Improving the quality of the personalized electronic program guide

As Digital TV subscribers are offered more and more channels, it is becoming increasingly difficult for them to locate the right programme information at the right time. The personalized Electronic Programme Guide (pEPG) is one solution to this problem; it leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the limited availability of profiling information is a key limiting factor in such personalized recommender systems. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this article we address the sparsity problem in the Digital TV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovered by mining user profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale, state-of-the-art online systems—PTVPlus, a personalized TV listings portal and Físchlár, an online digital video library system

The effectiveness of web-based interventions designed to decrease alcohol consumption – a systematic review

OBJECTIVE To review the published literature on the effectiveness of web-based interventions designed to decrease consumption of alcohol and/or prevent alcohol abuse. METHOD Relevant articles published up to, and including, May 2006 were identified through electronic searches of Medline, PsycInfo, Embase, Cochrane Library, ASSIA, Web of Science and Science Direct. Reference lists of all articles identified for inclusion were checked for articles of relevance. An article was included if its stated or implied purpose was to evaluate a web-based intervention designed to decrease consumption of alcohol and/or to prevent alcohol abuse. Studies were reliably selected and quality-assessed, and data were independently extracted and interpreted by two authors. RESULTS Initial searches identified 191 articles of which 10 were eligible for inclusion. Of these, five provided a process evaluation only, with the remaining five providing some pre-to post-intervention measure of effectiveness. In general the percentage quality criteria met was relatively low and only one of the 10 articles selected was a randomized control trial. CONCLUSION The current review provides inconsistent evidence on the effectiveness of eIectronic screening and brief intervention (eSBI) for alcohol use. Process research suggests that web-based interventions are generally well received. However further controlled trials are needed to fully investigate their efficacy, to determine which elements are keys to outcome and to understand if different elements are required in order to engage low- and high-risk drinkers

Social Transparency through Recommendation Engines and its Challenges: Looking Beyond Privacy

Our knowledge society is quickly becoming a ‘transparent’ one. This transparency is acquired, among other means, by ’personalization’ or ‘profiling’: ICT tools gathering contextualized information about individuals in men–computers interactions. The paper begins with an overview of these ICT tools (behavioral targeting, recommendation engines, ‘personalization’ through social networking). Based on these developments the analysis focus a case study of developments in social network (Facebook) and the trade-offs between ‘personalization’ and privacy constrains. A deeper analysis will reveal unexpected challenges and the need to overcome the privacy paradigm. Finally a draft of possible normative solutions will be depicted, grounded in new forms of individual rights.Recommendation Engines, Profiling, Privacy, ‘Sui Generis’ Copyright

You can't see what you can't see: Experimental evidence for how much relevant information may be missed due to Google's Web search personalisation

The influence of Web search personalisation on professional knowledge work is an understudied area. Here we investigate how public sector officials self-assess their dependency on the Google Web search engine, whether they are aware of the potential impact of algorithmic biases on their ability to retrieve all relevant information, and how much relevant information may actually be missed due to Web search personalisation. We find that the majority of participants in our experimental study are neither aware that there is a potential problem nor do they have a strategy to mitigate the risk of missing relevant information when performing online searches. Most significantly, we provide empirical evidence that up to 20% of relevant information may be missed due to Web search personalisation. This work has significant implications for Web research by public sector professionals, who should be provided with training about the potential algorithmic biases that may affect their judgments and decision making, as well as clear guidelines how to minimise the risk of missing relevant information.Comment: paper submitted to the 11th Intl. Conf. on Social Informatics; revision corrects error in interpretation of parameter Psi/p in RBO resulting from discrepancy between the documentation of the implementation in R (https://rdrr.io/bioc/gespeR/man/rbo.html) and the original definition (https://dl.acm.org/citation.cfm?id=1852106) as per 20/05/201