Computing word-of-mouth trust relationships in social networks from Semantic Web and Web 2.0 data sources

Social networks can serve as both a rich source of new information and as a filter to identify the information most relevant to our specific needs. In this paper we present a methodology and algorithms that, by exploiting existing Semantic Web and Web2.0 data sources, help individuals identify who in their social network knows what, and who is the most trustworthy source of information on that topic. Our approach improves upon previous work in a number of ways, such as incorporating topic-specific rather than global trust metrics. This is achieved by generating topic experience profiles for each network member, based on data from Revyu and del.icio.us, to indicate who knows what. Identification of the most trustworthy sources is enabled by a rich trust model of information and recommendation seeking in social networks. Reviews and ratings created on Revyu provide source data for algorithms that generate topic expertise and person to person affinity metrics. Combining these metrics, we are implementing a user-oriented application for searching and automated ranking of information sources within social networks

How private is private information?:The ability to spot deception in an economic game

We provide experimental evidence on the ability to detect deceit in a buyer-seller game with asymmetric information. Sellers have private information about the buyer's valuation of a good and sometimes have incentives to mislead buyers. We examine if buyers can spot deception in face-to-face encounters. We vary (1) whether or not the buyer can interrogate the seller, and (2) the contextual richness of the situation. We find that the buyers' prediction accuracy is above chance levels, and that interrogation and contextual richness are important factors determining the accuracy. These results show that there are circumstances in which part of the information asymmetry is eliminated by people's ability to spot deception

Personalisation and recommender systems in digital libraries

Widespread use of the Internet has resulted in digital libraries that are increasingly used by diverse communities of users for diverse purposes and in which sharing and collaboration have become important social elements. As such libraries become commonplace, as their contents and services become more varied, and as their patrons become more experienced with computer technology, users will expect more sophisticated services from these libraries. A simple search function, normally an integral part of any digital library, increasingly leads to user frustration as user needs become more complex and as the volume of managed information increases. Proactive digital libraries, where the library evolves from being passive and untailored, are seen as offering great potential for addressing and overcoming these issues and include techniques such as personalisation and recommender systems. In this paper, following on from the DELOS/NSF Working Group on Personalisation and Recommender Systems for Digital Libraries, which met and reported during 2003, we present some background material on the scope of personalisation and recommender systems in digital libraries. We then outline the working group’s vision for the evolution of digital libraries and the role that personalisation and recommender systems will play, and we present a series of research challenges and specific recommendations and research priorities for the field

ICOPER Project - Deliverable 4.3 ISURE: Recommendations for extending effective reuse, embodied in the ICOPER CD&R

The purpose of this document is to capture the ideas and recommendations, within and beyond the ICOPER community, concerning the reuse of learning content, including appropriate methodologies as well as established strategies for remixing and repurposing reusable resources. The overall remit of this work focuses on describing the key issues that are related to extending effective reuse embodied in such materials. The objective of this investigation, is to support the reuse of learning content whilst considering how it could be originally created and then adapted with that ‘reuse’ in mind. In these circumstances a survey on effective reuse best practices can often provide an insight into the main challenges and benefits involved in the process of creating, remixing and repurposing what we are now designating as Reusable Learning Content (RLC). Several key issues are analysed in this report: Recommendations for extending effective reuse, building upon those described in the previous related deliverables 4.1 Content Development Methodologies and 4.2 Quality Control and Web 2.0 technologies. The findings of this current survey, however, provide further recommendations and strategies for using and developing this reusable learning content. In the spirit of ‘reuse’, this work also aims to serve as a foundation for the many different stakeholders and users within, and beyond, the ICOPER community who are interested in reusing learning resources. This report analyses a variety of information. Evidence has been gathered from a qualitative survey that has focused on the technical and pedagogical recommendations suggested by a Special Interest Group (SIG) on the most innovative practices with respect to new media content authors (for content authoring or modification) and course designers (for unit creation). This extended community includes a wider collection of OER specialists. This collected evidence, in the form of video and audio interviews, has also been represented as multimedia assets potentially helpful for learning and useful as learning content in the New Media Space (See section 4 for further details). Section 2 of this report introduces the concept of reusable learning content and reusability. Section 3 discusses an application created by the ICOPER community to enhance the opportunities for developing reusable content. Section 4 of this report provides an overview of the methodology used for the qualitative survey. Section 5 presents a summary of thematic findings. Section 6 highlights a list of recommendations for effective reuse of educational content, which were derived from thematic analysis described in Appendix A. Finally, section 7 summarises the key outcomes of this work

Transparent government, not transparent citizens: a report on privacy and transparency for the Cabinet Office

1. Privacy is extremely important to transparency. The political legitimacy of a transparency programme will depend crucially on its ability to retain public confidence. Privacy protection should therefore be embedded in any transparency programme, rather than bolted on as an afterthought. 2. Privacy and transparency are compatible, as long as the former is carefully protected and considered at every stage. 3. Under the current transparency regime, in which public data is specifically understood not to include personal data, most data releases will not raise privacy concerns. However, some will, especially as we move toward a more demand-driven scheme. 4. Discussion about deanonymisation has been driven largely by legal considerations, with a consequent neglect of the input of the technical community. 5. There are no complete legal or technical fixes to the deanonymisation problem. We should continue to anonymise sensitive data, being initially cautious about releasing such data under the Open Government Licence while we continue to take steps to manage and research the risks of deanonymisation. Further investigation to determine the level of risk would be very welcome. 6. There should be a focus on procedures to output an auditable debate trail. Transparency about transparency – metatransparency – is essential for preserving trust and confidence. Fourteen recommendations are made to address these conclusions

Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance