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Nowadays recommender systems are developed to provide information for users to choose the best things that they want. Recently different collaborative filtering techniques have been successfully employed to provide precise recommendations. However, sparsity problem is still considered as an important remained challenges. Existing CF based recommendation methods generally focus on positive similarities...
Establishing trust relationships between routing nodes represents a vital security requirement to establish reliable routing processes that exclude infected or selfish nodes. In this paper, we propose a new security scheme for the Internet of things and mainly for the RPL (Routing Protocol for Low-power and Lossy Networks) called: Metric-based RPL Trustworthiness Scheme (MRTS). The primary aim is...
Recently, with the surge of students pursuing graduate studies after completing their bachelors, there is a lack of open source resources which could point out universities and programs, based on an individual's profile. In this paper, we present our novel approach of predicting universities for graduate studies based on one's whole profile. A model is built which is able to predict the list of top-‘n’...
Similarity computations are crucial in various web activities like advertisements, search or trust-distrust predictions. These similarities often vary with time as product perception and popularity constantly change with users' evolving inclination. The huge volume of user-generated data typically results in heavyweight computations for even a single similarity update. We present I-SIM, a novel similarity...
Preprocessors are a common way to implement variability in software. They are used in numerous software systems, such as operating systems and databases. Due to the ability of preprocessors to enable and disable code fragments, not all parts of the program are active at the same time. Thus, programmers and tools need to handle the interactions resulting from annotations in the program. With our Eclipse-based...
Academic publication archives often draw from numerous, heterogeneous sources, whose records can follow differing naming conventions. As such, ambiguity issues concerning authorship of scientific papers often arise, such as authors sharing similar names, the use of first names versus initials, or alternate name spellings for the same author. These ambiguities have plagued research on scientific collaboration...
We propose a novel method based on user's double identities in the context of social network which people can consume information as well as generate content. We acquire relationship, which we call trust, between users from users' activities that performed on the related items (i.e. resource) that authors have published. Then we improve users' ratings on items with their relationship with authors...
The design of routing protocols for opportunistic networks (OppNets) generally assume that some cooperation prevail between the nodes. But, in the presence of non-collaborative attacks such as supernova and hypernova, the routing operations become a challenge. This paper proposes a defense mechanism against the misbehavior of supernova and hypernova nodes in an OppNet running Epidemic and ProPHet...
In the last two decades, the increase in the amount of information available online have resulted in an information overload problem making it very complex for users to get the useful information they require within time. A recommender system helps customer to make useful decisions about products they wants to purchase thus providing better customer satisfaction which is vital in online environments...
The evolution dynamics of a software ecosystem depend on the activity of the developer community contributing to projects within it. Both social and technical changes affect an ecosystem's evolution and the research community has been investigating the impact of these modifications over the last few years. Existing studies mainly focus on temporary modifications, often ignoring the effect of permanent...
Compiler optimizations discover facts about program behavior by querying static analysis. However, developing or extending precise analysis is difficult. Some prior works implement analysis with a single algorithm, but the algorithm becomes more complex as it is extended for greater precision. Other works achieve modularity by implementing several simple algorithms and trivially composing them to...
Collaborative Filtering technique is a recognized technique used in recommender systems for providing useful recommendations to users. The domain dependent nature of Collaborative Filtering allows more diverse set of recommendations at the same time making the user interested in recommendation process. Cold start problem is the most inevitable problem in collaborative filtering which makes it difficult...
During the last decade a huge amount of data have been shown and introduced in the Internet. Recommender systems are thus predicting the rating that a user would give to an item. Collaborative filtering (CF) techniques are the most popular and widely used by recommender systems technique, which utilize similar neighbors to generate recommendations. This paper provides the concepts, methods, applications...
With AI celebrating its 60th anniversary, questions arise of when (not even if) a computational system will be able to understand humor. These questions open up interesting opportunities, but point out areas of research that yet are insufficient for informal human computer communication. This paper looks at computational humor as a way of verifying computational understanding of text (written or verbal)...
Recommender systems benefit us in tackling the problem of information overload and finding potential objects that we are interested in among diverse objects. A variety of recommendation algorithms have been proposed. Most of them only focus on the relationship between users and objects, but neglect the influence of social relationships. In this paper, by considering users' social trust relationships,...
Recommender system refers to an information system that predicts the intuition of user observing behavior of all the users. The idea of collaborative filtering lies in producing a set of recommendations based on similarity as well as knowledge of users' relationships to items. In this paper, we combine some traditional similarity metrics to find three types of similar users which are super similar,...
Almost all e-commerce websites generates recommendations for their users but most of them are irrelevant. Collaborative filtering is one of the most widely used recommendation generation technique by e-commerce websites. Collaborative filtering generates recommendations for the target user from the collaboration of the other users who have the similar interest derived from their ratings. With the...
Collaborative Filtering (CF) Recommendation System is a prominent technology which is widely online. Large variety of available CF algorithms and the multitude of their possible parameters have a huge impact on quality of the outcome on ECommerce. Unfortunately, the literature on CF recommender system evaluation presents different evaluation metrics at different situation and could not provide any...
An e-commerce website provides a platform for merchants to sell products to customers. While most existing research focuses on providing customers with personalized product suggestions by recommender systems, in this paper, we consider the role of merchants and introduce a parallel problem, i.e., how to select the most valuable customers for a merchant? Accurately answering this question can not only...
In order to generate effective results, it is essential for a recommender system to model the information about the user interests (user profiles). A profile usually contains preferences that reflect the recommendation technique, so collaborative systems represent a user with the ratings given to items, while content-based approaches assign a score to semantic/text-based features of the evaluated...
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