A multilayer network diffusion-based model for reviewer recommendation

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.

Construction of Intelligent Recommendation Retrieval Model of FuJian Intangible Cultural Heritage Digital Archives Resources

In order to improve the consistency between the recommended retrieval results and user needs,improve the recommendation efficiency,and reduce the average absolute deviation of resource retrieval,a design method of intelligent recommendation retrieval model for Fujian intangible cultural heritage digital archive resources based on knowledge atlas is proposed.The TG-LDA(Tag-granularity LDA)model is proposed on the basis of the standard LDA(Linear Discriminant Analysis)model.The model is used to mine archive resource topics.The Pearson correlation coefficient is used to measure the relevance between topics.Based on the measurement results,the FastText deep learning model is used to achieve archive resource classification.According to the classification results,TF-IDF(term frequency–inverse document frequency)algorithm is used to calculate the weight of resource retrieval keywords to achieve resource retrieval,and a recommendation model of intangible cultural heritage digital archives resources is built through the knowledge map to achieve comprehensive and personalized recommendation of resources.The experimental results show that the recommendation and retrieval results of the proposed method are more in line with users’needs,can provide users with personalized digital archive resources,and the average absolute deviation of resource retrieval is low,the recommendation efficiency is high,and the utilization effect of archive resources is effectively improved.

Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

With the rapid development of the Internet globally since the 21st century,the amount of data information has increased exponentially.Data helps improve people’s livelihood and working conditions,as well as learning efficiency.Therefore,data extraction,analysis,and processing have become a hot issue for people from all walks of life.Traditional recommendation algorithm still has some problems,such as inaccuracy,less diversity,and low performance.To solve these problems and improve the accuracy and variety of the recommendation algorithms,the research combines the convolutional neural networks(CNN)and the attention model to design a recommendation algorithm based on the neural network framework.Through the text convolutional network,the input layer in CNN has transformed into two channels:static ones and non-static ones.Meanwhile,the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes higher.The recommendation algorithm combines CNN and attention system and divides the embedding layer into user information feature embedding and data name feature extraction embedding.It obtains data name features through a convolution kernel.Finally,the top pooling layer obtains the length vector.The attention system layer obtains the characteristics of the data type.Experimental results show that the proposed recommendation algorithm that combines CNN and the attention system can perform better in data extraction than the traditional CNN algorithm and other recommendation algorithms that are popular at the present stage.The proposed algorithm shows excellent accuracy and robustness.

Dynamic Trust Model Based on Service Recommendation in Big Data

In big data of business service or transaction,it is impossible to provide entire information to both of services from cyber system,so some service providers made use of maliciously services to get more interests.Trust management is an effective solution to deal with these malicious actions.This paper gave a trust computing model based on service-recommendation in big data.This model takes into account difference of recommendation trust between familiar node and stranger node.Thus,to ensure accuracy of recommending trust computing,paper proposed a fine-granularity similarity computing method based on the similarity of service concept domain ontology.This model is more accurate in computing trust value of cyber service nodes and prevents better cheating and attacking of malicious service nodes.Experiment results illustrated our model is effective.

Does knowledge of physical activity recommendations increase physical activity among Chinese college students？ Empirical investigations based on the transtheoretical model

