Towards personalization in digital libraries through ontologies

In this paper we describe a browsing and searching personalization system for digital libraries based on the use of ontologies for describing the relationships between all the elements which take part in a digital library scenario of use. The main goal of this project is to help the users of a digital library to improve their experience of use by means of two complementary strategies: first, by maintaining a complete history record of his or her browsing and searching activities, which is part of a navigational user profile which includes preferences and all the aspects related to community involvement; and second, by reusing all the knowledge which has been extracted from previous usage from other users with similar profiles. This can be accomplished in terms of narrowing and focusing the search results and browsing options through the use of a recommendation system which organizes such results in the most appropriate manner, using ontologies and concepts drawn from the semantic web field. The complete integration of the experience of use of a digital library in the learning process is also pursued. Both the usage and information organization can be also exploited to extract useful knowledge from the way users interact with a digital library, knowledge that can be used to improve several design aspects of the library, ranging from internal organization aspects to human factors and user interfaces. Although this project is still on an early development stage, it is possible to identify all the desired functionalities and requirements that are necessary to fully integrate the use of a digital library in an e-learning environment

Adapting Recommendation Diversity to Openness to Experience : A Study of Human Behaviour

Postprin

Emergence of scale-free leadership structure in social recommender systems

The study of the organization of social networks is important for understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a "good get richer" mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

The impact of social influence and third party endorsement on online shopping in Saudi Arabia

It is well documented that social influence and third party endorsements play a significant role in developing trust in E-Commerce. Previous studies have shown that it is relatively true in many countries and across cultures. However, very few studies were conducted in the Middle East and to our knowledge this was the first time to consider family members and friends Recommendation with the context of social influence conducted within Saudi Arabia. The research reported in this paper attempts to investigate whether the findings from previous studies will be similar in Saudi Arabia. Specifically, this study will evaluate the impact of social influence and endorsements on online shopping and whether this plays an important role in increasing online shopping in Saudi Arabia. The results of this study are based on quantitative data collected from a sample of 606 Saudis citizens living in Saudi Arabia. Four factors connected to the impact of social influence and third party endorsements in online shopping are examined. The initial findings of this research confirm that there are similarities with the results of previous studies conducted in other countries. Similarly, the impact of social influence and third party endorsements seems to encourage and support the development of online shopping in Saudi Arabia

+Spaces: Intelligent Virtual Spaces for eGovernment

Intelligent Environments most commonly take a physical form such as homes, offices, hotels, restaurants, shops, that are equipped with advanced networked computer based systems, which enable better or new lifestyles for people. However, Intelligent Environments can also take the form of virtual online spaces such as SecondLife, which can both mimic the real world and provide functionalities which could not be provided in reality, such as advanced simulations and movement. There is the growing trend for people to spend more time in such virtual environments and, to these ends, this work in progress paper reports on a new project, +Spaces which is developing a range of virtual world tools for e-government applications, and presents some of the concepts and technical challenges involved in creating these intelligent virtual spaces for e-government. © 2010 IEEE

Improving the Performance of Recommendation on Social Network by Investigating Interactions of Trust and Interest Similarity

On the social media, lots of people share their experiences through various factors like blogs, online ratings, reviews, online polling and tweets. Study shows that the factors such as interpersonal interest and interpersonal influence from the social media which is based on the circles as well as groups of friends leads to opportunities and challenges in solving the problems on datasets. This challenge is for the Recommender System (RS) to find the solution on cold start and sparsity problems. In this paper, on the basis of the probabilistic matrix factorization, the social factors like personal interest, interpersonal influence and interpersonal interest similarity are combined into a unified personalized recommendation model. These factors can improve the associating linkage in latent space. Various datasets are used to conduct the experiments to get the results that show that the proposed model performs better than the existing approaches

The Mediating Role of Adaptive Personalization in Online Shopping

In the context of e-commerce, personalization system provides customers with recommendations on what products to buy. Grounded on social exchange theory, this study empirically examines and theoretically articulates the effects of willingness to share information and adaptive personalization on willingness to repurchase products. A survey was conducted and PLS was used demonstrating that adaptive personalization fully mediates the relationship between willingness to share information and willingness to repurchase products. The results suggest that online customers might take risks to provide their information to online retailers in exchange of offerings, and that continuous capturing of customer’s preferences throughout their interaction time with the system can lead to better recommendations from the system, thus providing more incentives for them to repurchase products