Personalization in cultural heritage: the road travelled and the one ahead

Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed

The Partial Evaluation Approach to Information Personalization

Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, we mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.Comment: Comprehensive overview of the PIPE model for personalizatio

Science of Digital Libraries(SciDL)

Our purpose is to ensure that people and institutions better manage information through digital libraries (DLs). Thus we address a fundamental human and social need, which is particularly urgent in the modern Information (and Knowledge) Age. Our goal is to significantly advance both the theory and state-of-theart of DLs (and other advanced information systems) - thoroughly validating our approach using highly visible testbeds. Our research objective is to leverage our formal, theory-based approach to the problems of defining, understanding, modeling, building, personalizing, and evaluating DLs. We will construct models and tools based on that theory so organizations and individuals can easily create and maintain fully functional DLs, whose components can interoperate with corresponding components of related DLs. This research should be highly meritorious intellectually. We bring together a team of senior researchers with expertise in information retrieval, human-computer interaction, scenario-based design, personalization, and componentized system development and expect to make important contributions in each of those areas. Of crucial import, however, is that we will integrate our prior research and experience to achieve breakthrough advances in the field of DLs, regarding theory, methodology, systems, and evaluation. We will extend the 5S theory, which has identified five key dimensions or onstructs underlying effective DLs: Streams, Structures, Spaces, Scenarios, and Societies. We will use that theory to describe and develop metamodels, models, and systems, which can be tailored to disciplines and/or groups, as well as personalized. We will disseminate our findings as well as provide toolkits as open source software, encouraging wide use. We will validate our work using testbeds, ensuring broad impact. We will put powerful tools into the hands of digital librarians so they may easily plan and configure tailored systems, to support an extensible set of services, including publishing, discovery, searching, browsing, recommending, and access control, handling diverse types of collections, and varied genres and classes of digital objects. With these tools, end-users will for be able to design personal DLs. Testbeds are crucial to validate scientific theories and will be thoroughly integrated into SciDL research and evaluation. We will focus on two application domains, which together should allow comprehensive validation and increase the significance of SciDL's impact on scholarly communities. One is education (through CITIDEL); the other is libraries (through DLA and OCKHAM). CITIDEL deals with content from publishers (e.g, ACM Digital Library), corporate research efforts e.g., CiteSeer), volunteer initiatives (e.g., DBLP, based on the database and logic rogramming literature), CS departments (e.g., NCSTRL, mostly technical reports), educational initiatives (e.g., Computer Science Teaching Center), and universities (e.g., theses and dissertations). DLA is a unit of the Virginia Tech library that virtually publishes scholarly communication such as faculty-edited journals and rare and unique resources including image collections and finding aids from Special Collections. The OCKHAM initiative, calling for simplicity in the library world, emphasizes a three-part solution: lightweightprotocols, component-based development, and open reference models. It provides a framework to research the deployment of the SciDL approach in libraries. Thus our choice of testbeds also will nsure that our research will have additional benefit to and impact on the fields of computing and library and information science, supporting transformations in how we learn and deal with information

Personalizing Interactions with Information Systems

Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation tailored to the individual. In this chapter, we study personalization from the viewpoint of personalizing interaction. The survey covers mechanisms for information-finding on the web, advanced information retrieval systems, dialog-based applications, and mobile access paradigms. Specific emphasis is placed on studying how users interact with an information system and how the system can encourage and foster interaction. This helps bring out the role of the personalization system as a facilitator which reconciles the user’s mental model with the underlying information system’s organization. Three tiers of personalization systems are presented, paying careful attention to interaction considerations. These tiers show how progressive levels of sophistication in interaction can be achieved. The chapter also surveys systems support technologies and niche application domains

A Survey on Conversational Search and Applications in Biomedicine

This paper aims to provide a radical rundown on Conversation Search (ConvSearch), an approach to enhance the information retrieval method where users engage in a dialogue for the information-seeking tasks. In this survey, we predominantly focused on the human interactive characteristics of the ConvSearch systems, highlighting the operations of the action modules, likely the Retrieval system, Question-Answering, and Recommender system. We labeled various ConvSearch research problems in knowledge bases, natural language processing, and dialogue management systems along with the action modules. We further categorized the framework to ConvSearch and the application is directed toward biomedical and healthcare fields for the utilization of clinical social technology. Finally, we conclude by talking through the challenges and issues of ConvSearch, particularly in Bio-Medicine. Our main aim is to provide an integrated and unified vision of the ConvSearch components from different fields, which benefit the information-seeking process in healthcare systems

Personalisation and recommender systems in digital libraries

Widespread use of the Internet has resulted in digital libraries that are increasingly used by diverse communities of users for diverse purposes and in which sharing and collaboration have become important social elements. As such libraries become commonplace, as their contents and services become more varied, and as their patrons become more experienced with computer technology, users will expect more sophisticated services from these libraries. A simple search function, normally an integral part of any digital library, increasingly leads to user frustration as user needs become more complex and as the volume of managed information increases. Proactive digital libraries, where the library evolves from being passive and untailored, are seen as offering great potential for addressing and overcoming these issues and include techniques such as personalisation and recommender systems. In this paper, following on from the DELOS/NSF Working Group on Personalisation and Recommender Systems for Digital Libraries, which met and reported during 2003, we present some background material on the scope of personalisation and recommender systems in digital libraries. We then outline the working group’s vision for the evolution of digital libraries and the role that personalisation and recommender systems will play, and we present a series of research challenges and specific recommendations and research priorities for the field

Personalized Memory Transfer for Conversational Recommendation Systems

Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach