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Serendipity is a notion that means an unexpected but valuable discovery. Due to its elusive and subjective nature, serendipity is difficult to study even with today's advances in machine learning and deep learning techniques. Both ground truth data collecting and model developing are the open research questions. This paper addresses both the data and the model challenges for identifying serendipity in recommender systems. For the ground truth data collecting, it proposes a new and scalable approach by using both user generated reviews and a crowd sourcing method. The result is a large-scale ground truth data on serendipity. For model developing, it designed a self-enhanced module to learn the fine-grained facets of serendipity in order to mitigate the inherent data sparsity problem in any serendipity ground truth dataset. The self-enhanced module is general enough to be applied with many base deep learning models for serendipity. A series of experiments have been conducted. As the result, a base deep learning model trained on our collected ground truth data, as well as with the help of the self-enhanced module, outperforms the state-of-the-art baseline models in predicting serendipity. 

As the popularity of online travel platforms increases, users tend to make ad-hoc decisions on places to visit rather than preparing the detailed tour plans in advance. Under the situation of timeliness and uncertainty of users’ demand, how to integrate real-time context into a dynamic and personalized recommendations have become a key issue in travel recommender system. In this paper, by integrating the users’ historical preferences and real-time context, a location-aware recommender system called TRACE (Travel Reinforcement Recommendations Based on Location-Aware Context Extraction) is proposed. It captures users’ features based on location-aware context learning model, and makes dynamic recommendations based on reinforcement learning. Specifically, this research: (1) designs a travel reinforcing recommender system based on an Actor-Critic framework, which can dynamically track the user preference shifts and optimize the recommender system performance; (2) proposes a location-aware context learning model, which aims at extracting user context from real-time location and then calculating the impacts of nearby attractions on users’ preferences; and (3) conducts both offline and online experiments. Our proposed model achieves the best performance in both of the two experiments, which demonstrates that tracking the users’ preference shifts based on real-time location is valuable for improving the recommendation results. 

Abstract The Andean bear is the only extant member of the Tremarctine subfamily and the only extant ursid species to inhabit South America. Here, we present an annotated de novo assembly of a nuclear genome from a captive-born female Andean bear, Mischief, generated using a combination of short and long DNA and RNA reads. Our final assembly has a length of 2.23 Gb, and a scaffold N50 of 21.12 Mb, contig N50 of 23.5 kb, and BUSCO score of 88%. The Andean bear genome will be a useful resource for exploring the complex phylogenetic history of extinct and extant bear species and for future population genetics studies of Andean bears. 

This paper exploits triggered lightning as a point source for the basin‐scale electromagnetic tomographic survey to image 3‐D subsurface electrical properties in basins. This paper further develops a new temporal moment approach, overcoming the difficulties in forward and inverse modeling of 3‐D Maxwell’s equations with heterogeneous parameter fields. Using this approach, we find that the influence of a single triggered lightning strike covers a radius of 20–70 km with detectable signals. The cross‐correlation analysis between the moment difference of the electric and electric/magnetic property field indicates that the approach is suitable for mapping subsurface electric conductivity () heterogeneity. A numerical experiment with 3‐D spatially random parameter fields demonstrates that the method captures the spatial distribution of electric conductivity over large areas with a sparse monitoring network. It reveals the potential of using triggered lightning as a basin‐scale electric/magnetic tomography survey.