Many trust-aware recommender systems have explored the value of explicit trust, which is specified by users with binary values and simply treated as a concept with a single aspect. However, in social science, trust is known as a complex term with multiple facets, which have not been well exploited in prior recommender systems. In this paper, we attempt to address this issue by proposing a (dis)...

Recommender systems are amongst the most prominent and successful fruits of social computing; they harvest profiles from a community of users in order to offer individuals personalised recommendations. The notion of trust plays a central role in this process, since users are unlikely to interact with a system or respond positively to recommendations that they do not trust. However, trust is a m...

The Web is currently characterised by user contribution. As a result, content is generated in an uncontrolled way leading to the so-called “information overload”. The role of information filtering techniques and recommender systems is to give a solution to this problem by taking into account user preferences, and/or other context of information to present the content. Furthermore, word-of-mouth...

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. ...

Recommender Systems (RS) suggest to users items they might like such as movies or songs. However they are not able to generate recommendations for users who just registered, in fact bootstrapping Recommender Systems for new users is still an open challenge. While traditional RSs exploit only ratings provided by users about items, Trust-aware Recommender Systems let the user express also trust s...

Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust v...

Recommender Systems (RS) suggest to users items they might like such as movies or songs. However they are not able to generate recommendations for users who just registered, in fact bootstrapping Recommender Systems for new users is still an open challenge. While traditional RSs exploit only ratings provided by users about items, Trust-aware Recommender Systems let the user express also trust s...

From the observation that users reading news tend to not click outdated news, we propose notion of 'lifetime' with two hypotheses: (i) has a shorter lifetime, compared other types items such as movies or e-commerce products; (ii) only competes whose lifetimes have ended, and which an overlapping lifetime (i.e., limited competitions). By further developing characteristics then present novel appr...