Using distributed objects to build the Stanford digital libraryInfobus

For digital libraries to thrive, the providers of information processing services must be able to evolve their systems autonomously. However, as the complexity of their offerings increases, software tools more sophisticated than existing Web facilities are needed. Distributed object technology may be the answer. The availability of high-volume, increasingly sophisticated information is making the need for metadata facilities more urgent. Traditional, library-based approaches break down when used in an advanced digital library. More modular mechanisms are needed, and the CORBA system is one approach. Digital libraries are affected at a deep technical level by the widely differing user traditions of Web users and library patrons. The challenge and opportunity of digital libraries will be the synthesis of these traditions. The authors set out to create a technical infrastructure to support the construction of digital libraries. In their view, a digital library comprises widely distributed resources that can be maintained autonomously by different organizations and will not require adherence to uniform interfaces     

Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries

As in the Web, the growing of information is the main problem of the academic digital libraries. Thus, similar tools could be applied in university digital libraries to facilitate the information access by the students and teachers. In [46] we presented a fuzzy linguistic recommender system to advice research resources in university digital libraries. The problem of this system is that the user profiles are provided directly by the own users and the process for acquiring user preferences is quite difficult because it requires too much user effort. In this paper we present a new fuzzy linguistic recommender system that facilitates the acquisition of the user preferences to characterize the user profiles. We allow users to provide their preferences by means of incomplete fuzzy linguistic preference relation. We include tools to manage incomplete information when the users express their preferences, and, in such a way, we show that the acquisition of the user profiles is improved.     

Multimedia information using mobile computers: accessing the digital campus and the digital library

Abstract

It is clear that the role of the information resource is changing. Major publishers have been slow to adapt to the emergence of a global digital medium, but there are now signs that a great deal of information will be delivered on-line, (although at present only about 25 databases account for 80% of usage in UK and optical publishing is still in its early stages). However, digital publishing on the Internet — with services for libraries such as just-in-time purchasing and delivery, for example — will be a driving force in creating the ‘global digital medium’. One issue that will become increasingly relevant is how the individual user accesses rich multimedia data in the most appropriate way. The ‘digital university campus’ and the ‘digital library’ are coming to be important concepts, with the aim that users of information services will receive information on-line supported by a ‘ubiquistructure’ of information technology. For the ‘digital campus’ this means that not only scholarly but also teaching activities are based on interactive access to information, and where not only the digital library but also the digital bookshop and the digital classroom are becoming possible with the development of 140Mb/s SuperJANET links. However, it is recognised that libraries will not be truly digital for the foreseeable future, and that libraries will maintain traditional and digital media side by side. In this paper, reporting on work at the University of Bristol's Educational Technology Service multimedia resources unit MRU, and the University of the West of England's Centre for Personal Information Management (in collaboration with Hewlett-Packard Research Laboratories and the University of Bristol's Centre for Communications Research), we look the ‘digital library’ and ‘digital campus’ from the perspective of the individual user and her information needs. We are particularly interested in the use of small, mobile computers as access points to the global digital medium. We suggest that, in an environment of change — where the traditional campus and the traditional library exist alongside the digital campus and digital library — the most appropriate form of access technology is based on ‘personal technology’ which allows a linking between digital information and traditional paper-based information.     

Experiments on user experiences with recommender interfaces

Recommender systems have been increasingly adopted as personalisation services in e-commerce. They facilitate users to locate items which they would be interested in viewing or purchasing. However, most studies have emphasised on the algorithm's performance, rather than on in-depth analysis of user experiences with the recommender interface. In this article, we report the results of two studies that compared two recommender interfaces: the organisation-based interface (where recommendations are presented in a category structure via the preference-based organisation method) and the standard ranked list (where recommendations are listed one after the other as ordered by their prediction scores).The first study focuses on evaluating users' eye-movement behaviour in these interfaces. With the help of an eye tracker, we found that the organisation interface (ORG) can significantly attract users' attentions to more recommended items. As a result, more users made product choices in that interface. The second, larger-scale, cross-cultural user survey further shows that the ORG performed significantly better in terms of enhancing users' perceived recommendation quality, perceived ease of use and perceived usefulness of the system. Hence, these empirical findings suggest that the change of recommender interface design can not only alter users' attention distribution, but also influence their subjective attitudes towards the system.     

