datasets, one from Amazon comprising ratings for media (video, music, DVD) and goods (electronics, kitchen, toys) and another from Epinions comprising ten disparate categories of items. Validation compared results for single-domain, traditional cross-domain, and their new clustered cross-domain top- N ratings using recall for N of 5, 10, 15 or 20 as a metric. Cross-domain cold-start performed better than single-domain; clustered cross-domain performed as well as traditional cross-domain for low N , and better than traditional for larger N . The main contribution of this paper is the novelty of a relatively simple implementation for the underlying idea, which is not entirely original but new in this form. Its main limitation is in the difficulty of assessing impact on the basis of a single machine-driven metric on only two datasets; albeit traditionally acceptable, validation would be more informative if carried out with more data, multiple metrics, and ideally before a live audience. Online Computing Reviews Service
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