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Serendipity means unexpected discoveries that are valuable, with positive outcomes ranging from personal benefits to scientific breakthroughs. This study proposes a cross-domain recommendation model, calledSerenCDR, to model serendipity.SerenCDRleverages the knowledge beyond one domain as well as mitigates the inherent data sparsity problem in serendipity recommendations. The novelty ofSerenCDRlies in the fact that it is the first deep learning-based cross-domain model for a serendipity task. More importantly, it does not rely on any overlapping users or overlapping items across different domains, which especially fits for the task of recommending serendipity, because serendipity in a single domain tends to be sparse; finding overlapping users or overlapping items in other domains are nearly impossible. To train and testSerenCDR, we have collected a two-domain ground truth dataset on serendipity, calledSerenCDRLens. In addition, since we found that serendipity is sparse inSerenCDRLens, we designed an auxiliary loss function to supplement the main loss function to enhance serendipity learning. Through a series of experiments, we have harvested positive performance in recommending serendipity, empowering users with increased chances of bumping into unexpected but valuable discoveries. 

Automatically finding contradictions from text is a fundamental yet under-studied problem in natural language understanding and information retrieval. Recently, topology, a branch of mathematics concerned with the properties of geometric shapes, has been shown useful to understand semantics of text. This study presents a topological approach to enhancing deep learning models in detecting contradictions in text. In addition, in order to better understand contradictions, we propose a classification with six types of contradictions. Following that, the topologically enhanced models are evaluated with different contradictions types, as well as different text genres. Overall we have demonstrated the usefulness of topological features in finding contradictions, especially the more latent and more complex contradictions in text.