Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

A Survey on Conversational Recommender Systems

A View of the Brain By J. S. Griffith. (Oxford Science Research Papers, 1.) Pp. 79. (Oxford: Clarendon Press; London: Oxford University Press, 1967.) 15s. net. Models of the Nervous System By Sid Deutsch. Pp. vii + 266. (New York and London: John Wiley and Sons, Inc., 1967.)

Understanding the Brain

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users’ preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this article, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

The rapid growth of COVID-19 publications has driven clinical researchers and healthcare professionals in pursuit to reduce the knowledge gap on reliable information for effective pandemic solutions The manual task of retrieving high-quality publications based on the evidence pyramid levels, however, presents a major bottleneck in researchers’ workflows In this paper, we propose an “evidence-based” recommender system namely, KnowCOVID-19 that utilizes an edge computing service to integrate recommender modules for data analytics using end-user thin-clients The edge computing service features chatbot-based web interface that handles a given COVID-19 publication dataset using two recommender system modules: (i) evidence-based filtering that observes domain specific topics across the literature and classifies the filtered information according to a clinical category, and (ii) social filtering that allows diverse experts with similar objectives to collaborate via a “social plane” to jointly find answers to critical clinical questions to fight the pandemic We compare the Domain-specific Topic Model (DSTM) used in our evidence-based filtering with state-of-the-art models considering the CORD-19 dataset (a COVID-19 publication archive) and show improved generalization effectiveness as well as knowledge pattern query effectiveness In addition, we conduct a comparison study between a manual literature review process and the KnowCOVID-19 augmented process, and evaluate the benefits of our information retrieval techniques over important queries provided by COVID-19 clinical experts

/pdf/evidence-based-recommender-system-for-a-covid-19-publication-4csxhguvlj.pdf

Evidence-Based Recommender System for a COVID-19 Publication Analytics Service

Cloud computing has been devoted to the development of recent advanced technology, aiding in improving a government’s functioning and the services it offers to its people and organizations. It has achieved widespread popularity due to the rising number of benefits, including scalability, versatility, reliability, safety, confidentiality, pay-per-use, and dynamicity for the distribution of IT services. This paper explores a descriptive literature review and aims to classify the scattered communities involved in cloud computing research in information technology. This involves 51 referred published articles between 2010 and 2020 relating to the inception of community cloud computing by various organizations. Based on a systematic structured of reviewed literature and grounded theory approach, articles are categorized with respect to the eleven key influencing variables: cost, security, performance, QoS, trust, accuracy, ease of use, usefulness, architecture, framework, and model. Following that, those variables are comprehended into final one in order to envisage the community cloud computing adoption factors and concepts. This will consent for a strong examination of the community cloud computing phenomenon. The study is a novel attempt to demonstrate the differences in key factors for cloud computing in varying community settings and their causal relationships among the considered variables. The findings from the long-term systematic review contribute by assisting a collection of determinants for the penetration of community clouds services and potential adopters. It could serve as a basis for future directions and the development of theories in the exploring the community cloud computing in the field of information systems. This study contributes to identify various existing research gaps by providing holistic insights and future exploratory approaches that are anticipated to result in a robust unified structure for the adoption of community cloud computing services in the higher education institution (HEIs).

Descriptive Literature Review and Classification of Community Cloud Computing Research

Neuro-scientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. Although science gateways have democratized relevant high performance/throughput resources, users require expert knowledge about programming and infrastructure configuration that is beyond the repertoire of most neuroscience programs. These factors become deterrents for the successful adoption and the ultimate diffusion (i.e., systemic spread) of science gateways in the neuroscience community. In this paper, we present a novel intuitionistic fuzzy logic based conversational recommender that can provide guidance to users when using science gateways for research and education workflows. The users interact with a context-aware chatbot that is embedded within custom web-portals to obtain simulation tools/resources to accomplish their goals. In order to ensure user goals are met, the chatbot profiles a user’s cyberinfrastructure and neuroscience domain proficiency level using a ‘usability quadrant’ approach. Simulation of user queries for an exemplary neuroscience use case demonstrates that our chatbot can provide step-by-step navigational support and generate distinct responses based on user proficiency.

Fuzzy-Based Conversational Recommender for Data-intensive Science Gateway Applications

The adoption of big data analytics in healthcare applications is overwhelming not only because of the huge volume of data being analyzed, but also because of the heterogeneity and sensitivity of the data. Effective and efficient analysis and visualization of secure patient health records are needed to e.g., find new trends in disease management, determining risk factors for diseases, and personalized medicine. In this paper, we propose a novel community cloud architecture to help clinicians and researchers to have easy/increased accessibility to data sets from multiple sources, while also ensuring security compliance of data providers is not compromised. Our cloud-based system design configuration with cloudlet principles ensures application performance has high-speed processing, and data analytics is sufficiently scalable while adhering to security standards (e.g., HIPAA, NIST). Through a case study, we show how our community cloud architecture can be implemented along with best practices in an ophthalmology case study which includes health big data (i.e., Health Facts database, I2B2, Millennium) hosted in a campus cloud infrastructure featuring virtual desktop thin-clients and relevant Data Classification Levels in storage.

https://dl.acm.org/doi/pdf/10.1145/3288599.3295594

Community cloud architecture to improve use accessibility with security compliance in health big data applications

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, data sets 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).

Recommender‐as‐a‐service with chatbot guided domain‐science knowledge discovery in a science gateway

Bold scientific research tasks today need multi-disciplinary knowledge and interdisciplinary collaborations that require finding scholars from a particular domain with relevant knowledge. Given the variety of scholars and diversity of research tasks, finding the appropriate scholar is a critically important and challenging problem for scientific communities. Prior approaches to identify scholars use supervised learning with fixed or high-level research interest tags. Such approaches make it hard to recognize scholars with specific interests, or track their changes in research interests. Hence, there is a need to investigate suitable methods to quantify scholars' expertise knowledge for matching research tasks. In this paper, we propose a novel model viz., "ScholarFinder" that uses contextual information (abstracts or publications) for embedding a scholar's knowledge in an unsupervised learning manner. Subsequently, with pre-trained knowledge embeddings, we can perform machine learning tasks such as classification, visualization or checking whether a scholar is suitable for performing particular research tasks or not. Based on our pre-trained techniques, we also provide a novel negative sampling method to overcome the issues of missing negative samples. Using a "follow-the-money" strategy, we apply our model to a large collection of NSF (National Science Foundation) grant awards dataset collected over the last twenty years that contains more than 20,000 award records (with project abstracts and names), corresponding to 15,074 scholars who received grants. We evaluate different deep learning models to see how to use pre-trained knowledge embedding for achieving optimal performance, and how our negative sampling method improves model performance. We also compare our model with state-of-the-art baseline models (e.g., XGBoost, DNN), and our results show that the ScholarFinder model outperforms those models in terms of precision, recall, F1-score, and accuracy.

Sai Swathi Sivarathri

Papers

Fuzzy-Based Conversational Recommender for Data-intensive Science Gateway Applications

Community cloud architecture to improve use accessibility with security compliance in health big data applications

Recommender‐as‐a‐service with chatbot guided domain‐science knowledge discovery in a science gateway

ScholarFinder: Knowledge Embedding Based Recommendations using a Deep Generative Model