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The recommender system is widely used in many areas in the age of information overload. Collaborative filtering (CF), as one of the most successful methods used for recommendation, recommends items based on the nearest neighbors of the target user. Thus, the performance of the recommender system depends largely on the similarity measure used for selecting neighbors. Most of the traditional similarity...
This paper presents an open recommender system to ease the entering barriers due to lack of sufficient background knowledge for small or new service providers. The proposed Open Preference and Feature recommender (OPF) uses user preference and item feature as the basis of recommendations, since the generality of preference and feature and therefore meets the needs of an open recommender system. In...
Nowadays, more and more applications are moving to cloud computing. How to deploy these applications optimally is a great challenge. Many cloud applications, such as scientific applications, are large-scale distributed systems that are deployed on a lot of distributed cloud nodes, and there are a lot of communications between these nodes. Therefore, taking the communication latency between cloud nodes...
In this paper we examine an advanced collaborative filtering method that uses similarity transitivity concepts. By propagating "similarity" between users, in a similar way as with "trust", we can significantly expand the space of potential recommenders and system's coverage, improving also the recommendations' accuracy. While "trust" information might be missing or be...
One of the key aspects instrumental in the advancement of science relates to “team science,” or in other words “group” collaborations. There have been extensive studies analyzing various statistical properties of collaborations of individual or pairs of authors. However, the number of studies pertaining to groups/teams of scientists working together is limited in number. In this paper, we set an objective...
Repeat transmission of hotspot traffics results in great waste of energy and bandwidth in wireless network, for the communication of current network is content careless. To address this issue, a novel content aware transmission schema named CASoRT System was put forward in our previous research to reduce wireless resource waste by the benefit of broadcast. In this paper, we propose a K neighbors collaborative...
Collaborative filtering (CF) is a widely-used technique for generating personalized recommendations. CF systems are typically based on the ratings given by users to items. There are many factors influencing user's rating, beside user's interest and rating scale, item objective character is also the important element. Considering these factors, the improved item prediction approaches present a more...
The accuracy and quality is the best evaluation of recommend system. This paper proposes a collaborative filtering remmendation algorithms based on computing the sematic similarity of items in order to improve the accuracy of items' similarity. The experimental results shows that the optimized algorithm can give a better prediction, by way of increasing accuracy and reducing cold-start problem of...
Many of the recent algorithms have been developed to improve the various aspects of collaborative filtering recommender systems, however, most of them do not take the sectional data of users and items information or characteristic into account. This paper, we present a new improved collaborative filtering based on item similarity modified and item common ratings which take full advantage of the sectional...
We study the problem of predicting a rating for an unseen item based on a distributed dataset owned by two honest-but-curious parties without revealing their private datasets to each other. Our proposed idea uses a new similarity measure such that the similarity aggregated from two local similarities is approximately equal to the global similarity. We evaluate the accuracy of prediction of rating...
User reputation is an important factor in collaborative filtering approaches, in which every user may be another's nearest neighbor and may provide recommendations. In order to generate accurate results, the recommender system assigns different weights to users according to their reputations. However, existing methods for evaluating user reputation consider only the number of feedback ratings and...
Trust -- aware recommender systems are intelligent technology applications that make use of trust information and user personal data in social networks to provide personalized recommendations. Recent research on recommender systems shows that these recommender systems are more robust against shilling attacks and can better be used for generating recommendations for new users. In this paper we proposed...
In the field of collaborative filtering recommendation, the accuracy requirement of the recommendation algorithm always makes it complex and hard to realize. Slope One algorithm, as a recent proposed algorithm, was not only easy to achieve, but also efficient and effective. However, Slope One algorithm performs not so well when dealing with personalized recommendation tasks which concern the relationship...
Internet and E-Commerce are becoming an integral part of everyday life as we accumulate more and more knowledge that demands for some personalized recommendation technology. Collaborative filtering recommendation is the most successful personalized recommendation algorithm among current technologies. The paper suggests the two-phase clustering-based collaborative filtering algorithm. which not only...
As Cloud Computing has emerged as new computing paradigm, more and more services have been deployed and provided on the cloud platform with a SaaS model, thereby how to select a qualified service is becoming a key issue. Several approaches based on service feedback ranking e.g., rating-oriented collaborative filtering (CF) have been proposed. Traditional CF approaches predict the potential QoS values...
Collaborative filtering is one of widely used Web service recommendation techniques. There have been several methods of Web service selection and recommendation based on collaborative filtering, but seldom have they considered personalized influence of users and services. In this paper, we present an effective personalized collaborative filtering method for Web service recommendation. A key component...
Recommendation systems have been investigated and implemented in many aspects. Particularly, in case of collaborative filtering system, more important issue is how to manipulate the personalized recommendation results for better user understandability and satisfaction. Collaborative filtering system predicts items of interest for users based on predictive relationship discovered between the item and...
Cyber defense competitions arising from U.S. service academy exercises offer a platform for collecting data that can inform research that ranges from characterizing the ideal cyber warrior to describing behaviors during certain challenging cyber defense situations. This knowledge in turn could lead to better preparation of cyber defenders in both military and civilian settings. We conducted proof-of-concept...
Recommender systems are gaining a great importance with the emergence of E-commerce and business on the internet. These recommender systems help users in making decision by suggesting products and services that satisfy the users' tastes and preferences. Collaborative filtering and content-based recommendation are two fundamental methods used to develop recommender systems. Although, both methods have...
This paper presents an personalized recommendation model to recommend potentially interesting resources to users based on the users' search behaviors and resource properties. This model builds on the user-based collaborative filtering technology and the top-N resource recommending algorithm, which consists of three parts: users' preference description, similar users' calculation and the resource recommending...
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