Graph ranking-based recommender systems

University of Technology Sydney. Faculty of Engineering and Information Technology.The rapid growth of web technologies and the volume of Internet users provide excellent opportunities for large-scale online applications but also have caused increasing information overloading problems whereby users find it hard to locate relevant information to exactly meet their needs efficiently by basic Internet searching functions. Recommender systems are emerging to aim to handle this issue and provide personalized suggestions of resources (items) to particular users, which have been implemented in many domains such as online shopping assistants, information retrieval tools and decision support tools. In the current era of information explosion, recommender systems are facing some new challenges. Firstly, there are increasing tree-structured taxonomy attributes as well as freeform folksonomy tags associated with items. Secondly, there are increasing explicit and implicit social relations or correlations available for web users. Thirdly, there is increasingly diverse contextual information that affects or reflects user preferences. Furthermore, the recommendation demands of users are becoming diverse and flexible. In other words, users may have changing multi-objective recommendation requests at different times. This research aims to handle these four challenges and propose a set of recommendation approaches for different scenarios. Graph ranking theories are employed due to their ease of modelling different information entities and complex relations and their good extensibility. In different scenarios, different graphs are generated and some unique graph ranking problems are raised. Concretely, we first propose a bipartite graph random walk model for a hybrid recommender system integrating complex item content information of both tree-structured taxonomy attributes and free-form folksonomy tags. Secondly, we propose a multigraph ranking model for a multi-relational social network-based recommendation system that is able to incorporate multiple types of social relations or correlations between users. Thirdly, we propose a multipartite hypergraph ranking model for a generic full information-based recommender system that is able to handle various parities of information entities and their high-order relations. In addition, we extend the multipartite hypergraph ranking model to be able to respond to users' multi-objective recommendation requests and propose a novel multi-objective recommendation framework. We conduct comprehensive empirical experiments with a set of real-word public datasets in different domains such as movies (Movielens), music (Last.fm), e-Commerce products (Epinions) and local business (Yelp) to test the proposed graph ranking-based recommender systems. The results demonstrate that our models can generally achieve significant improvement compared to existing approaches in terms of recommendation success rate and accuracy. By these empirical experiments, we can conclude that the proposed graph ranking models are able to handle well the indicated four key challenges of recommender systems in the current era. This work is hence of both theoretical and practical significances in the field of both graph ranking and recommender systems

Popperian Falsificationism in IS: Major Confusions and Harmful Influences

The current relationship between Popper’s philosophy of science and Information Systems (IS) is complex and often confused. On the one hand, many influential members of the IS community claim that much IS research follows Popper’s falsificationism. On the other hand, many assumptions underlying Popper’s falsificationism, including the nature of theories as exceptionless laws rejected by a singular unsupportive observation are inappropriate and misleading. Moreover, Popper also rejected all inductive inferences and inductive methods as unscientific which, alas, has led some influential IS scholars to dismiss inductive inferences in major IS methodologies. Such Popperian advice is harmful as virtually all statistical or qualitative IS research relies on inductive inferences – and there is nothing wrong with that. Finally, we offer a solution for how to deal with the scientific significance of the problem of induction. This solution is inductive fallibilism. This means recognizing that theories, rather than always being held as true or false simply, often contain varying inductive supportive and unsupportive evidence

Mobile Services in Hubei: Adoption Model and Empirical Analysis

Mobile Commerce has developed rapidly in China with the characters of ubiquity, location relevance, convenience and personalization. The researches on technology, value chain, business models, user adoption have become a hot topic among academics. Based on the classical Davis’ TAM theory and the expansions of it, and the predecessors\u27 research on perceived enjoyment and perceived cost, this study builds an adoption model of Mobile value-added services in Hubei Province. In the variety of individual mobile value-added business, four most commonly used services are extracted in this study ,including Mobile Instant Message, Multimedia Messaging Service, WAP Web browse and Multi-media Downloads to represent the overall situation. According to the result of empirical analysis based on valid data of questionnaires, perceived enjoyment and perceived cost are the most influential factors. Six of the seven hypotheses in this study are verified

What impacts the helpfulness of online multidimensional reviews? A perspective from cross-attribute rating and ranking Inconsistency

