these days, due to growing the e-commerce sites, access to information about items is easier than past. but because of huge amount of information, we need new filtering techniques to find interested information faster and more accurate. therefore recommender systems (rs) introduced for solving this problem. although several recommender approaches have proposed, collaborative filtering (cf) appr...

Almost everyone has turned to the internet to vet a product before purchase. Some online retailers – like amazon.com – even provide you with recommendations for products you might like. In this age of online retailers and multiple anonymous raters, it is a worthwhile goal to use the pool of collective knowledge and opinions to guide product recommendations. The objective of recommender systems ...

Recommender systems are one of the information filtering systems which provides business intelligence to the active users on e-commerce by more suggesting suitable items through personalized web page. User’s explicit knowledge about the items, preferences, density of neighbours and recommendation criteria play vital role in providing accurate recommendations. This paper introduces knowledge and...

In this paper, a validation and an experimentation of the use of graded BDI agents is reported. This agent model has been proposed to specify agents capable to deal with the environment uncertainty and with graded attitudes in an efficient way. As a case study we focus on a Tourism Recommender Agent specified using this agent model. The experimentation on the case study aims at proving that thi...

Recommender systems aim to identify interesting items (e.g. movies, books, websites) for a given user, based on their previously expressed preferences. As recommender systems grow in popularity, a notable divergence emerges between research practices and the reality of deployed systems: when recommendation algorithms are designed, they are evaluated in a relatively static context, mainly concer...

With the change from classical paper-based to online representations of newspapers publishers are able to provide adaptive recommender services supporting users in finding the relevant articles in the huge amount of published news. Since most traditional recommender approaches are tailored to scenarios characterized by static sets of items and exactly identifiable users, these approaches cannot...

We develop a causal inference approach to recommender systems. Observational recommendation data contains two sources of information: which items each user decided to look at and which of those items each user liked. We assume these two types of information come from differentmodels—the exposure data comes from a model by which users discover items to consider; the click data comes from a model...

Recommender systems help people cope with the problem of information overload. A recently proposed adaptive news recommender model [Medo et al., 2009] is based on epidemic-like spreading of news in a social network. By means of agent-based simulations we study a " good get richer " feature of the model and determine which attributes are necessary for a user to play a leading role in the network...

Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...

Homeland security intelligence analysts need help finding relevant information quickly in a rapidly increasing volume of incoming raw data. Many different AI techniques are needed to handle this deluge of data. This paper describes initial investigations in the application of recommender systems to this problem. It illustrates various recommender systems technologies and suggests scenarios for ...