From the Publisher:
Written by the author of the best-selling HyperText & HyperMedia, this book provides an excellent guide to the methods of usability engineering. Special features: emphasizes cost-effective methods that will help developers improve their user interfaces immediately, shows you how to avoid the four most frequently listed reasons for delay in software projects, provides step-by-step information about which methods to use at various stages during the development life cycle, and offers information on the unique issues relating to informational usability. You do not need to have previous knowledge of usability to implement the methods provided, yet all of the latest research is covered.

Usability Engineering

How to Do Things With Words

The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes it freely available on the Web using Semantic Web and Linked Data technologies. The project extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things. The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. The DBpedia project maps Wikipedia infoboxes from 27 different language editions to a single shared ontology consisting of 320 classes and 1,650 properties. The mappings are created via a world-wide crowd-sourcing effort and enable knowledge from the different Wikipedia editions to be combined. The project publishes releases of all DBpedia knowledge bases for download and provides SPARQL query access to 14 out of the 111 language editions via a global network of local DBpedia chapters. In addition to the regular releases, the project maintains a live knowledge base which is updated whenever a page in Wikipedia changes. DBpedia sets 27 million RDF links pointing into over 30 external data sources and thus enables data from these sources to be used together with DBpedia data. Several hundred data sets on the Web publish RDF links pointing to DBpedia themselves and make DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud. In this system report, we give an overview of the DBpedia community project, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications.

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DBpedia - A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia

What Video Games Have to Teach Us about Learning and Literacy

Matrix Factorization Techniques for Recommender Systems

Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We show how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Offline experiments on two real-world datasets indicate that our extensions clearly improve objective performance when compared to state-of-the-art recommender algorithms and to a conventional Recurrent Neural Network.

Sequential User-based Recurrent Neural Network Recommendations

Tag clouds have become a popular visualization and navigation interface on the Web. Despite their popularity, little is known about tag cloud perception and performance with respect to different user goals. This paper presents results from a comparative study of several tag cloud layouts. The results show differences in task performance, leading to the conclusion that interface designers should carefully select the appropriate tag cloud layout according to the expected user goals. Furthermore, the analysis of eye tracking data provides insights into the visual exploration strategies of tag cloud users.

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Comparison of Tag Cloud Layouts: Task-Related Performance and Visual Exploration

A method and a set of supporting tools have been developed for an improved integration of user interface design with software engineering methods and tools. Animated user interfaces for database-oriented applications are generated from an extended data model and a new graphical technique for specifying dialogues. Based on views defined for the data model, an expert system uses explicit design rules derived from existing guidelines for producing the static layout of the user interface. A petri net based technique called dialogue nets is used for specifying the dynamic behaviour. Output is generated for an existing user interface management system. The approach supports rapid prototyping while using the advantages of standard software engineering methods.

Generating user interfaces from data models and dialogue net specifications

While the Semantic Web is rapidly filling up, appropriate tools for searching it are still at infancy In this paper we describe an approach that allows humans to access information contained in the Semantic Web according to its semantics and thus to leverage the specific characteristic of this Web To avoid the ambiguity of natural language queries, users only select already defined attributes organized in facets to build their search queries The facets are represented as nodes in a graph visualization and can be interactively added and removed by the users in order to produce individual search interfaces This provides the possibility to generate interfaces in arbitrary complexities and access arbitrary domains Even multiple and distantly connected facets can be integrated in the graph facilitating the access of information from different user-defined perspectives Challenges include massive amounts of data, massive semantic relations within the data, highly complex search queries and users' unfamiliarity with the Semantic Web

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Facet graphs: complex semantic querying made easy

Trust in a Recommender System (RS) is crucial for its overall success. However, it remains underexplored whether users trust personal recommendation sources (i.e. other humans) more than impersonal sources (i.e. conventional RS), and, if they do, whether the perceived quality of explanation provided account for the difference. We conducted an empirical study in which we compared these two sources of recommendations and explanations. Human advisors were asked to explain movies they recommended in short texts while the RS created explanations based on item similarity. Our experiment comprised two rounds of recommending. Over both rounds the quality of explanations provided by users was assessed higher than the quality of the system's explanations. Moreover, explanation quality significantly influenced perceived recommendation quality as well as trust in the recommendation source. Consequently, we suggest that RS should provide richer explanations in order to increase their perceived recommendation quality and trustworthiness.

Jürgen Ziegler

Papers

Sequential User-based Recurrent Neural Network Recommendations

Comparison of Tag Cloud Layouts: Task-Related Performance and Visual Exploration

Generating user interfaces from data models and dialogue net specifications

Facet graphs: complex semantic querying made easy

Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems