Accuracy in Rating and Recommending Item Features

This paper discusses accuracy in processing ratings of and recommendations for item features. Such processing facilitates featurebased user navigation in recommender system interfaces. Item features, often in the form of tags, categories or meta-data, are becoming important hypertext components of recommender interfaces. Recommending features would help unfamiliar users navigate in such environments. This work explores techniques for improving feature recommendation accuracy. Conversely, it also examines possibilities for processing user ratings of features to improve recommendation of both features and items. This work’s illustrative implementation is a web portal for a museum collection that lets users browse, rate and receive recommendations for both artworks and interrelated topics about them. Accuracy measurements compare proposed techniques for processing feature ratings and recommending features. Resulting techniques recommend features with relative accuracy. Analysis indicates that processing ratings of either features or items does not improve accuracy of recommending the other

Be Your Own Curator with the CHIP Tour Wizard [html]

Web 2.0 enables increased access to the museum digital collection. More and more, users will spend time preparing their visits to the museums and reflecting on them after the visits. In this context, the CHIP (Cultural Heritage Information Personalization) project offers tools to the users to be their own curator, e.g. planning a personalized museum tour, discovering interesting artworks they want to see in a 'virtual' or a 'real' tour and quickly finding their ways in the museum. In this paper we present the new additions to the CHIP tools, which target the above functionality - a Web-based Tour Preparation Wizard and an export of a personalized tour to an interactive Mobile Guide used in the physical museum space. In addition, the user interactions during a real museum visit are stored and synchronized with the user model, which is maintained at the museum Web site

On the Role of User-generated Metadata in Audio Visual Collections

Recently, various crowdsourcing initiatives showed that targeted efforts of user communities result in massive amo

Be Your Own Curator with the CHIP Tour Wizard [pdf]

Web 2.0 enables increased access to the museum digital collection. More and more, users will spend time preparing their visits to the museums and reflecting on them after the visits. In this context, the CHIP (Cultural Heritage Information Personalization) project offers tools to the users to be their own curator, e.g. planning a personalized museum tour, discovering interesting artworks they want to see in a 'virtual' or a 'real' tour and quickly finding their ways in the museum. In this paper we present the new additions to the CHIP tools, which target the above functionality - a Web-based Tour Preparation Wizard and an export of a personalized tour to an interactive Mobile Guide used in the physical museum space. In addition, the user interactions during a real museum visit are stored and synchronized with the user model, which is maintained at the museum Web site

The design space of a configurable autocompletion component

Autocompletion is a commonly used interface feature in diverse applications. Semantic Web data has, on the one hand, the potential to provide new functionality by exploiting the semantics in the data used for generating autocompletion suggestions. Semantic Web applications, on the other hand, typically pose extra requirements on the semantic properties of the suggestions given. When the number of syntactic matches becomes too large, some means of selecting a semantically meaningful subset of suggestions to be presented to the user is needed. In this paper we identify a number of key design dimensions of autocompletion interface components. Our hypothesis is that a one-size-fits-all solution to autocompletion interface components does not exist, because different tasks and different data sets require interfaces corresponding to different points in our design space. We present a fully configurable architecture, which can be used to configure autocompletion components to the desired point in this design space. The architecture has been implemented as an open source software component that can be plugged into a variety of applications. We report on the results of a user evaluation that confirms this hypothesis, and describe the need to evaluate semantic autocompletion in a task and application-specific context

The design space of a configurable autocompletion component

Autocompletion is a commonly used interface feature in diverse applications. Semantic Web data has, on the one hand, the potential to provide new functionality by exploiting the semantics in the data used for generating autocompletion suggestions. Semantic Web applications, on the other hand, typically pose extra requirements on the semantic properties of the suggestions given. When the number of syntactic matches becomes too large, some means of selecting a semantically meaningful subset of suggestions to be presented to the user is needed. In this paper we identify a number of key design dimensions of autocompletion interface components. Our hypothesis is that a one-size-fits-all solution to autocompletion interface components does not exist, because different tasks and different data sets require interfaces corresponding to different points in our design space. We present a fully configurable architecture, which can be used to configure autocompletion components to the desired point in this design space. The architecture has been implemented as an open source software component that can be plugged into a variety of applications. We report on the results of a user evaluation that confirms this hypothesis, and describe the need to evaluate semantic autocompletion in a task and application-specific context