Swallow: Resource and Tag Recommender System Based on Heat Diffusion Algorithm in Social Annotation Systems

Social annotation systems (SAS) allow users to annotate different online resources with keywords (tags). These systems help users in finding, organizing, and retrieving online resources to significantly provide collaborative semantic data to be potentially applied by recommender systems. Previous studies on SAS had been worked on tag recommendation. Recently, SAS‐based resource recommendation has received more attention by scholars. In the most of such systems, with respect to annotated tags, searched resources are recommended to user, and their recent behavior and click‐through is not taken into account. In the current study, to be able to design and implement a more precise recommender system, because of previous users' tagging data and users' current click‐through, it was attempted to work on the both resource (such as web pages, research papers, etc.) and tag recommendation problem. Moreover, by applying heat diffusion algorithm during the recommendation process, more diverse options would present to the user. After extracting data, such as users, tags, resources, and relations between them, the recommender system so called “Swallow” creates a graph‐based pattern from system log files. Eventually, following the active user path and observing heat conduction on the created pattern, user further goals are anticipated and recommended to him. Test results on SAS data set demonstrate that the proposed algorithm has improved the accuracy of former recommendation algorithms.     

Usage derived recommendations for a video digital library

We describe a minimalist methodology to develop usage-based recommender systems for multimedia digital libraries. A prototype recommender system based on this strategy was implemented for the Open Video Project, a digital library of videos that are freely available for download. Sequential patterns of video retrievals are extracted from the project's web download logs and analyzed to generate a network of video relationships. A spreading activation algorithm locates video recommendations by searching for associative paths connecting query-related videos. We evaluate the performance of the resulting system relative to an item-based collaborative filtering technique operating on user profiles extracted from the same log data.     

How should I explain? A comparison of different explanation types for recommender systems

Recommender systems help users locate possible items of interest more quickly by filtering and ranking them in a personalized way. Some of these systems provide the end user not only with such a personalized item list but also with an explanation which describes why a specific item is recommended and why the system supposes that the user will like it. Besides helping the user understand the output and rationale of the system, the provision of such explanations can also improve the general acceptance, perceived quality, or effectiveness of the system.In recent years, the question of how to automatically generate and present system-side explanations has attracted increased interest in research. Today some basic explanation facilities are already incorporated in e-commerce Web sites such as Amazon.com. In this work, we continue this line of recent research and address the question of how explanations can be communicated to the user in a more effective way.In particular, we present the results of a user study in which users of a recommender system were provided with different types of explanation. We experimented with 10 different explanation types and measured their effects in different dimensions. The explanation types used in the study include both known visualizations from the literature as well as two novel interfaces based on tag clouds. Our study reveals that the content-based tag cloud explanations are particularly helpful to increase the user-perceived level of transparency and to increase user satisfaction even though they demand higher cognitive effort from the user. Based on these insights and observations, we derive a set of possible guidelines for designing or selecting suitable explanations for recommender systems.     

Multistakeholder recommendation: Survey and research directions

Recommender systems provide personalized information access to users of Internet services from social networks to e-commerce to media and entertainment. As is appropriate for research in a field with a focus on personalization, academic studies of recommender systems have largely concentrated on optimizing for user experience when designing, implementing and evaluating their algorithms and systems. However, this concentration on the user has meant that the field has lacked a systematic exploration of other aspects of recommender system outcomes. A user-centric approach limits the ability to incorporate system objectives, such as fairness, balance, and profitability, and obscures concerns that might come from other stakeholders, such as the providers or sellers of items being recommended. Multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article outlines the multistakeholder perspective on recommendation, highlighting example research areas and discussing important issues, open questions, and prospective research directions.

     

Lessons learned from the design and evaluation of visual information-seeking systems

Designing information-seeking systems has become an increasingly complex task as today’s information spaces are rapidly growing in quantity, heterogeneity, and dimensionality. The challenge is to provide user interfaces that have a satisfying usability and user experience even for novice users. Although information visualization and interaction design offer solutions, many information-seeking systems such as online catalogs for libraries or web search engines continue to use outdated user-interface concepts developed decades ago. In this paper, we will present four principles that we identified as crucial for the successful design of a modern visual information-seeking system. These are (1) to support various ways of formulating an information need, (2) to integrate analytical and browsing-oriented ways of exploration, (3) to provide views on different dimensions of the information space, and (4) to make search a pleasurable experience. These design principles are based on our experience over a long period in the user-centered design and evaluation of visual information-seeking systems. Accordingly, we will showcase individual designs from our own work of the past 10 years to illustrate each principle and hence narrow the gap between the scientific discussion and the designing practitioner that has often hindered research ideas from becoming reality. However, most of the times search is only one part of a higher level user activity (e.g. writing a paper). Thus future research should focus on the challenges when regarding search in such a broader context. We will use the final two chapters to point out some of these challenges and outline our vision of an integrated and consistent digital work environment named Zoomable Object-oriented Information Landscape.     

