Exploring new applications and services for mobile environments has generated considerable excitement among both commercial companies and academics. In this paper we propose a location-aware recommender system that accommodates a customer’s shopping needs with location-dependent vendor offers and promotions. Specifically, we propose a recommender system for recommending vendors’ webpages – incl...

Users planning a trip to a given destination often search for the most appropriate points of interest location, this being a non-straightforward task as the range of information available is very large and not very well structured. The research presented by this paper introduces a context-aware tourism recommender system that overcomes the information overload problem by providing personalized ...

Context-aware recommender systems extend traditional recommender systems by adapting their output to users’ specific contextual situations. Most of the existing approaches to context-aware recommendation involve directly incorporating context into standard recommendation algorithms (e.g., collaborative filtering, matrix factorization). In this paper, we highlight the importance of context simil...

Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite ...

Current major P2P systems focus on PCs and do not provide services for the mobile environment. Compared to traditional P2P, characteristics of Mobile P2P include unreliable connections, limited bandwidth and constraints of mobile devices. In addition, nomadic users demand applications and services that are context-aware, personalised, secure, and trustworthy. Recommender systems are one of thes...

Recommender systems are a subclass of information filtering systems that predict the 'rating' or 'preference' that a user would give to an item. Most traditional Recommender Systems (RSs) focus on recommending the most relevant items to individual users and do not take into consideration the circumstances and other contextual information such as time, place and company of other people when reco...

With the rapid expansion of the information on the Internet, recommender systems play an important role in terms of trade and research. Recommender systems try to guess the user's way of thinking, using the in-formation of user's behavior or similar users and their views, to discover and then propose a product which is the most appropriate and closest product of user's interest. In the past dec...

with the rapid expansion of the information on the internet, recommender systems play an important role in terms of trade and research. recommender systems try to guess the user's way of thinking, using the in-formation of user's behavior or similar users and their views, to discover and then propose a product which is the most appropriate and closest product of user's interest. ...

In this paper, we introduce a framework for modular generation of context-aware recommendations. The components of this framework include context sensors, recommender algorithms and utility modules (converters and filters), all realized as so-called services, which can flexibly be combined in terms of a recommender construction kit. Different areas of an application (e. g. a web portal) thus ca...