Application of Nonparametric Techniques to Collaborative Recommender Systems

The introduction of the World Wide Web dramatically impacted our fundamental notion of information sharing, providing unparalleled awareness of both the power of information access and the penalty of information overload. Today’s research on Semantic Web techniques focuses on the next step, a Service Oriented Architecture supporting automated sharing of services as well as data. Personalized service/source recommendation tools, utilizing user preference data, would be extremely valuable in tailoring information access to the user. Much can be learned from the Recommender community about incorporating preference data into the retrieval process. However, it is critical that rigorous statistical techniques be maintained in combining results across data and service sources that are not under the control of a single developer. In this paper we explore the extension of nonparametric techniques to the development of Collaborative Recommenders and its impact on establishing a generalized recommendation service within a Service Oriented Architecture

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

NoTube – making TV a medium for personalized interaction

In this paper, we introduce NoTube’s vision on deploying semantics in interactive TV context in order to contextualize distributed applications and lift them to a new level of service that provides context-dependent and personalized selection of TV content. Additionally, lifting content consumption from a single-user activity to a community-based experience in a connected multi-device environment is central to the project. Main research questions relate to (1) data integration and enrichment - how to achieve unified and simple access to dynamic, growing and distributed multimedia content of diverse formats? (2) user and context modeling - what is an appropriate framework for context modeling, incorporating task-, domain and device-specific viewpoints? (3) context-aware discovery of resources - how could rather fuzzy matchmaking between potentially infinite contexts and available media resources be achieved? (4) collaborative architecture for TV content personalization - how can the combined information about data, context and user be put at disposal of both content providers and end-users in the view of creating extremely personalized services under controlled privacy and security policies? Thus, with the grand challenge in mind - to put the TV viewer back in the driver's seat – we focus on TV content as a medium for personalized interaction between people based on a service architecture that caters for a variety of content metadata, delivery channels and rendering devices

Is adaptation of e-advertising the way forward?

E-advertising is a multi-billion dollar industry that has shown exponential growth in the last few years. However, although the number of users accessing the Internet increases, users don’t respond positively to adverts. Adaptive e-advertising may be the key to ensuring effectiveness of the ads reaching their target. Moreover, social networks are good sources of user information and can be used to extract user behaviour and characteristics for presentation of personalized advertising. Here we present a two-sided study based on two questionnaires, one directed to Internet users and the other to businesses. Our study shows that businesses agree that personalized advertising is the best way for the future, to maximize effectiveness and profit. In addition, our results indicate that most Internet users would prefer adaptive advertisements. From this study, we can propose a new design for a system that meets both Internet users’ and businesses’ requirements

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

The Partial Evaluation Approach to Information Personalization

Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, we mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.Comment: Comprehensive overview of the PIPE model for personalizatio

The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research