Cross-Domain Recommender Systems

Abstract

The proliferation of e-commerce sites and online social media has allowed users to provide preference feedback and maintain profiles in multiple systems, reflecting a variety of their tastes and interests. Leveraging all the user preferences available in several systems or domains may be beneficial for generating more encompassing user models and better recommendations, e.g., through mitigating the cold-start and sparsity problems in a target domain, or enabling personalized crossselling recommendations for items from multiple domains. Cross-domain recommender systems, thus, aim to generate or enhance recommendations in a target domain by exploiting knowledge from source domains. In this chapter, we formalize the cross-domain recommendation problem, unify the perspectives from which it has been addressed, analytically categorize, describe and compare prior work, and identify open issues for future research.

User Model in a Box: Cross-System User Model Transfer for Resolving Cold Start Problems

“…The classic examples or collaborative filtering transfer are [1,2]. These papers offered an extensive discussion of cross-domain recommendation problems and suggested interesting generic approaches but were not able to explore these approaches in a true cross-domain context using instead artificial datasets produced by separation of single-domain user movie ratings into subdomains.…”

Enhanced Cross Domain Recommender System using Contextual parameters in Temporal Domain

“…Bayesian generative model to generate and predict ratings for multiple related CF domains on the site-time coordinate system, is used as the basic model for the cross-domain CF framework which is extended for modeling user-interest drifting over time. Cross-domain recommender systems [1] Yes No…”

Mining Semantic Data, User Generated Contents, and Contextual Information for Cross-Domain Recommendation

“…Tiroshi and Kuflik [9] evaluate the influence of the involved domains in the recommendation using a kNN approach in which the neighborhood of a user in the target domain is selected among the most similar users from a source domain. To address the problem of little domain overlap, Cremonesi et al [2] model user and item similarities through graphs, in which all possible paths linking two users or items are used to enhance user-and item-based neighborhood algorithms.…”