Background: Based on the transtheoretical model, the current study investigated whether awareness of physical activity(PA) recommendations had an impact on the stages of PA behavior change and levels of PA among Chinese college students.Methods: In Study 1, with a cross-sectional study design, 9826 students were recruited, and their knowledge of international PA recommendations,PA stage distribution, and self-reported PA level were surveyed. Pearson's χ2 test was used to test whether those participants who were aware and not aware of PA guidelines were equally distributed across the stages of PA behavior, and independent t test was conducted to test the group difference in the actual levels of PA. In Study 2, 279 students who were not aware of the PA recommendations were randomly allocated to either an intervention group or a control group, and only those in the intervention group were presented with international PA guidelines. In both groups,students' PA stages and PA level were examined before the test and then 4 months post-test. Mc Nemar's test for correlated proportions and repeated-measures analysis of variance were conducted to examine the changes in PA stage membership and PA level after the intervention.Results: Study 1 results revealed that only 4.4% of the surveyed students had correct knowledge of PA recommendations. Those who were aware of the recommendations were in later stages of PA behavior(χ~2(4) = 167.19, p < 0.001). They were also significantly more physically active than those who were not aware of the recommendations(t(443.71) = 9.00, p < 0.001, Cohen's d = 0.53). Study 2 results demonstrated that the intervention group participants who were at the precontemplation and contemplation stages at the pre-test each progressed further in the PA stages in the post-test(χ~2(1) = 112.06, p < 0.001; χ~2(1) = 118.76, p = 0.03, respectively), although no significant change in PA level was observed(t(139) < 1, p = 0.89).Conclusion: The results showed that awareness of the PA recommendations was associated with higher stages and levels of PA behavior, and a brief educational exposure to PA recommendations led to improved stages of PA behavior but no change in the levels of PA among Chinese college students. More effective public health campaign strategies are needed to promote the dissemination of the PA recommendations and to raise the awareness of the Chinese student population.

Recommendation algorithm of cloud computing system based on random walk algorithm and collaborative filtering model

The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed.

Entity Burst Discriminative Model for Cumulative Citation Recommendation

Knowledge base acceleration-cumulative citation recommendation(KBA-CCR)aims to detect citation-worthiness documents from a chronological stream corpus for a set of target entities in a knowledge base.Most previous works only consider a number of semantic features between documents and target entities in the knowledge base,and then use powerful machine learning approaches such as logistic regression to classify relevant documents and non-relevant documents.However,the burst activities of an entity have been proved to be a significant signal to predict potential citations.In this paper,an entity burst discriminative model(EBDM)is presented to substantially exploit such burst features.The EBDM presents a new temporal representation based on the burst features,which can capture both temporal and semantic correlations between entities and documents.Meanwhile,in contrast to the bag-of-words model,the EBDM can significantly decrease the number of non-zero entries of feature vectors.An extensive set of experiments were conducted on the TREC-KBA-2012 dataset.The results show that the EBDM outperforms the performance of the state-of-the-art models.

A Novel Recommendation Service Method Based on Cloud Model and User Personality

The number of Internet Web services has become increasingly large recently.Cloud services consumers face a critical challenge in selecting services from abundant candidates.Due to the uncertainty of Web service QoS and the diversity of user characteristics,this paper proposes a Web service recommendation method based on cloud model and user personality(WSRCP),which employs cloud model similarity method to analyze the similarity of QoS feedback data among different users,to identify the user with high similarity to the potential user.Based on the QoS data of the users’feedback,Finally,user characteristic attribute Web service recommendation is implemented by personalized collaborative filtering algorithm.The experimental results on the WS-Dream dataset show that our approach not only solves the drawbacks of the sparse user service,but also improves the recommend accuracy.

Collaboration Filtering Recommendation Algorithm Based on the Latent Factor Model and Improved Spectral Clustering

Due to the development of E-Commerce, collaboration filtering (CF) recommendation algorithm becomes popular in recent years. It has some limitations such as cold start, data sparseness and low operation efficiency. In this paper, a CF recommendation algorithm is propose based on the latent factor model and improved spectral clustering (CFRALFMISC) to improve the forecasting precision. The latent factor model was firstly adopted to predict the missing score. Then, the cluster validity index was used to determine the number of clusters. Finally, the spectral clustering was improved by using the FCM algorithm to replace the K-means in the spectral clustering. The simulation results show that CFRALFMISC can effectively improve the recommendation precision compared with other algorithms.

Research on Recommendation Algorithms Based on Cloud Models in Probabilistic Linguistic Environments

To solve the problem that the traditional cloud model can't directly process the textual review information in the recommendation algorithm,this paper combines the merits of the cloud model in transforming qualitative and quantitative knowledge with the multi-granularity advantages of probabilistic linguistic term sets in representing uncertain information,and proposes a recommendation algorithm based on cloud model in probabilistic language environment.Initially,this paper quantifies the attributes in the review text based on the probabilistic linguistic term set.Subsequently,the maximum deviation method is used to determine the weight of each attribute in the evaluation information of the product to be recommended,and the comprehensive evaluation number and attribute weight are converted into the digital characteristic value of the cloud model by using the backward cloud generator.Finally,the products are recommended and sorted based on the digital characteristic value of the cloud model.The algorithm is applied to the recommendation of 10 hotels,and the results show that the method is effective and practical,enriching the application of cloud models in the recommendation field.