This paper proposes investigations of the effects of information inconsistency, particularly ranking inconsistency, on the review helpfulness in a multidimensional rating system, based on information diagnosticity and attribution theory. The insight findings of this paper are: (a) The product cross-attribute dispersion has a significant negative impact on review helpfulness, while the overall attribute ranking consistency and the ranking consistency of the product’s best prominent attribute positively impact review helpfulness. (b) The product cross-attribute dispersion negatively impacts the review helpfulness for non-luxury products but it positively impacts that for luxury products, while the cross-attribute rating difference of a single review positively impacts it helpfulness only if the product is non-luxury. (c) The overall attribute ranking consistency significantly impacts the review helpfulness only for luxury products, whereas the ranking consistency of the product\u27s best and worst prominent attributes impact the review helpfulness only for non-luxury products

Dismantling the Black Box: Understanding Consumers\u27 Motivations for the Usage of Live Streaming Shopping Platform

Capturing consumers’ motivations for using the live streaming shopping platform (LSSP) can help guide the optimization of the platform and enhance shopping experience of consumers while watching live videos. Previous studies on user motivation typically explored technical and psychological antecedents of usage by considering the platform holistically. However, this black-box like treatment to the platform blurs the finer-grained details of consumer usage. This study takes a micro-level approach, disassembling the LSSP into 13 representative design features, and refines nine user motivations based on the uses and gratifications theory. Through collecting 237 questionnaires and employing regression analysis, we reveal the nuanced relationship between platform design features and consumer motivations. Our findings show that different design features are driven by distinct motivations, diverging from overall LSSP usage motivations. This research broadens the scope of LSSP studies, improves platform functionality, and offers practical insights for service providers

An Empirical Study on Factors Affecting Continuance Intention of Using Yu’e Bao

The success of Internet investment products such as Yu’e Bao directly relies on users\u27 continuous participation. However, few researches have examined Internet investment products from the context of post-adoption. The purpose of this paper is to study continuance usage intention toward Yu’e Bao, a prevalent Internet investment product. The dual-process model (DPM) has been used in several studies of information systems continuance. We have extended the DPM by examining the impact of some critical factors that directly influence continuance usage intention including expected earnings, capital liquidity and perceived risk. We used structural equation modelling to validate the proposed model and hypotheses. We discover that perceived usefulness, perceived enjoyment, satisfaction, loyalty, habit, expected earnings and capital liquidity positively influence continuance intention. Perceived risk has a negative effect on continuance usage intention. Furthermore, the results also demonstrate that expected earnings and capital liquidity have positive impacts on users’ perceived risk. The capital liquidity has negative influence on expected earnings. The results provide important implications for research and practice in the field of information systems and finance

A Wireless Image Transmission System Based on Visible Light Communication

According to the characteristics of visible light communication (VLC), inverse pulse position modulation (IPPM) has been adopted for the LED used as a lighting source and communication part in the VLC system. The practical need of the visible light communication can be satisfy as the IPPM has higher average transmit power and lower slot error rate. IPPM coding implementation is relatively simple, while the decoding synchronization is difficult to achieve

Additional file 1 of COSMIC-based mutation database enhances identification efficiency of HLA-I immunopeptidome

Additional file 1: Figure S1. A Whole picture of HLA-A/B western blot. B. Scatter plot experimental RT and predicted RT for PSM from 3 biological replicates of HepG2 cell line. C. Overlap of 9 a.a identified peptides for three biological replicates from HepG2 cells using Uniprot Human database. Figure S2. A Log2 intensity for binding peptides predicted by NetMHCpan, ranked by peptide length. B HLA-I immunopeptides main binding motifs by Gibbs cluster, when cluster number = 1. C HLA-I immunopeptides main binding motifs by Gibbs cluster, when cluster number = 3. D Scatter plot shows the proportion of P2 and P3 amino acids in the Gibbs clustered peptides and NetMHCpan HLA-A0201 data set. E Scatter plot shows the proportion of P2 and P3 amino acids in the Gibbs clustered peptides and NetMHCpan HLA-A2402 data set. Figure S3. A Tumor tissue distribution of COSMIC-reported somatic mutations in the identified mutant peptides using COSMIC-based database. B The proportion of equal weight peptides to unique peptides HepG2 WES-based or COSMIC-based database. Figure S4. A MS2 spectrum of identified binder mutant peptides using COSMIC-based database. B MS2 spectrum of identified non-binder mutant peptides using COSMIC-based database. Table S1. HLA-I peptidome identification from UniProt Human databas