Digital Libraries for the Next Millennium: Challenges and Research Directions

The unprecedented growth of Internet technologies has made resources on the World Wide Web instantly accessible to various user communities through digital libraries. Since the early 1990s, there have been several digital library initiatives sponsored by government agencies and/or private organizations all over the world. A digital library is a networked system environment that provides diverse user communities with coherent, seamless and transparent access to large, organized, and digitized information resources. This article provides a comprehensive overview of major digital library projects that are currently being undertaken across the globe. We also identify and discuss major challenges and research issues to be addressed in the design and implementation of digital libraries for the next millennium. We believe that digital libraries are ripe with research opportunities, offer many challenges, and will continue to grow in the next several years.     

A Graphical Model for Context-Aware Visual Content Recommendation

Existing recommender systems provide an elegant solution to the information overload in current digital libraries such as the Internet archive. Nowadays, the sensors that capture the user's contextual information such as the location and time are become available and have raised a need to personalize recommendations for each user according to his/her changing needs in different contexts. In addition, visual documents have richer textual and visual information that was not exploited by existing recommender systems. In this paper, we propose a new framework for context-aware recommendation of visual documents by modeling the user needs, the context and also the visual document collection together in a unified model. We address also the user's need for diversified recommendations. Our pilot study showed the merits of our approach in content based image retrieval.     

Improving the Quality of the Personalized Electronic Program Guide

As Digital TV subscribers are offered more and more channels, it is becoming increasingly difficult for them to locate the right programme information at the right time. The personalized Electronic Programme Guide (pEPG) is one solution to this problem; it leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the limited availability of profiling information is a key limiting factor in such personalized recommender systems. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this article we address the sparsity problem in the Digital TV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovered by mining user profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale, state-of-the-art online systems—PTVPlus, a personalized TV listings portal and Físchlár, an online digital video library system.     

How scales influence user rating behaviour in recommender systems

Many websites allow users to rate items and share their ratings with others, for social or personalisation purposes. In recommender systems in particular, personalised suggestions are generated by predicting ratings for items that users are unaware of, based on the ratings users provided for other items. Explicit user ratings are collected by means of graphical widgets referred to as ‘rating scales’. Each system or website normally uses a specific rating scale, in many cases differing from scales used by other systems in their granularity, visual metaphor, numbering or availability of a neutral position. While many works in the field of survey design reported on the effects of rating scales on user ratings, these, however, are normally regarded as neutral tools when it comes to recommender systems. In this paper, we challenge this view and provide new empirical information about the impact of rating scales on user ratings, presenting the results of three new studies carried out in different domains. Based on these results, we demonstrate that a static mathematical mapping is not the best method to compare ratings coming from scales with different features, and suggest when it is possible to use linear functions instead.     

Semantic inference of user’s reputation and expertise to improve collaborative recommendations

Collaborative recommender systems select potentially interesting items for each user based on the preferences of like-minded individuals. Particularly, e-commerce has become a major domain in these research field due to its business interest, since identifying the products the users may like or find useful can boost consumption. During the last years, a great number of works in the literature have focused in the improvement of these tools. Expertise, trust and reputation models are incorporated in collaborative recommender systems to increase their accuracy and reliability. However, current approaches require extra data from the users that is not often available. In this paper, we present two contributions that apply a semantic approach to improve recommendation results transparently to the users. On the one hand, we automatically build implicit trust networks in order to incorporate trust and reputation in the selection of the set of like-minded users that will drive the recommendation. On the other hand, we propose a measure of practical expertise by exploiting the data available in any e-commerce recommender system – the consumption histories of the users.     

On the role of trust in collaborative Web search

Recommender systems combine ideas from information retrieval, user modelling, and artificial intelligence to focus on the provision of more intelligent and proactive information services. As such, recommender systems play an important role when it comes to assisting the user during both routine and specialised information retrieval tasks. Like any good assistant it is important that users can trust in the ability of a recommender system to respond with timely and relevant suggestions. In this paper, we will look at a collaborative recommendation system operating in the domain of Web search. We will show how explicit models of trust can help to inform more reliable recommendations that translate into more relevant search results. Moreover, we demonstrate how the availability of this trust-model facilitates important interface enhancements that provide a means to declare the provenance of result recommendations in a way that will allow searchers to evaluate their likely relevance based on the reputation and trustworthiness of the recommendation partners behind these suggestions.     