Increased physical activity, physician recommendation, and senior center participation

Physical activity is a recognized preventive health measure for seniors and an important focus for senior centers. This paper employs the Andersen Behavioral Model to explore increased physical activity and participation in three types of senior center activities: physical fitness, dance/aerobic classes, and chair exercises. Data were collected in 2006 on 798 and in 2007 on 742 participants at 21 multipurpose senior centers in a large urban county. Logistic regression analysis (PROC RLOGIST in SAS-callable SUDAAN) was employed to predict increased physical activity, with modes of center participation in physical activity as mediating factors. Predisposing and enabling factors predicted both engaging in center-based exercise programs and increases in physical activity;but the strongest predictors of increases in physical activity were needed factors: physician recommendations to increase exercise and to lose weight. Implications are that both SCs and healthcare providers are important to promote physical activity in the older population.

Point-of-Interest Recommendation in LocationBased Social Networks with Personalized Geo-Social Influence

Point-of-interest(POI) recommendation is a popular topic on location-based social networks(LBSNs).Geographical proximity,known as a unique feature of LBSNs,significantly affects user check-in behavior.However,most of prior studies characterize the geographical influence based on a universal or personalized distribution of geographic distance,leading to unsatisfactory recommendation results.In this paper,the personalized geographical influence in a two-dimensional geographical space is modeled using the data field method,and we propose a semi-supervised probabilistic model based on a factor graph model to integrate different factors such as the geographical influence.Moreover,a distributed learning algorithm is used to scale up our method to large-scale data sets.Experimental results based on the data sets from Foursquare and Gowalla show that our method outperforms other competing POI recommendation techniques.

Recommendations for Evidence-based Thinking on Migrant Worker Training

Evidence-based thinking originates from the United States. It stresses combination of actual facts and practical experience of managers to find out optimal evidence and make decisions accordingly. Migrant worker is a unique concept of China. Migrant workers are essential parts of industrial forces. However,due to limitation of their quality,they generally fail to bring into play their important function in the industry chain. At present,there are many problems in training models of migrant workers,leading to failure to raise their employment ability. This study is expected to introduce the evidence-based thinking into the building of training models for migrant workers,to provide recommendations for migrant worker training,raise efficiency of migrant worker training,and so as to bring into play important function of migrant workers in socialist construction of China.

FedRec:Trusted rank-based recommender scheme for service provisioning in federated cloud environment

The emergence of on-demand service provisioning by Federated Cloud Providers(FCPs)to Cloud Users(CU)has fuelled significant innovations in cloud provisioning models.Owing to the massive traffic,massive CU resource requests are sent to FCPs,and appropriate service recommendations are sent by FCPs.Currently,the FourthGeneration(4G)-Long Term Evolution(LTE)network faces bottlenecks that affect end-user throughput and latency.Moreover,the data is exchanged among heterogeneous stakeholders,and thus trust is a prime concern.To address these limitations,the paper proposes a Blockchain(BC)-leveraged rank-based recommender scheme,FedRec,to expedite secure and trusted Cloud Service Provisioning(CSP)to the CU through the FCP at the backdrop of base 5G communication service.The scheme operates in three phases.In the first phase,a BCintegrated request-response broker model is formulated between the CU,Cloud Brokers(BR),and the FCP,where a CU service request is forwarded through the BR to different FCPs.For service requests,Anything-as-aService(XaaS)is supported by 5G-enhanced Mobile Broadband(eMBB)service.In the next phase,a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender(RBR)model based on the CU requests.In the final phase,based on the matching recommendations between the CU and the FCP,Smart Contracts(SC)are executed,and resource provisioning data is stored in the Interplanetary File Systems(IPFS)that expedite the block validations.The proposed scheme FedRec is compared in terms of SC evaluation and formal verification.In simulation,FedRec achieves a reduction of 27.55%in chain storage and a transaction throughput of 43.5074 Mbps at 150 blocks.For the IPFS,we have achieved a bandwidth improvement of 17.91%.In the RBR models,the maximum obtained hit ratio is 0.9314 at 200 million CU requests,showing an improvement of 1.2%in average servicing latency over non-RBR models and a maximization trade-off of QoE index of 2.7688 at the flow request 1.088 and at granted service price of USD 1.559 million to FCP for provided services.The obtained results indicate the viability of the proposed scheme against traditional approaches.

