Impact of personalized recommendation and social comparison on learning behaviours and outcomes

ELearning suffers from the lack of face-to-face interaction and can deprive learners from the benefits of social interaction and comparison. In this paper we present the results of a study conducted for the impact of social comparison. The study was conducted by collecting students’ engagement with an eLearning tool, the attendance, and grades scored by students at specific milestones and presented these metrics to students as feedback using Kiviat charts. The charts were complemented with appropriate recommendations to allow them to adapt their study strategy and behaviour. The study spanned over 4 semesters (2 with and 2 without the Kiviats) and the results were analysed using paired T tests to test the pre and post results on topics covered by the eLearning tool. Survey questionnaires were also administered at the end for qualitative analysis. The results indicated that the Kiviat feedback with recommendation had positive impact on learning outcomes and attitudes.

Recommendation techniques based on off-line data processing: a multifaceted survey

Recommendations based on off-line data processing has attracted increasing attention from both research communities and IT industries. The recommendation techniques could be used to explore huge volumes of data, identify the items that users probably like, and translate the research results into real-world applications, etc. This paper surveys the recent progress in the research of recommendations based on off-line data processing, with emphasis on new techniques (such as context-based recommendation, temporal recommendation), and new features (such as serendipitous recommendation). Finally, we outline some existing challenges for future research.

Can analyst coverage reduce the incidence of fraud? Evidence from China

We investigate the impact of security analyst coverage on the incidence of corporate financial fraud in China. After controlling for the endogeneity between analyst following and fraud, we find that financial analyst coverage cannot significantly influence the incidence of fraud. The empirical findings suggest that financial analysts do not serve as external monitors to managers and large shareholders in China. © 2014 Taylor & Francis.

Modeling dual role preferences for trust-aware recommendation

Unlike in general recommendation scenarios where a user has only a single role, users in trust rating network, e.g. Epinions, are associated with two different roles simultaneously: as a truster and as a trustee. With different roles, users can show distinct preferences for rating items, which the previous approaches do not involve. Moreover, based on explicit single links between two users, existing methods can not capture the implicit correlation between two users who are similar but not socially connected. In this paper, we propose to learn dual role preferences (truster/trustee-specific preferences) for trust-aware recommendation by modeling explicit interactions (e.g., rating and trust) and implicit interactions. In particular, local links structure of trust network are exploited as two regularization terms to capture the implicit user correlation, in terms of truster/trustee-specific preferences. Using a real-world and open dataset, we conduct a comprehensive experimental study to investigate the performance of the proposed model, RoRec. The results show that RoRec outperforms other trust-aware recommendation approaches, in terms of prediction accuracy. Copyright 2014 ACM.

Deferentially private tagging recommendation based on topic model

Tagging recommender system allows Internet users to annotate resources with personalized tags and provides users the freedom to obtain recommendations. However, It is usually confronted with serious privacy concerns, because adversaries may re-identify a user and her/his sensitive tags with only a little background information. This paper proposes a privacy preserving tagging release algorithm, PriTop, which is designed to protect users under the notion of differential privacy. The proposed PriTop algorithm includes three privacy preserving operations: Private Topic Model Generation structures the uncontrolled tags, Private Weight Perturbation adds Laplace noise into the weights to hide the numbers of tags; while Private Tag Selection finally finds the most suitable replacement tags for the original tags. We present extensive experimental results on four real world datasets and results suggest the proposed PriTop algorithm can successfully retain the utility of the datasets while preserving privacy. © 2014 Springer International Publishing.

Preference Networks: probabilistic models for recommendation systems

Abstract
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation. The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filtering into a single conditional
Markov random field. Once estimated, it serves as a probabilistic database that supports various useful queries such as rating prediction and top-N recommendation. To handle the challenging problem of learning large networks of users and items, we employ a simple but effective pseudo-likelihood with regularisation. Experiments on the movie rating data demonstrate the merits of the PN.

The use and abuse of 'community' and 'neighbourhood' within disability research: an exposé, clarification, and recommendation

Almost invariably in the disability literature, the terms 'neighbourhood' and 'community' are used as though they have some commonly understood meaning. They do not, and authors rarely offer a definition. This problem adds opacity to the literature describing people's living environment and the nature of their interaction with others living in the same area. This ambiguity becomes crucial to understanding when these terms are linked to other vague, but emotionally-charged words, such as 'inclusion' or 'integration'. This review presents some of the ways 'neighbourhood' and 'community' may be correctly employed. It also explores the theoretical basis for understanding how and why their use may be misleading. Finally, it is demonstrated that the assumed relevance of neighbourhood participation for life quality has been greatly exaggerated. We recommend that authors carefully define their use of these terms in order to facilitate understanding free from emotional bias.