Multidimensional ranking for data in digital spatial libraries

Digital spatial libraries currently under development are generating large repositories of data which will continue to grow. As these repositories grow, the situation will inevitably arise in which a digital library user may be confronted with several hundred spatial data sets in response to a particular query. The question then arises as to how the results from this search can be most easily assimilated by the user. Text based materials have benefited from substantial research and experience on ranking of search results. Ranking of spatial data sets has not received the same attention since there has been little motivation for such activity until recently. In this paper we propose a multidimensional ranking scheme based on the three dimensions of space, time, and theme. The multidimensional rank is presented graphically to inform users about how well data sets from a digital spatial library meet their spatial, temporal, and thematic targets.     

面向用户偏好建模的个性化对话推荐算法

推荐系统的目标是从物品数据库中，选择出与用户兴趣偏好相匹配的子集，缓解用户面临的“信息过载”问题。因而近年来推荐系统越来越多地应用到电商、社交等领域，展现出巨大的商业潜力。传统推荐系统中，系统对用户的认知往往来源于历史交互记录，例如点击率或者购买记录，这是一种隐式用户反馈。对话推荐系统能够通过自然语言与用户进行多轮对话，逐步深入挖掘其兴趣偏好，从而向对方提供高质量的推荐结果。相比于传统推荐系统，对话推荐系统主要有两方面的不同。其一，对话推荐系统能够利用自然语言与用户进行语义上连贯的多轮对话，提升了人机交互中的用户体验；其二，系统能够询问特定的问题直接获取用户的显式反馈，从而更深入地理解用户兴趣偏好，提供更可靠的推荐结果。目前已经有不少工作在不同的问题设定下对该领域进行了探索，然而尽管如此，这些工作仍仅局限于关注当前正在进行的对话，忽视了过去交互记录中蕴涵的丰富信息，导致对用户偏好建模的不充分。为了解决这个问题，本文提出了一个面向用户偏好建模的个性化对话推荐算法框架，通过双线性模型注意力机制与自注意力层次化编码结构进行用户偏好建模，从而完成对候选物品的排序与推荐。本文设计的模型结构能够在充分利用用户历史对话信息的同时，权衡历史对话与当前对话两类数据的重要性。丰富的用户相关信息来源使得推荐结果在契合用户个性化偏好的同时，更具备多样性，从而缓解“信息茧房”等现象带来的不良影响。基于公开数据集的实验表明了本文方法在个性化对话推荐任务上的有效性。     

Accessing networked information: Research,issues and implications

The provision of information services has changed dramatically over the last few years, particularly with developments in access to networked information technology and the shift from a paper‐based to a digital information environment. The development of the ‘digital library’ or ‘virtual library’ in particular has created an information environment that is complex and fluid, connective and interactive, and diverse and unpredictable. It is an environment that now places considerable responsibility on information users to be able to navigate this complex and often ambiguous information space. This does not happen by chance, and calls for considerable attention to be given to reshaping the role of information professionals in enabling users to effectively interact with this information. Against this backdrop, this paper will examine current research on information user behaviour in digital environments, particularly in relation to using Web‐based information services. It will highlight substantive issues in relation to education and training, particularly in the context of information literacy and its development in digital environments.     

Power coefficient as a similarity measure for memory-based collaborative recommender systems

E-commerce systems employ recommender systems to enhance the customer loyalty and hence increasing the cross-selling of products. However, choosing appropriate similarity measure is a key to the recommender system success. Based on this measure, a set of neighbors for the current active user is formed which in turn will be used later to recommend unseen items to this active user. Pearson correlation coefficient, the most popular similarity measure for memory-based collaborative recommender system (CRS), measures how much two users are correlated. However, statistic’s literature introduced many other coefficients for matching two sets (vectors) that may perform better than Pearson correlation coefficient. This paper explores Jaccard and Dice coefficients for matching users of CRS. A more general coefficient called a Power coefficient is proposed in this paper which represents a family of coefficients. Specifically, Power coefficient gives many degrees for emphasizing on the positive matches between users. However, CRS users have positive and negative matches and therefore these coefficients have to be modified to take negative matches into consideration. Consequently, they become more suitable for CRS research. Many experiments are carried out for all the proposed variants and are compared with the traditional approaches. The experimental results show that the proposed variants outperform Pearson correlation coefficient and cosine similarity measure as they are the most common approaches for memory-based CRS.     

Collaborative user modeling with user-generated tags for social recommender systems

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user’s characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.