Social Web stands for the culture of participation and collaboration on the Web. Structures emerge from social interactions: social tagging enables a community of users to assign freely chosen keywords to Web resources. The structure that evolves from social tagging is called folksonomy and recent research has shown that the exploitation of folksonomy structures is beneficial to information sys...

The goal of Web recommender system is the process of selecting web pages shown to user based on his navigation patterns and interests. In this paper, a new model for recommender system is proposed to increase the accuracy of recommendations. In this model, some effective data sources are integrated to know the user interestingness. The sources used the proposed model are user spent times on pag...

The way academic community members develop their research activities, access information resources and communicate with each other has dramatically changed with the irruption of the web. Nevertheless, the tools provided by today’s web aren’t efficient enough to satisfy many of the specific requirements of this new generation of e-scholars. In this paper we present a filtering and recommender sy...

Collaborative Filtering has been widely used in Recommender Systems helping customers of e-shops to find out items matching their requirements in huge or complex search spaces. There exist many commercial applications that show the utility of these systems, especially in e-commerce whose features and good performance obtained has driven us to consider their application in a specific domain as A...

Related Pins is the Web-scale recommender system that powers over 40% of user engagement on Pinterest. This paper is a longitudinal study of three years of its development, exploring the evolution of the system and its components from prototypes to present state. Each component was originally built with many constraints on engineering effort and computational resources, so we prioritized the si...

Weblogs are one of fundamental components of Web 2.0 and there are a lot of unskilled bloggers and visitors who have difficulties in finding relevant blogs. Recommender systems are a solution to the information overload problems. In this paper a weblog recommender system based on link structure of weblog graph is introduced. Here we treat links between weblogs as some kind of rating, meaning th...

The huge amount of information on the Internet creates a problem for the users – information overload. For this reason, finding the worthwhile information is becoming a challenge. To aid users a new approach based on Recommender System. This type of system applies information filtering in order to recommend items to a user based on the user's profile and historical consumption. Recommender Syst...

Online shops and B2C sites in diverse domains such as ’quality & taste’, consumer electronics or e-tourism require on the one side persuasive Web presentation and on the other side deep product knowledge. In that context recommender applications may help to create an enjoyable shopping experience for online users. The Advisor Suite framework is a knowledge-based conversational recommender syste...

cold start is one of the main challenges in recommender systems. solving sparsechallenge of cold start users is hard. more cold start users and items are new. sine many general methods for recommender systems has over fittingon cold start users and items, so recommendation to new users and items is important and hard duty. in this work to overcome sparse problem, we present a new method for rec...

Due to the rapid growth of internet, a useful technology named recommender system (RS) become an effective application to make recommendations to users, nowadays, many collaborative recommender systems (CRS) have succeeded in some fields like movies and music web applications; however, there are also some ways for them to be a more effective RS. This paper introduces a new item-based collaborat...