Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit
This addresses the need for more diverse news recommendations to benefit users and society, but it is incremental as it builds on existing work by extending diversification to multiple aspects.
The paper tackles the problem of limited diversity in news recommendations by proposing multi-aspect diversification across four recommendation modes, aiming to balance consumption for individual benefits like increased serendipity and societal benefits like decreased polarization.
News recommendations are complex, with diversity playing a vital role. So far, existing literature predominantly focuses on specific aspects of news diversity, such as viewpoints. In this paper, we introduce multi-aspect diversification in four distinct recommendation modes and outline the nuanced challenges in diversifying lists, sequences, summaries, and interactions. Our proposed research direction combines symbolic and subsymbolic artificial intelligence, leveraging both knowledge graphs and rule learning. We plan to evaluate our models using user studies to not only capture behavior but also their perceived experience. Our vision to balance news consumption points to other positive effects for users (e.g., increased serendipity) and society (e.g., decreased polarization).