A dynamic multi-level collaborative filtering method for improved recommendations

One of the most used approaches for providing recommendations in various online environments such as e-commerce is collaborative filtering. Although, this is a simple method for recommending items or services, accuracy and quality problems still exist. Thus, we propose a dynamic multi-level collaborative filtering method that improves the quality of the recommendations. The proposed method is based on positive and negative adjustments and can be used in different domains that utilize collaborative filtering to increase the quality of the user experience. Furthermore, the effectiveness of the proposed method is shown by providing an extensive experimental evaluation based on three real datasets and by comparisons to alternative methods

Trust and Distrust in Big Data Recommendation Agents

Big data technology allows for managing data from a variety of sources, in large amounts, and at a higher velocity than before, impacting several traditional systems, including recommendation agents. Along with these improvements, there are concerns about trust and distrust in RA recommendations. Much prior work on trust has been done in IS, but only a few have examined trust and distrust in the context of big data and analytics. In this vein, the purpose of this study is to study the eight antecedents of trust and distrust in recommendation agents’ cues in the context of the Big Data ecosystem using an experiment. Our study contributes to the literature by integrating big data and recommendation agent IT artifacts, expanding trust and distrust theory in the context of a big data ecosystem, and incorporating the constructs of algorithm innovativeness and process transparency

روشی نوین برای محاسبه اعتماد در شبکه‌های اجتماعی موبایلی

شبکه‌های اجتماعی موبایلی موجب تسهیل ارتباطات از طریق موبایل می‌شوند که کاربران این شبکه‌ها از موبایل به‌منظور دسترسی، اشتراک و توزیع اطلاعات استفاده می‌کنند. با افزایش روزافزون کاربران در شبکه‌های اجتماعی، حجم زیادی از اطلاعات به اشتراک گذاشته می‌شود که مشکلاتی ازجمله انتشار مطالب نادرست و شایعات دروغ را نیز به دنبال دارد. در این زمینه قوی‌ترین عامل برای سنجش صحت اطلاعات، استفاده از اعتبار هر کاربر به‌عنوان منبع توزیع اطلاعات است. اعتبار هر کاربر به‌عنوان منبع پخش اطلاعات می‌تواند بر اساس اعتماد دیگر کاربران به آن کاربر محاسبه شود. با توجه به ذهنی و ادراکی بودن مفهوم اعتماد، نگاشت اعتماد به یک مدل محاسباتی یکی از مسائل مهم در سیستم‌های محاسباتی شبکه‌های اجتماعی است. ازجمله پیچیدگی‌های فرآیند محاسبه اعتماد در این شبکه‌ها توجه به این موضوع است که در شبکه‌های اجتماعی، اجتماعات گوناگونی وجود داشته که همه کاربران آن‌ها به‌صورت مستقیم به یکدیگر متصل نمی‌باشند. در این مقاله با استفاده از ویژگی‌های کاربران در شبکه‌های اجتماعی، روشی منطبق بر منطق فازی برای دسته‌بندی کاربران پیشنهادشده است که اعتماد بین کاربران واقع در یک دسته با استفاده از مدل پیشنهادی محاسبه می‌شود. هم‌چنین با استفاده از فرآیندهای ترکیب، انتقال و اجتماع اعتمادها، اعتماد بین کاربرانی که به‌صورت مستقیم به یکدیگر متصل نیستند نیز بدست می‌آید. بررسی نتایج بیانگر این مسئله است که روش پیشنهادشده اعتماد افراد را در یک شبکه با دقت قابل قبولی معین می‌سازد

A Trust-based Recommender System over Arbitrarily Partitioned Data with Privacy

Recommender systems are effective mechanisms for recommendations about what to watch, read, or taste based on user ratings about experienced products or services. To achieve higher quality recommendations, e-commerce parties may prefer to collaborate over partitioned data. Due to privacy issues, they might hesitate to work in pairs and some solutions motivate them to collaborate. This study examines how to estimate trust-based predictions on arbitrarily partitioned data in which two parties have ratings for similar sets of customers and items. A privacy- preserving scheme is proposed, and it is justified that it efficiently offers trust-based predictions on partitioned data while preserving privacy

A knowledge graph-supported information fusion approach for multi-faceted conceptual modelling

It has become progressively more evident that a single data source is unable to comprehensively capture the variability of a multi-faceted concept, such as product design, driving behaviour or human trust, which has diverse semantic orientations. Therefore, multi-faceted conceptual modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is frequently applied to cope with the high dimensionality and data heterogeneity. The consideration of intra-facets relationships is also indispensable. In this context, a knowledge graph (KG), which can aggregate the relationships of multiple aspects by semantic associations, was exploited to facilitate the multi-faceted conceptual modelling based on heterogeneous and semantic-rich data. Firstly, rules of fault mechanism are extracted from the existing domain knowledge repository, and node attributes are extracted from multi-sourced data. Through abstraction and tokenisation of existing knowledge repository and concept-centric data, rules of fault mechanism were symbolised and integrated with the node attributes, which served as the entities for the concept-centric knowledge graph (CKG). Subsequently, the transformation of process data to a stack of temporal graphs was conducted under the CKG backbone. Lastly, the graph convolutional network (GCN) model was applied to extract temporal and attribute correlation features from the graphs, and a temporal convolution network (TCN) was built for conceptual modelling using these features. The effectiveness of the proposed approach and the close synergy between the KG-supported approach and multi-faceted conceptual modelling is demonstrated and substantiated in a case study using real-world data

Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice

Factors Leading to Organizational Success Despite Low Trust in Leadership

Generally, research results have suggested that employees’ lack of trust in leadership typically results in poor performance. The purpose of this research was to explore the factors that contributed to the federal government’s organizational success despite organization employees’ low interpersonal trust/distrust in the agency’s leadership. This change in the theory of trust was identified as a gap in literature; the change in the traditional knowledge of trust merits this specific problem to be explored and analyzed for further understanding. The conceptual framework was composed of trust, distrust, and employee-leader interpersonal trust/distrust theory. A qualitative exploratory case study was used along with the critical incident technique to collect, analyze and report data results for 77 critical incidents. Semistructured, in-depth interviews with open-ended questions were used to explore 20 federal employees’ experiences and understand this phenomenon. The data collected in this research extended the existing body of knowledge of trust, distrust and offered insight into factors that influence federal employees to perform positively despite experiencing low trust or distrust in leadership. The study results indicated that 90% of participants identified mission as a factor that influenced their positive performance, while 85% of participants identified personality as a second factor that influenced them to perform positively despite experiencing low interpersonal trust in leadership. The results of this research extend knowledge to the management field professionals. The results may affect change in leadership’s awareness and behavior within the federal government, leading to a social change

Novel Directions for Multiagent Trust Modeling in Online Social Networks

This thesis presents two works with the shared goal of improving the capacity of multiagent trust modeling to be applied to social networks. The first demonstrates how analyzing the responses to content on a discussion forum can be used to detect certain types of undesirable behaviour. This technique can be used to extract quantified representations of the impact agents are having on the community, a critical component for trust modeling. The second work expands on the technique of multi-faceted trust modeling, determining whether a clustering step designed to group agents by similarity can improve the performance of trust link predictors. Specifically, we hypothesize that learning a distinct model for each cluster of similar users will result in more personalized, and therefore more accurate, predictions. Online social networks have exploded in popularity over the course of the last decade, becoming a central source of information and entertainment for millions of users. This radical democratization of the flow of information, while purporting many benefits, also raises a raft of new issues. These networks have proven to be a potent medium for the spread of misinformation and rumors, may contribute to the radicalization of communities, and are vulnerable to deliberate manipulation by bad actors. In this thesis, our primary aim is to examine content recommendation on social media through the lens of trust modeling. The central supposition along this path is that the behaviors of content creators and the consumers of their content can be fit into the trust modeling framework, supporting recommendations of content from creators who not only are popular, but have the support of trustworthy users and are trustworthy themselves. This research direction shows promise for tackling many of the issues we've mentioned. Our works show that a machine learning model can predict certain types of anti-social behaviour in a discussion starting comment solely on the basis of analyzing replies to that comment with accuracy in the range of 70% to 80%. Further, we show that a clustering based approach to personalization for multi-faceted trust models can increase accuracy on a down-stream trust aware item recommendation task, evaluated on a large data set of Yelp users

Improving accuracy of recommender systems through triadic closure

The exponential growth of social media services led to the information overload problem which information filtering and recommender systems deal by exploiting various techniques. One popular technique for making recommendations is based on trust statements between users in a social network. Yet explicit trust statements are usually very sparse leading to the need for expanding the trust networks by inferring new trust relationships. Existing methods exploit the propagation property of trust to expand the existing trust networks; however, their performance is strongly affected by the density of the trust network. Nevertheless, the utilisation of existing trust networks can model the users’ relationships, enabling the inference of new connections. The current study advances the existing methods and techniques on developing a trust-based recommender system proposing a novel method to infer trust relationships and to achieve a fully-expanded trust network. In other words, the current study proposes a novel, effective and efficient approach to deal with the information overload by expanding existing trust networks so as to increase accuracy in recommendation systems. More specifically, this study proposes a novel method to infer trust relationships, called TriadicClosure. The method is based on the homophily phenomenon of social networks and, more specifically, on the triadic closure mechanism, which is a fundamental mechanism of link formation in social networks via which communities emerge naturally, especially when the network is very sparse. Additionally, a method called JaccardCoefficient is proposed to calculate the trust weight of the inferred relationships based on the Jaccard Cofficient similarity measure. Both the proposed methods exploit structural information of the trust graph to infer and calculate the trust value. Experimental results on real-world datasets demonstrate that the TriadicClosure method outperforms the existing state-of-the-art methods by substantially improving prediction accuracy and coverage of recommendations. Moreover, the method improves the performance of the examined state-of-the-art methods in terms of accuracy and coverage when combined with them. On the other hand, the JaccardCoefficient method for calculating the weight of the inferred trust relationships did not produce stable results, with the majority showing negative impact on the performance, for both accuracy and coverage