Latent Space Model for Multi-Modal Social Data

With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.Comment: 12 pages, 7 figures, 2 table

An improved model for trust-aware recommender systems based on multi-faceted trust

As customers enjoy the convenience of online shopping today, they face the problem of selecting from hundreds of thousands of products. Recommender systems, which make recommendations by matching products to customers based on the features of the products and the purchasing history of customers, are increasingly being incorporated into e-commerce websites. Collaborative filtering is a major approach to design algorithms for these systems. Much research has been directed toward enhancing the performance of recommender systems by considering various psychological and behavioural factors affecting the behaviour of users, e.g. trust and emotion. While e-commerce firms are keen to exploit information on social trust available on social networks to improve their services, conventional trust-aware collaborative filtering does not consider the multi-facets of social trust. In this research, we assume that a consumer tends to trust different people for recommendations on different types of product. For example, a user trusts a certain reviewer on popular items but may not place as much trust on the same reviewer on unpopular items. Furthermore, this thesis postulates that if we, as online shoppers, choose to establish trust on an individual while we ourselves are reviewing certain products, we value this individual’s opinions on these products and we most likely will value his/her opinions on similar products in future. Based on the above assumptions, this thesis proposes a new collaborative filtering algorithm for deriving multi-faceted trust based on trust establishment time. Experimental results based on historical data from Epinions show that the new algorithm can perform better in terms of accuracy when compared with conventional algorithms

The impact of trust and power on knowledge sharing in design projects: some empirical evidence from the aerospace industry

It is acknowledged by aerospace engineers that relationships between partners are influenced by topics such as trust and that they enable or inhibit knowledge flow. This paper presents findings from interviews with engineers in the aerospace industry on how trust and power within supply chain teams impact knowledge sharing and integration. From a trust perspective, the results of the paper indicate that individually, engineers are aware of its importance but that there is little organisational awareness and consequently no framework or support exists for managing it. With regards to power, we show that there are positive as well as negative impacts on knowledge sharing to be considered

Fact Checking in Community Forums

Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information. Unfortunately, this information is not always factual. Thus, here we explore a new dimension in the context of cQA, which has been ignored so far: checking the veracity of answers to particular questions in cQA forums. As this is a new problem, we create a specialized dataset for it. We further propose a novel multi-faceted model, which captures information from the answer content (what is said and how), from the author profile (who says it), from the rest of the community forum (where it is said), and from external authoritative sources of information (external support). Evaluation results show a MAP value of 86.54, which is 21 points absolute above the baseline.Comment: AAAI-2018; Fact-Checking; Veracity; Community-Question Answering; Neural Networks; Distributed Representation

‘The teacher is here to ask for your help’: A story of schools, employers and networks.

This paper explores the development of the Jobs4Kids (J4K) campaign, a joint initiative of the SGR LLEN Employer Reference Group and the Beacon Foundation. Involving a three-year business plan, the J4K campaign aims to broker young people into employment in local jobs in the region. The campaign is the result of the intersection between an evolving project within the LLEN and the growth of an established program of the Beacon Foundation. The paper will use a Deleuzian lens to explore the ground shifts that have occurred in the process of forming this connection; I am concerned with the intersecting movements of different orders that have created a necessary transitory coordination. Within such a ‘rhizome’ there are only lines: dimensional lines of segmentarity and stratification and lines of flight as ‘the maximum dimension after which the multiplicity undergoes metamorphosis, changes in nature’ (Deleuze & Guattari 1987 p.21). My perspective of this metamorphosis is specifically focused on SGR LLEN; I close with a consideration of the possibilities of this change in nature for the continuing work of the LLEN

Devolved governance systems

This article assesses the extent, nature and outcomes of the recently devolved health service governance in the four countries which comprise the United Kingdom. This four-part configuration can be seen as a natural experiment in comparative governance which could therefore carry important lessons not only for the UK but in other countries too. While remaining under the aegis of the National Health Service, each constituent devolved administration has a developed a substantially different governance system. These systems reflect fundamental issues and priorities concerning the decentring of authority, the production and deployment of authority, the suite of incentives required and the preferred role of quasi-market mechanisms. The paper makes an evaluation of the strengths and weaknesses of each governance regime

Closing the loop: assisting archival appraisal and information retrieval in one sweep

In this article, we examine the similarities between the concept of appraisal, a process that takes place within the archives, and the concept of relevance judgement, a process fundamental to the evaluation of information retrieval systems. More specifically, we revisit selection criteria proposed as result of archival research, and work within the digital curation communities, and, compare them to relevance criteria as discussed within information retrieval's literature based discovery. We illustrate how closely these criteria relate to each other and discuss how understanding the relationships between the these disciplines could form a basis for proposing automated selection for archival processes and initiating multi-objective learning with respect to information retrieval

RECOMMENDING SERVICES IN A DIFFERNTIATED TRUST-BASED DECENTRALIZED USER MODELING SYSTEM

Trust and reputation mechanisms are often used in peer-to-peer networks, multi-agent systems and online communities for trust-based interactions among the users. Trust values are used to differentiate among members of the community as well as to recommend service providers. Although different users have different needs and expectations in different aspects of the service providers, traditional trust-based models do not use trust values on neighbors for judging different aspects of service providers. In this thesis, I use multi-faceted trust models for users connected in a network who are looking for suitable service providers according to their preferences. Each user has two sets of trust values: i) trust in different aspects of the quality of service providers, ii) trust in recommendations provided for these aspects. These trust models are used in a decentralized user modeling system where agents (representing users) have different preference weights in different criteria of service providers. My approach helps agents by recommending the best possible service provider for each agent according to its preferences. The approach is evaluated by conducting simulation on both small and large social networks. The results of the experiments illustrate that agents find better matches or more suitable service providers for themselves using my trust-based recommender system without the help of any central server. To the best of my knowledge this is the first system that uses multi-faceted trust values both in the qualities of service-providers and in other users’ ability to evaluate these qualities of service providers in a decentralized user modeling system