Gender-diverse boards and properties of analyst earnings forecasts

Using a sample of 2,200 U.S. listed firm-year observations (2001-2007), this study shows a positive (negative) relation between gender diversity on corporate boards and analysts' earnings forecast accuracy (dispersion), after controlling for earnings quality, corporate governance, audit quality, stock price informativeness, and potential endogeneity. Our findings are important as they suggest that board diversity adds to the transparency and accuracy of financial reports such that earnings expectations are likely to be more accurate for these firms.

A graph-based model for context-aware recommendation using implicit feedback data

Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach.

A survey of recommendation techniques based on offline data processing

Recommendations based on offline data processing has attracted increasing attention from both research communities and IT industries. The recommendation techniques could be used to explore huge volumes of data, identify the items that users probably like, translate the research results into real-world applications and so on. This paper surveys the recent progress in the research of recommendations based on offline data processing, with emphasis on new techniques (such as temporal recommendation, graph-based recommendation and trust-based recommendation), new features (such as serendipitous recommendation) and new research issues (such as tag recommendation and group recommendation). We also provide an extensive review of evaluation measurements, benchmark data sets and available open source tools. Finally, we outline some existing challenges for future research.

Dimensions and metrics for evaluating recommendation systems

Recommendation systems support users and developers of various computer and software systems to overcome information overload, perform information discovery tasks, and approximate computation, among others. They have recently become popular and have attracted a wide variety of application scenarios ranging from business process modeling to source code manipulation. Due to this wide variety of application domains, different approaches and metrics have been adopted for their evaluation. In this chapter, we review a range of evaluation metrics and measures as well as some approaches used for evaluating recommendation systems. The metrics presented in this chapter are grouped under sixteen different dimensions, e.g., correctness, novelty, coverage. We review these metrics according to the dimensions to which they correspond. A brief overview of approaches to comprehensive evaluation using collections of recommendation system dimensions and associated metrics is presented. We also provide suggestions for key future research and practice directions.

Online video recommendation in sharing community

The creation of sharing communities has resulted in the astonishing increasing of digital videos, and their wide applications in the domains such as entertainment, online news broadcasting etc. The improvement of these applications relies on effective solutions for social user access to video data. This fact has driven the recent research interest in social recommendation in shared communities. Although certain effort has been put into video recommendation in shared communities, the contextual information on social users has not been well exploited for effective recommendation. In this paper, we propose an approach based on the content and social information of videos for the recommendation in sharing communities. Specifically, we first exploit a robust video cuboid signature together with the Earth Mover's Distance to capture the content relevance of videos. Then, we propose to identify the social relevance of clips using the set of users belonging to a video. We fuse the content relevance and social relevance to identify the relevant videos for recommendation. Following that, we propose a novel scheme called sub-community-based approximation together with a hash-based optimization for improving the efficiency of our solution. Finally, we propose an algorithm for efficiently maintaining the social updates in dynamic shared communities. The extensive experiments are conducted to prove the high effectiveness and efficiency of our proposed video recommendation approach.

Using GIS Network Analyst to Identify Suppliers for a Produce Co-op in Waterville, ME

The idea for organizing a cooperative market on Waterville Main Street was proposed by Aime Schwartz in the fall of 2008. The Co-op would entail an open market located on Main Street to provide fresh, local produce and crafts to town locals. Through shorter delivery distances and agreements with local farmers, the co-op theoretically will offer consumers lower prices on produce than can be found in conventional grocery stores, as well as an opportunity to support local agriculture. One of the tasks involved with organizing the Co-op is to source all of the produce from among the hundreds of farmers located in Maine. The purpose of this project is to show how Geographic Information System (GIS) tools can be used to help the Co-op and other businesses a) site nearby farms that carry desired produce and products, and b) determine which farms are closest to the business site. Using GIS for this purpose will make it easier and more efficient to source produce suppliers, and reduce the workload on business planners. GIS Network Analyst is a tool that provides network-based spatial analysis, and can be used in conjunction with traditional GIS technologies to determine not only the geometric distance between points, but also distance over existing networks (like roads). We used Network Analyst to find the closest produce suppliers to the Co-op for specific produce items, and compute how far they are over existing roads. This will enable business planners to source potential suppliers by distance before contacting individual farmers, allowing for more efficient use of their time and a faster planning process.

Feasibility analysis for biofuel production and investment recommendation

A FGV Projetos posiciona-se com destaque no desenvolvimento de projetos e estudos sobre biocombustíveis. Especialistas no assunto apontam para a viabilidade da produção de biocombustíveis em diversos países, e recomendam investimentos em toda a cadeia produtiva, específicos para cada localidade. Isso permitirá aos países que desejam reduzir a dependência por combustíveis fósseis, tais como o petróleo, a diversificação da matriz energética, o aumento do emprego no campo, e uma melhora do saldo comercial. Este documento apresenta síntese dos trabalhos realizados e em desenvolvimento, e tem como base os estudos de viabilidade para a produção de biocombustíveis de El Salvador e da República Dominicana.