Trust beyond reputation: A computational trust model based on stereotypes

Models of computational trust support users in taking decisions. They are commonly used to guide users' judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger's actions in absence of the knowledge of such behavioral history, we often use our "instinct"- essentially stereotypes developed from our past interactions with other "similar" persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger's profile. Since stereotypes are formed locally, recommendations stem from the trustor's own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Users' trust in information resources in the Web environment: a status report

This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users

How to Price Your House: Exploring Price Determinants of Online Accommodation Rental

Tourism and hospitality have emerged as one of the pioneering sectors of sharing economy. However, homeowners who lack knowledge background are confused with pricing. Traditional hotel pricing and rental pricing methods may be not suitable for online accommodation rental. Therefore, CRISP-DM, the data analysis framework, is used to solve this problem. The price prediction model is established via the house data from the Airbnb.com. Finally, 33 determinants closely related to the price are found, and the most important 10 determinants are sorted. The study also finds several interesting rules: (1) the basic situation of housing is an important determinant, (2) online rental houses with more convenient transaction conditions have higher price, (3) providing more facilities and services can increase the price, (4) some determinants in traditional hotel pricing are not efficient in sharing houses. These findings can help the homeowners to understand customers and improve their own house and pricing

Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation

Recommending users with preferred point-of-interests (POIs) has become an important task for location-based social networks, which facilitates users' urban exploration by helping them filter out unattractive locations. Although the influence of geographical neighborhood has been studied in the rating prediction task (i.e. regression), few work have exploited it to develop a ranking-oriented objective function to improve top-N item recommendations. To solve this task, we conduct a manual inspection on real-world datasets, and find that each individual's traits are likely to cluster around multiple centers. Hence, we propose a co-pairwise ranking model based on the assumption that users prefer to assign higher ranks to the POIs near previously rated ones. The proposed method can learn preference ordering from non-observed rating pairs, and thus can alleviate the sparsity problem of matrix factorization. Evaluation on two publicly available datasets shows that our method performs significantly better than state-of-the-art techniques for the top-N item recommendation task

Hotel online reviews: creating a multi-source aggregated index

Purpose This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems. Design/methodology/approach This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating. Findings Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment. Originality/value This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languagesinfo:eu-repo/semantics/acceptedVersio

Social-media monitoring for cold-start recommendations

Generating personalized movie recommendations to users is a problem that most commonly relies on user-movie ratings. These ratings are generally used either to understand the user preferences or to recommend movies that users with similar rating patterns have rated highly. However, movie recommenders are often subject to the Cold-Start problem: new movies have not been rated by anyone, so, they will not be recommended to anyone; likewise, the preferences of new users who have not rated any movie cannot be learned. In parallel, Social-Media platforms, such as Twitter, collect great amounts of user feedback on movies, as these are very popular nowadays. This thesis proposes to explore feedback shared on Twitter to predict the popularity of new movies and show how it can be used to tackle the Cold-Start problem. It also proposes, at a finer grain, to explore the reputation of directors and actors on IMDb to tackle the Cold-Start problem. To assess these aspects, a Reputation-enhanced Recommendation Algorithm is implemented and evaluated on a crawled IMDb dataset with previous user ratings of old movies,together with Twitter data crawled from January 2014 to March 2014, to recommend 60 movies affected by the Cold-Start problem. Twitter revealed to be a strong reputation predictor, and the Reputation-enhanced Recommendation Algorithm improved over several baseline methods. Additionally, the algorithm also proved to be useful when recommending movies in an extreme Cold-Start scenario, where both new movies and users are affected by the Cold-Start problem

Exploring the Impact of Information and Communication Technology (ICT) on Intermediation Market Structure in the Microfinance Industry

The microfinance industry provides financial services to the world’s poor in hopes of moving individuals and families out of poverty. In 2013 there were 4.7 million active microfinance borrowers in Africa. This represents a smaller percentage of the population compared to other regions of the world, indicating the potential for rapid growth of microfinance in Africa. However, microfinance is maturing, in part due to the adoption of information and communication technologies (ICTs). This research examines how ICTs are changing the microfinance industry given recent advancements in mobile banking, Internet usage, and connectivity. By examining the microfinance market structure, we determine that ICTs impact intermediation and market structure among various players in the microfinance industry. We use recent industry risk reports from 2011 through 2014 to inform our predictions of changes to the intermediation structure of the industry. We give particular attention to the impact of ICT on microfinance in Africa

Subject: Human Resource Management

Compiled by Susan LaCette.HumanResourceManagement.pdf: 5527 downloads, before Oct. 1, 2020