This work explores unifying knowledge enhanced recommendation with multi-domain recommendation systems in a conversational AI assistant application. Multi-domain recommendation leverages users’ interactions in previous domains to improve recommendations in a new one. Knowledge graph enhancement seeks to use external knowledge graphs to improve recommendations within a single domain. Both research threads incorporate related information to improve the recommendation task. We propose to unify these approaches: using information from interactions in other domains as well as external knowledge graphs to make predictions in a new domain that would not be possible with either information source alone. We develop a new model and demonstrate the additive benefit of these approaches on a dataset derived from millions of users’ queries for content across three domains (videos, music, and books) in a live virtual assistant application. We demonstrate significant improvement on overall recommendations as well as on recommendations for new users of a domain.

Elan Markowitz, Anil Ramakrishna, Jwala Dhamala, Ninareh Mehrabi, Charith Peris, Rahul Gupta, Kai-Wei Chang, Aram Galstyan. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Recommendation systems connect users to items and create value chains in the internet economy. Recommendation systems learn from past user-item interaction histories. As such, items that have short interaction histories, either because they are new or not popular, have been shown to be disproportionately under-recommended. This long-tail item problem can exacerbate model bias, and reinforce poor recommendation of tail items. In this paper, we propose biased user history synthesis, to not only address this problem but also achieve better personalization in recommendation systems. As a result, we concurrently improve tail and head item recommendation performance. Our approach is built on a tail item biased User Interaction History (UIH) sampling strategy and a synthesis model that produces an augmented user representation from the sampled user history. We provide a theoretical justification for our approach using information theory and demonstrate through extensive experimentation, that our model outperforms state-of-the-art baselines on tail, head, and overall recommendation. The source code is available at https://github.com/lkp411/BiasedUserHistorySynthesis.