As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

/pdf/a-survey-of-collaborative-filtering-techniques-19zxbumsgq.pdf

A survey of collaborative filtering techniques

Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd preventing chronic diseases a vital investment to read.

Preventing Chronic Diseases: A Vital Investment

Cognitive engineering

コンピュータ・サイエンス : ACM computing surveys

Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. Dempster-Shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained from expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for Dempster-Shafer structures and provides examples of the implementation of these rules for discrete and interval-valued data.

/pdf/combination-of-evidence-in-dempster-shafer-theory-244gn8j3se.pdf

Combination of Evidence in Dempster-Shafer Theory

Spoken dialogue systems allow users to interact with computer-based applications such as databases and expert systems by using natural spoken language. The origins of spoken dialogue systems can be traced back to Artificial Intelligence research in the 1950s concerned with developing conversational interfaces. However, it is only within the last decade or so, with major advances in speech technology, that large-scale working systems have been developed and, in some cases, introduced into commercial environments. As a result many major telecommunications and software companies have become aware of the potential for spoken dialogue technology to provide solutions in newly developing areas such as computer-telephony integration. Voice portals, which provide a speech-based interface between a telephone user and Web-based services, are the most recent application of spoken dialogue technology. This article describes the main components of the technology---speech recognition, language understanding, dialogue management, communication with an external source such as a database, language generation, speech synthesis---and shows how these component technologies can be integrated into a spoken dialogue system. The article describes in detail the methods that have been adopted in some well-known dialogue systems, explores different system architectures, considers issues of specification, design, and evaluation, reviews some currently available dialogue development toolkits, and outlines prospects for future development.

/pdf/spoken-dialogue-technology-548u6qypik.pdf

Spoken Dialogue Technology

JMUI (Journal on Multimodal User Interfaces), Special issue “Best of affective computing and intelligent Guidelines for multimodal user interface design. support, human multi-modal information processing. characteristics to the design of a user-oriented and guidelines of multimodal interface design. Artifact lifecycle management, Consumer and user, Interfaces in Automated.Aug 2 Aug 7Los Angeles, CA, USAThursday, 6 August 2015 / HCI International 20152015.hci.international/thursday​CachedDefining and Optimizing User Interfaces Information Complexity for AI Design and Development of Multimodal Applications: A Vision on Key Issues and Traditional Heuristics and Industry Guidelines to Evaluate Multimodal Digital Artifacts

Guidelines for multimodal user interface design

This book provides a comprehensive introduction to the conversational interface, which is becoming the main mode of interaction with virtual personal assistants, smart devices, various types of wearable, and social robots. The book consists of four parts. Part I presents the background to conversational interfaces, examining past and present work on spoken language interaction with computers. Part II covers the various technologies that are required to build a conversational interface along with practical chapters and exercises using open source tools. Part III looks at interactions with smart devices, wearables, and robots, and discusses the role of emotion and personality in the conversational interface. Part IV examines methods for evaluating conversational interfaces and discusses future directions.

The Conversational Interface: Talking to Smart Devices

Semantics deals with the organization of meanings and the relations between sensory signs or symbols and what they denote or mean. Computational semantics performs a conceptualization of the world using computational processes for composing a meaning representation structure from available signs and their features present, for example, in words and sentences. Spoken language understanding (SLU) is the interpretation of signs conveyed by a speech signal. SLU and natural language understanding (NLU) share the goal of obtaining a conceptual representation of natural language sentences. Specific to SLU is the fact that signs to be used for interpretation are coded into signals along with other information such as speaker identity. Furthermore, spoken sentences often do not follow the grammar of a language; they exhibit self-corrections, hesitations, repetitions, and other irregular phenomena. SLU systems contain an automatic speech recognition (ASR) component and must be robust to noise due to the spontaneous nature of spoken language and the errors introduced by ASR. Moreover, ASR components output a stream of words with no structure information like punctuation and sentence boundaries. Therefore, SLU systems cannot rely on such markers and must perform text segmentation and understanding at the same time.

Spoken language understanding

What is a Spoken Dialogue System?- The Components of a Spoken Dialogue System- Describing Dialogue- Developing a Spoken Dialogue System: The Dialogue Engineering Lifecycle- Directed Dialogue Systems- Developing Directed Dialogue Systems using the CSLU Toolkit- Developing Directed Dialogue Systems using VoiceXML- Mixed-Initiative Dialogue Systems- Developing Mixed - Initiative Dialogue Systems using VoiceXML- Developing Mixed - Initiative Dialogue Systems using the CU Communicator System- Conversational Agents- Future Developments

Michael F. McTear

Papers

Spoken Dialogue Technology

Guidelines for multimodal user interface design

The Conversational Interface: Talking to Smart Devices

Spoken language understanding

Spoken Dialogue Technology: Toward the Conversational User Interface