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...

Providing recommendations is acknowledged as an important feature of a business-to-consumer online storefront. Although many studies have been conducted the algorithms and operational procedures relating to personalized recommender systems, empirical evidence demonstrating relationships between social presence and two important outcomes of evaluating recommender systems, reuse intention and tru...

Trust plays an important role in making collaborative decisions about service evaluation and service selection in pervasive computing. Context is a fundamental concept in pervasive systems, which is based on the interpretation of environment and systems. The dynamic nature of context can strongly affect trust management and service selection. In this paper, we present a context-based trust mana...

A particularly challenging task for recommender systems (RSs) is deciding whether to recommend an item that received a variety of high and low scores from its users. RSs that incorporate a trust network among their users have the potential to make more personalized recommendations for such controversial items (CIs) compared to collaborative filtering (CF) based systems, provided they succeed in...

elite's trust is an important part of the social capital in a social system that can lead to further system goals. relationship and influence on students by the professors of universities has an important place for them among the elite. subject of this article is the trust of university professors in islamic republic of iran (iri). essential questions focused on various aspects of professo...

Recommender systems are commonly based on user ratings to generate tailored suggestions to users. Instabilities and inconsistencies in these ratings cause noise, reduce the quality of recommendations and decrease the users’ trust in the system. Detecting and addressing these instabilities in ratings is therefore very important. In this work, we investigate the influence of interaction methods o...

Recommender systems are popular tools dealing with the information overload problem in e-commerce web sites. The more they know about the users, the better recommendations they can provide. However, sometimes, in real situations, it is necessary to make guesses about the value of missing but useful data in order to generate a recommendation immediately, rather than waiting the data becomes avai...

In virtual communities (e.g., forums, blogs), modeling the trust of community members is an effective way to help members make decisions about whose information is more valuable. Towards this goal, we first formulate hypotheses on how various interaction attributes influence trust in virtual communities, and validate these hypotheses through experiments on real data. The influential attributes ...

Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a ...