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, called SerenCDR , to model serendipity. SerenCDR leverages the knowledge beyond one domain as well as mitigates the inherent data sparsity problem in serendipity recommendations. The novelty of SerenCDR lies 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 is nearly impossible. To train and test SerenCDR , we have collected a two-domain ground truth dataset on serendipity, called SerenCDRLens . In addition, since we found that serendipity is sparse in SerenCDRLens , 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.

The recent California Consumer Privacy Act (CCPA) requires that personal data shall be limited to what is necessary for business purposes. Business services shall “implement technical safeguards that prohibit re-identification of the consumer to whom the information may pertain”. For recommender systems, we believe the legal concepts of limitation and technical safeguard are not specific enough to operationalize in practice. This study makes efforts to map the legislative challenges to practice of reducing personal data. More importantly, we borrowed the notion of uncertainty from the machine learning community, and added it as another aspect of recommendation utility, in addition to recommendation accuracy, to guide the data reduction process. The benefit of using uncertainty is that we have more comprehensive consideration while reducing the personal data. In addition, two major types of uncertainty in machine learning models: aleatoric uncertainty and epistemic uncertainty, helped us formulate two groups of data reduction strategies: within-user and between-user. We conducted a series of analyses regarding uncertainty change and accuracy loss caused by different data reduction strategies. We found that at the aggregate level, data reduction is feasible with certain data reduction strategies. At the individual level, the recommendation utility (both uncertainty and accuracy) loss incurred by data reduction disparately impacts different users — a finding which has implications for fairness and transparency of AI models. Our results reveal the difficulty and intricacy of the data reduction problem in the context of recommender systems.

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.