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With the advent of social networks and tagging systems, The Internet has recently witnessed a big leap in the use of Web Recommendation Systems WRS. Based on users' likings of items and their browsing history on the world wide web, these systems are able to predict and recommend items and future purchases to users. They are being used now in various domains, like news article recommendation, product...
Context prediction approaches forecast future contexts based on known context patterns to adapt e.g., services in advance. In the case of the user's context history not providing suitable context information for the observed context pattern, to the best of our knowledge context prediction algorithms will fail to forecast the appropriate future context. To overcome the gap of missing context information...
The significant feature of a social networking website is the primary reason they are made for: connecting people and friends via internet. “Friend recommender systems” are wisely designed for finding people, most of whom tend to be with the same interests and backgrounds. These systems use a set of predefined items from which users specify their preferences simply by selecting from a fixed list....
The majority of existing recommender systems use one or more statistical techniques to recommend content. While such techniques can be very effective, they have a number of restrictions, such as their inability to recommend items based on meaning or relationships between different characteristics of each item. This paper describes the design of a hybrid recommender system that uses a combination of...
Tag recommendation is an integral part of any bookmarking application. With the growing popularity in Web 2.0 usage, recommending tags is of utmost importance in enabling a user to perform bookmarking easily. An issue that most recommendation systems do not consider is that users have a tendency to choose from tags that are suggested to them, which might bias the true popular rankings of tags. In...
Nowadays, the development and application of the recommender system has grown greatly to cope with information overloading. Meanwhile, social networh come into being and become more and more popular. In this paper, a recommendation model based on social networks is proposed which can collect the users profile from the feedback and system log, then set up the social networks. According to the input...
Given a user in a social network, which new friends should we recommend, the dual goal being to achieve user satisfaction and good network connectivity? Similarly, which new products are better to recommend to satisfy customers' taste/needs as well as increase vendor profit? Typical recommender systems use merely past purchases, product ratings, demographic meta-data, and network `proximity' to make...
Interactions between individuals are inherently dependent upon trust, no matter if they occur in the real world or in cyber communities. Over the past years, proposals have been made to model trust relations computationally, either to assist users or for modeling purposes in multi-agent systems. These models rely implicitly on the social networks established by participating entities (be they autonomous...
Social network studies are becoming increasingly popular and have been applied to several fields of study such as law enforcement, marketing, spread of disease, as well as in the improvement of organizational performance. One area that is yet to be explored relates to harnessing the power of social networks as recommender systems. The idea that users may provide other users with recommendations that...
This paper considers the problem of recommending experts for a question. The problem of expert finding has been investigated in the past, within the context of information retrieval, where the focus has been on measuring expert authority. However, we treat the problem as a recommendation problem in the context of social search, that is, expert recommendations are personalized for a questioner based...
This paper proposes a new approach of mentor selection in memory-based collaborative filtering when no rating is available. Users are represented under the form of a social network. The selection of mentors is performed through the use of a community detection algorithm used in the frame of social networks. It allows to recommend items to a given user, by applying democratic voting rules within his...
Locating IT Inventory Management information is a challenging task, as the knowledge gets transferred among employees that move within or leave the context of a large organization. Information that relates to IT inventory is hidden in the knowledge of individual team members. This fact is not reflected in organizational expertise repositories and therefore locating those employees becomes a cumbersome...
Recommender systems have emerged as an essential response to the rapidly growing digital information phenomenon in which users are finding it more and more difficult to locate the right information at the right time. Systems under Web2.0 allow users not only to give resources- ratings but also to assign tags to them. Tags play a significant role in Web 2.0. They can be used for navigation, browsing,...
Users want to access user generated content and recommendations in a contextualized way while on the move. In this paper we illustrate the POI radar service and its architecture, a mobile mashup of integrated location, social network and users' recommendations to provide contextualized notifications of nearby points of interest voted by user's contacts. A description of the service scenario, architecture...
Automatic discovery of Web services is a crucial task for e-business communities. Locating and selecting "the best" Web service from a vast number of similar services that matches the user's requirements and preferences is a cognitive challenge and requires the use of an intelligent decision making framework. This paper develops a flexible ontological architecture and framework for semantic...
This paper proposes a social recommendation algorithm for use in a research social network environment. The social recommendation algorithm proposed combines the concepts of a relationship ontology and item-based collaborative filtering (CF). While the network setup in social networking sites can accurately reflect the social landscape of its users, it is much harder to detect the importance or strength...
In social networks every user could act as an individual recommender for its neighbours. However, the information spread across the network change quickly over time. There is a need for a recommendation mechanism able to manage this dynamicity and to provide users with up-to-date and suitable recommendations. This paper proposes a well-known concept of argumentation theory, dialogue games, as a mechanism...
Recent years have witnessed the increased application of AI technologies to real-world e-commerce challenges. This article presents a brief overview of representative work by Chinese researchers, covering topics such as multiagent decision making, keyword advertising, social networks, recommender systems, information retrieval and the semantic Web, and computational experiments. This article is part...
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