KT-CDULF: Knowledge Transfer in Context-Aware Cross-Domain Recommender Systems via Latent User Profiling

Abstract

Recommender systems are crucial in today’s digital world, by enhancing user engagement experience in digital ecosystems. Internet of things (IoT) have huge potential to generate dynamic and real time data. The data generated through IoT are being utilized to extract dynamic context of the user. Integrating recommender systems with context-aware (IoT data) and cross-domain (Knowledge Transfer) capabilities have the capacity to further enhance the accuracy and relevance of recommendation systems. However, recommender systems struggle with the cold start problem, where non-availability of data hinders to make effective recommendations for new users. Therefore, IoT-enabled Context-Aware Cross-Domain Recommender Systems may employ latent user profiling to provide personalized and exceedingly relevant recommendations across domains. The proposed system, named Knowledge Transfer Cross-Domain User Latent Factors (KT-CDULF), creates a user profile that spans multiple data domains, capturing a wide range of user behavior on all domains. The KT-CDULF captures the combined knowledge across domains to make recommendations even with limited user data, i.e. cold start problem. Domain-independent factors, and context can be used across domains to make relevant recommendations. The effectiveness of a recommender system depends on the density of the ratings in the data. To address this, KT-CDULF used two benchmark datasets to create user profiles and an item-rating matrix with additional context extracted from IoT generated dataset. KT-CDULF is evaluated and compared it with state-of-the-art models for recommender systems and achieves an accuracy of 98%, demonstrating the benefits of transferring knowledge containing context across data domains in recommender systems.