A Recommendation Method for Highly Sparse Dataset Based on Teaching Recommendation Factorization Machines

There is no reasonable scientific basis for selecting the excellent teachers of the school’s courses.To solve the practical problem,we firstly give a series of normalization models for defining the key attributes of teachers’professional foundation,course difficulty coefficient,and comprehensive evaluation of teaching.Then,we define a partial weight function to calculate the key attributes,and obtain the partial recommendation values.Next,we construct a highly sparse Teaching Recommendation Factorization Machines(TRFMs)model,which takes the 5-tuples relation including teacher,course,teachers’professional foundation,course difficulty,teaching evaluation as the feature vector,and take partial recommendation value as the recommendation label.Finally,we design a novel Top-N excellent teacher recommendation algorithm based on TRFMs by course classification on the highly sparse dataset.Experimental results show that the proposed TRFMs and recommendation algorithm can accurately realize the recommendation of excellent teachers on a highly sparse historical teaching dataset.The recommendation accuracy is superior to that of the three-dimensional tensor decomposition model algorithm which also solves sparse datasets.The proposed method can be used as a new recommendation method applied to the teaching arrangements in all kinds of schools,which can effectively improve the teaching quality.

Context-Based Recommendation Approach for Measurement and Evaluation Projects

Data, information and knowledge are recognized as useful assets for analysis, recommendation and decision making at any business level of an organization. Providing the right information for decision making considering different user-requirements, projects and situations is, however, a difficult issue. A frequently-neglected challenge is to cope with the influence of contextual issues affecting the success of outcomes and decisions. Particularly, when conducting quality evaluations in software organizations, it is of paramount importance to identify beforehand the contextual issues affecting outcomes and interpretations for measurement and evaluation projects. Therefore, the relevant context information should be clearly identified, specified and recorded for performing more robust analysis and recommendations. In this work, a domain-independent context model and a mechanism to integrate it to any application domain is presented. The context model is built upon a measurement and evaluation framework enabling quantification and semantic capabilities. The context model is then integrated in the mentioned framework itself to enable recommendations in meas- urement and evaluation projects.

Desktop Data Driven Approach to Personalize Query Recommendation

Query recommendation is an effective method to help users describe their search intentions.In a personalized system,cold-start and the data sparsity were unavoidable,which directly lead to deficient performance of personalizing.As a significant part of a user’s personal information space,a personal computer owns lots of documents relevant to his or her interest.Therefore,desktop data was introduced to construct a user’s preference model.Furthermore,considering the variety of desktop data,relationship between search task and work task was simultaneously exploited to predict a user’s specific information need.Ten volunteers joined experiments to evaluate the potential of desktop data.A series of experiments were conducted and the results proved that desktop data greatly contributed to providing effective personalized reference words.Besides,the results demonstrated that a user’s long-term interest model performed steadier than work task context,but the most valuable words were the top-3 words extracted from the work context.

Evaluation of a model recommended for N fertilizer application in irrigated rice

The response of rice to N fertilizer applicationhas shown that high rates of N application donot always ensure a proportional increase inyield due to high N losses. A model, ORYZA-0 was developed by ten Berge for designingoptimum N fertilizer management strategy inrice. We evaluated the performance ofORYZA-0 in Jinhua, Zhejiang Province. ORYZA-0 includes N uptakes, partition-ing of N among the organs, and utilization ofleaf N in converting solar energy to dry mat-ter. It can predict the amount and time of Nfertilizer application to achieve a maximumbiomass or yield combining with Price algo-rithm optimization procedure.