Скачать или смотреть Biased User History Synthesis for Personalized Long-Tail Item Recommendation

by Keshav Balasubramanian (University of Southern California), Abdulla Alshabanah (University of Southern California), Elan Markowitz (University of Southern California), Greg Ver Steeg (University of California Riverside) and Murali Annavaram (University of Southern California)

Abstract:
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/BiasedUserH....

Full Text: https://dl.acm.org/doi/10.1145/364045...