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1. Recommender-as-a-Service with Chatbot Guided Domain-science Knowledge Discovery in a Science Gateway

   https://doi.org/10.1002/cpe.6080  

   Vekaria, K.; Calyam, P.; Sivarathri, S.; Wang, S.; Zhang, Y.; Pandey, A.; Chen, C.; Xu, D.; Joshi, T.; Nair, S.S. (January 2020, Wiley Concurrency and Computation: Practice and Experience)

   Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity,we present a novel “OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons). 

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2. Review-Based Cross-Domain Collaborative Filtering: A Neural Framework

   Doan, T.N; Sahebi, S. (January 2019, Proceedings of the Third Workshop on Recommendation in Complex Scenarios)

   Cross-domain collaborative filtering recommenders exploit data from other domains (e.g., movie ratings) to predict users’ interests in a different target domain (e.g., suggest music). Most current cross-domain recommenders focus on modeling user ratings but pay limited attention to user reviews. Additionally, due to the complexity of these recommender systems, they cannot provide any information to users to support user decisions. To address these challenges, we propose Deep Hybrid Cross Domain (DHCD) model, a cross-domain neural framework, that can simultaneously predict user ratings, and provide useful information to strengthen the suggestions and support user decision across multiple domains. Specifically, DHCD enhances the predicted ratings by jointly modeling two crucial facets of users’ product assessment: ratings and reviews. To support decisions, it models and provides natural review-like sentences across domains according to user interests and item features. This model is robust in integrating user rating and review information from more than two domains. Our extensive experiments show that DHCD can significantly outperform advanced baselines in rating predictions and review generation tasks. For rating prediction tasks, it outperforms cross-domain and single-domain collaborative filtering as well as hybrid recommender systems. Furthermore, our review generation experiments suggest an improved perplexity score and transfer of review information in DHCD. 

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3. Neighborhood-Aware Negative Sampling for Student Knowledge and Behavior Modeling

   https://doi.org/10.1609/aaai.v39i12.33460  

   Zhao, Siqian; Sahebi, Sherry (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Simple random negative sampling is a technique used to enhance decision-making in sequential models with numerous potential negative instances, like recommender systems. However, it ignores the patterns that can be discovered in complex sequences to select the most informative negative samples. In this paper, we address this challenge by introducing a Neighborhood-Aware Negative Sampling (NANS) technique in the context of student knowledge modeling (KM) and behavior modeling (BM). In the education domain, KM quantifies student knowledge based on past performance, while BM focuses on behaviors like student preferences of questions. With the vast number of problems to choose from and the intricate relationship between student knowledge and behavior, selecting the proper negative samples becomes a notable challenge in this problem. NANS, along with our proposed multi-objective, multi-task sequential model for KM and BM, NANS-KoBeM frames the simultaneous modeling of student knowledge and question selection as a multi-task learning problem with dual objectives: predicting students’ performance and their question selections. 

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4. Facilitating Data Discovery for Large-scale Science Facilities using Knowledge Networks

   https://doi.org/10.1109/IPDPS49936.2021.00073  

   Qin, Yubo; Rodero, Ivan; Parashar, Manish (May 2021, 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS))

   null (Ed.)

   Large-scale multiuser scientific facilities, such as geographically distributed observatories, remote instruments, and experimental platforms, represent some of the largest national investments and can enable dramatic advances across many areas of science. Recent examples of such advances include the detection of gravitational waves and the imaging of a black hole’s event horizon. However, as the number of such facilities and their users grow, along with the complexity, diversity, and volumes of their data products, finding and accessing relevant data is becoming increasingly challenging, limiting the potential impact of facilities. These challenges are further amplified as scientists and application workflows increasingly try to integrate facilities’ data from diverse domains. In this paper, we leverage concepts underlying recommender systems, which are extremely effective in e-commerce, to address these data-discovery and data-access challenges for large-scale distributed scientific facilities. We first analyze data from facilities and identify and model user-query patterns in terms of facility location and spatial localities, domain-specific data models, and user associations. We then use this analysis to generate a knowledge graph and develop the collaborative knowledge-aware graph attention network (CKAT) recommendation model, which leverages graph neural networks (GNNs) to explicitly encode the collaborative signals through propagation and combine them with knowledge associations. Moreover, we integrate a knowledge-aware neural attention mechanism to enable the CKAT to pay more attention to key information while reducing irrelevant noise, thereby increasing the accuracy of the recommendations. We apply the proposed model on two real-world facility datasets and empirically demonstrate that the CKAT can effectively facilitate data discovery, significantly outperforming several compelling state-of-the-art baseline models. 

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5. Using Visual Representations: How using visual representations may provide teacher leaders with a tool for supporting sustained teacher learning

   Placa, Nicora; Koellner, Karen; Seago, Nanette (December 2023, NCSM journal of mathematics education leadership)

   This paper highlights two teachers that participated in two different professional development (PD) experiences who sustained new teaching practices and learning five years after participating. Both PD projects focused on visual representations (VRs) and encouraged and modeled ambitious teaching practices. Teachers provided video clips and participated in interviews to illustrate and describe changes that took place in their learning and practice. Our qualitative analysis showed that (1) the teachers’ use of VRs appears to be strongly connected to teachers' own active learning of VRs in PD, (2) VRs appears to be a key factor that supported the teachers’ use of other ambitious teaching practices in their classroom and (3) that the two teachers remembered and continued to use ambitious practices and VRs in their classrooms in ways that not only aligned to the goals and intention of the PD, but also adapted and extended representations to different mathematical domains and settings. Implications for mathematics education leaders suggest that a focus on VRs may be one tool to anchor learning to deepen teachers’ abilities to engage in ambitious teaching practices. 

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