Expert Recommendation in Community Question Answering via Heterogeneous Content Network Embedding

ExpertRecommendation(ER)aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering(CQA)web services.How to model questions and users in the heterogeneous content network is critical to this task.Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues.Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for modeling.However,they often fail to distinguish the nodes’personalized preferences and only consider the textual content of a part of the nodes in network embedding learning,while ignoring the semantic relevance of nodes.In this paper,we propose a novel framework that jointly considers the structural proximity relations and textual semantic relevance to model users and questions more comprehensively.Specifically,we learn topology-based embeddings through a hierarchical attentive network learning strategy,in which the proximity information and the personalized preference of nodes are encoded and preserved.Meanwhile,we utilize the node’s textual content and the text correlation between adjacent nodes to build the content-based embedding through a meta-context-aware skip-gram model.In addition,the user’s relative answer quality is incorporated to promote the ranking performance.Experimental results show that our proposed framework consistently and significantly outperforms the state-of-the-art baselines on three real-world datasets by taking the deep semantic understanding and structural feature learning together.The performance of the proposed work is analyzed in terms of MRR,P@K,and MAP and is proven to be more advanced than the existing methodologies.

A Survey on Expert Recommendation in Community Question Answering

Community question answering （CQA） represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify the questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.

Expert panel’s guideline on cervicogenic headache:The Chinese Association for the Study of Pain recommendation

Cervicogenic headache(CEH)has been recognized as a unique category of headache that can be difficult to diagnose and treat.In China,CEH patients are managed by many different specialties,and the treatment plans remain controversial.Therefore,there is a great need for comprehensive evidence-based Chinese experts’recommendations for the management of CEH.The Chinese Association for the Study of Pain asked an expert panel to develop recommendations for a series of questions that are essential for daily clinical management of patients with CEH.A group of multidisciplinary Chinese Association for the Study of Pain experts identified the clinically relevant topics in CEH.A systematic review of the literature was performed,and evidence supporting the benefits and harms for the management of CEH was summarized.Twenty-four recommendations were finally developed through expert consensus voting for evidence quality and recommendation strength.We hope this guideline provides direction for clinicians and patients making treatment decisions for the management of CEH.

Network Pharmacology Approach to Investigate the Preventive Mechanism of Hunan Expert Group Recommended Chinese Medicine Prevention No.2 Prescription Against COVID-19

Objective To explore the possible preventive mechanism of Hunan expert group recommended Chinese medicine prescription of No.2(Pre-No.2)against coronavirus disease 2019(COVID-19)by network pharmacology method.Methods The target proteins of effective components and active compounds in Pre-No.2 were screened by searching the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP).A component-target-disease interaction network of Pre-No.2 was constructed by Cytoscape 3.7.2,gene ontology(GO)analysis,and Kyoto encyclopedia of genes and genomes(KEGG)analysis of target protein pathway by DAVID.Results A total of 163 compounds and 278 target protein targets in Pre-No.2 were collected from the TCMSP database.Kaempferol,wogonin,7-methoxy-2-methyl isoflavone,formononetin,isorhamnetin,and licochalcone A were the most frequent targets in the regulatory network.GO enrichment analysis showed that Pre-No.2 regulated response to virus,viral processes,humoral immune responses,defense responses to virus and viral entry into host cells.KEGG enrichment analysis showed that the formula regulated the NF-κB signaling pathway,B cell receptor signaling pathway,viral carcinogenesis,T cell signaling pathway and FcγR-mediated phagocytosis signaling pathway.Conclusions Pre-No.2 may play a preventive role against COVID-19 through regulation of the Toll-like signaling,T cell signaling,B cell signaling and other signaling pathways.It may regulate the immune system to protect against anti-influenza virus.