A theory of personalisation of appearance: quantitative evaluation of qualitatively derived data

Using qualitative data, Blom and Monk (2003) derived a theory of why people choose to personalise the appearance of their PCs and mobile phones. This paper describes some quantitative data that provide some support for, and some modifications to, the theory. Individuals having personal home pages were recruited to fill in a questionnaire that assesses dispositions to personalise and its effects. In addition the 82 home pages were inspected to assess the extent of personalisation. This correlated significantly with the dispositions 'Frequency of Use' and 'Knowledge of Personalisation'. The questionnaire items corresponding to effects were factor analysed. A four-factor solution suggested item groupings similar, but not identical, to those used in the theory. There are significant positive correlations between the extent of personalisation and cognitive effects and enduring emotional effects. The value of quantitative data for confirming and refining a qualitative theory is discussed.     

Personalizing 3D virtual fashion stores: Exploring modularity with a typology of atmospherics based on user input

This research develops a typology of atmospherics that contains user-identified modules and modular options for personalizing 3D virtual fashion stores. A content analysis of 46 focus group discussions (n = 170) was conducted to understand the user’s perspective for personalizing 3D virtual fashion store atmospherics. Based on three atmospheric categories (pathfinding assistant, environment, and the manner of product presentation), 17 modules and 207 modular options were identified for personalizing 3D virtual stores. This research pioneers the development of an atmospherics typology for personalizing 3D virtual shopping environments as a persuasive selling tool in the emerging field of 3D virtual reality (VR) retailing.     

Human-centric design personalization of 3D glasses frame in markerless augmented reality

This paper presents a virtual try-on system based on augmented reality for design personalization of facial accessory products. The system offers several novel functions that support real-time evaluation and modification of eyeglasses frame. 3D glasses model is embedded within video stream of the person who is wearing the glasses. Machine learning algorithms are developed for instantaneous tracking of facial features without use of markers. The tracking result enables continuously positioning of the glasses model on the user’s face while it is moving during the try-on process. In addition to color and texture, the user can instantly modify the glasses shape through simple semantic parameters. These functions not only facilitate evaluating products highly interactive with human users, but also engage them in the design process. This work has thus implemented the concept of human-centric design personalization.     

A semantic-expansion approach to personalized knowledge recommendation

The rapid propagation of the Internet and information technologies has changed the nature of many industries. Fast response and personalized recommendations have become natural trends for all businesses. This is particularly important for content-related products and services, such as consulting, news, and knowledge management in an organization. The digital nature of their products allows for more customized delivery over the Internet. To provide personalized services, however, a complete understanding of user profile and accurate recommendation are essential.In this paper, an Internet recommendation system that allows customized content to be suggested based on the user's browsing profile is developed. The method adopts a semantic-expansion approach to build the user profile by analyzing documents previously read by the person. Once the customer profile is constructed, personalized contents can be provided by the system. An empirical study using master theses in the National Central library in Taiwan shows that the semantic-expansion approach outperforms the traditional keyword approach in catching user interests. The proper usage of this technology can increase customer satisfaction.     

基于本体的个性化知识服务系统的构建

为用户提供有效的个性化知识服务是图书馆等机构信息服务发展方向之一.引入本体理论,构建了一个基于本体的个性化知识服务系统.系统利用本体对多领域、跨数据库的文献库进行统一描述和重构,在此基础上进一步提出了用户个性特征模型和个性化知识检索模型,使系统实现在多文献库间进行语义层次上的个性化检索,提高了知识服务的效率和质量.     

From MPEG-7 user interaction tools to hanging basket models: bridging the gap

The content–user gap is the difference between the limited range of content-relevant preferences that may be expressed using the MPEG-7 user interaction tools and the much wider range of metadata that may be represented using the MPEG-7 content tools. One approach for closing this gap is to make the user and content metadata isomorphic by using the existing MPEG-7 content tools to represent user (as well as content) metadata (Agius and Angelides 2006, 2007). Subsequently, user preferences may be specified for all content, without omission. Since there is a wealth of user preference and history metadata within the MPEG-7 user interaction tools that can usefully complement these specific content preferences, in this paper we develop a method by which all user and content metadata may be bridged.

基于Web数据挖掘的个性化搜索引擎研究综述

Web数据挖掘是将教据挖掘技术和理论应用于对WWW资源进行挖掘的一个新兴的研究领域.论述Web数据挖掘的发展现状、发展趋势以及将来可能的研究方向,并简单介绍个性化搜索引擎的一些情况,论述web教据挖掘在个性化搜索引擎中的应用.     

Personalized text snippet extraction using statistical language models

In knowledge discovery in a text database, extracting and returning a subset of information highly relevant to a user's query is a critical task. In a broader sense, this is essentially identification of certain personalized patterns that drives such applications as Web search engine construction, customized text summarization and automated question answering. A related problem of text snippet extraction has been previously studied in information retrieval. In these studies, common strategies for extracting and presenting text snippets to meet user needs either process document fragments that have been delimitated a priori or use a sliding window of a fixed size to highlight the results. In this work, we argue that text snippet extraction can be generalized if the user's intention is better utilized. It overcomes the rigidness of existing approaches by dynamically returning more flexible start-end positions of text snippets, which are also semantically more coherent. This is achieved by constructing and using statistical language models which effectively capture the commonalities between a document and the user intention. Experiments indicate that our proposed solutions provide effective personalized information extraction services.     

Personalized top-k skyline queries in high-dimensional space

As data of an unprecedented scale are becoming accessible, it becomes more and more important to help each user identify the ideal results of a manageable size. As such a mechanism, skyline queries have recently attracted a lot of attention for its intuitive query formulation. This intuitiveness, however, has a side effect of retrieving too many results, especially for high-dimensional data. This paper is to support personalized skyline queries as identifying “truly interesting” objects based on user-specific preference and retrieval size k. In particular, we abstract personalized skyline ranking as a dynamic search over skyline subspaces guided by user-specific preference. We then develop a novel algorithm navigating on a compressed structure itself, to reduce the storage overhead. Furthermore, we also develop novel techniques to interleave cube construction with navigation for some scenarios without a priori structure. Finally, we extend the proposed techniques for user-specific preferences including equivalence preference. Our extensive evaluation results validate the effectiveness and efficiency of the proposed algorithms on both real-life and synthetic data.     

Determinants of consumers’ attitudes toward mobile advertising: The mediating roles of intrinsic and extrinsic motivations

Mobile advertising is booming, due to the popularity of mobile devices. Drawing on motivation theory, this study examines the factors that influence consumers’ attitudes toward mobile advertising. The authors identify timeliness, localization, and personalization of the advertisement message as antecedents of extrinsic motivation, as well as consumer innovativeness and perceived enjoyment as antecedents of intrinsic motivation. Using structural equation modeling, the study analyzes the conceptual model with a sample of 218 mobile phone users. Both intrinsic and extrinsic motivations mediate the effects of the advertising message’s characteristics on mobile phone users’ attitudes toward mobile advertising.