Table_5_Comprehensive analysis and immune landscape of chemokines- and chemokine receptors-based signature in hepatocellular carcinoma.xlsx

BackgroundDespite encouraging results from immunotherapy combined with targeted therapy for hepatocellular carcinoma (HCC), the prognosis remains poor. Chemokines and their receptors are an essential component in the development of HCC, but their significance in HCC have not yet been fully elucidated. We aimed to establish chemokine-related prognostic signature and investigate the association between the genes and tumor immune microenvironment (TIME).Methods342 HCC patients have screened from the TCGA cohort. A prognostic signature was developed using least absolute shrinkage and selection operator regression and Cox proportional risk regression analysis. External validation was performed using the LIHC-JP cohort deployed from the ICGC database. Single-cell RNA sequencing (scRNA-seq) data from the GEO database. Two nomograms were developed to estimate the outcome of HCC patients. RT-qPCR was used to validate the differences in the expression of genes contained in the signature.ResultsThe prognostic signature containing two chemokines-(CCL14, CCL20) and one chemokine receptor-(CCR3) was successfully established. The HCC patients were stratified into high- and low-risk groups according to their median risk scores. We found that patients in the low-risk group had better outcomes than those in the high-risk group. The results of univariate and multivariate Cox regression analyses suggested that this prognostic signature could be considered an independent risk factor for the outcome of HCC patients. We discovered significant differences in the infiltration of various immune cell subtypes, tumor mutation burden, biological pathways, the expression of immune activation or suppression genes, and the sensitivity of different groups to chemotherapy agents and small molecule-targeted drugs in the high- and low-risk groups. Subsequently, single-cell analysis results showed that the higher expression of CCL20 was associated with HCC metastasis. The RT-qPCR results demonstrated remarkable discrepancies in the expression of CCL14, CCL20, and CCR3 between HCC and its paired adjacent non-tumor tissues.ConclusionIn this study, a novel prognostic biomarker explored in depth the association between the prognostic model and TIME was developed and verified. These results may be applied in the future to improve the efficacy of immunotherapy or targeted therapy for HCC.</p

Image_8_Comprehensive analysis and immune landscape of chemokines- and chemokine receptors-based signature in hepatocellular carcinoma.tif

BackgroundDespite encouraging results from immunotherapy combined with targeted therapy for hepatocellular carcinoma (HCC), the prognosis remains poor. Chemokines and their receptors are an essential component in the development of HCC, but their significance in HCC have not yet been fully elucidated. We aimed to establish chemokine-related prognostic signature and investigate the association between the genes and tumor immune microenvironment (TIME).Methods342 HCC patients have screened from the TCGA cohort. A prognostic signature was developed using least absolute shrinkage and selection operator regression and Cox proportional risk regression analysis. External validation was performed using the LIHC-JP cohort deployed from the ICGC database. Single-cell RNA sequencing (scRNA-seq) data from the GEO database. Two nomograms were developed to estimate the outcome of HCC patients. RT-qPCR was used to validate the differences in the expression of genes contained in the signature.ResultsThe prognostic signature containing two chemokines-(CCL14, CCL20) and one chemokine receptor-(CCR3) was successfully established. The HCC patients were stratified into high- and low-risk groups according to their median risk scores. We found that patients in the low-risk group had better outcomes than those in the high-risk group. The results of univariate and multivariate Cox regression analyses suggested that this prognostic signature could be considered an independent risk factor for the outcome of HCC patients. We discovered significant differences in the infiltration of various immune cell subtypes, tumor mutation burden, biological pathways, the expression of immune activation or suppression genes, and the sensitivity of different groups to chemotherapy agents and small molecule-targeted drugs in the high- and low-risk groups. Subsequently, single-cell analysis results showed that the higher expression of CCL20 was associated with HCC metastasis. The RT-qPCR results demonstrated remarkable discrepancies in the expression of CCL14, CCL20, and CCR3 between HCC and its paired adjacent non-tumor tissues.ConclusionIn this study, a novel prognostic biomarker explored in depth the association between the prognostic model and TIME was developed and verified. These results may be applied in the future to improve the efficacy of immunotherapy or targeted therapy for HCC.</p