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Modeling of data is an important step in process of interpreting the data and to understand the desired situation more clearly. The topic of social network structures is one of the highly studied subject and modeling is very important for social network mining. One of the modeling tools for such structures is Graphs. Graphs have been used for modeling and visualization tool of many structures such...
Collaborative learning is widely accepted as an approach to promote learning effectiveness and student satisfaction. However, the quality and outcomes of collaboration depend upon a number of factors, among which group formation plays an important role. Existing approaches take into account groups formed through random assignment or based on certain criteria such as academic performance, demographic...
Recommender systems are becoming the crystal ball of the Internet because they can anticipate what the users may want, even before the users know they want it. However, the machine-learning algorithms typically involved in the training of such systems can be computationally expensive, and often may require several days for retraining. Here, we present a distributed approach for load-balancing the...
The collaborative approach has shown interest in several fields of application, particularly in information retrieval to satisfy a need for shared information. Despite this collaboration, the search for relevant information is always a tedious task as long as the mass of information continues to increase, part of which is a source, while other parties represent comments on these sources. It is obvious...
In recent years, although large volumes of data of health-related physical fitness (HRPF) have been collected, the exercise prescription for Chinese kids is still formulated manually by experts. It is necessary to develop an effective and efficient mechanism to recommend an automatic physical exercise prescription. Toward this purpose, this paper presents an experimental study of the framework for...
The group formation problem is a key problem in group learning. In this paper, we proposed a group formation system by using learning logs, which were collected from digital books system and Moodle system. We described the design and the usage of the system. In the future, we will evaluate the effective of this system.
Friend recommendation service is a common and important demand for the users on various online platforms. Current studies mainly focus on making predictions with the neighborhood and path information derived from the personal relationship networks. However, the formed links do not indicate that two users are familiar with each other nor have intimate connections. Selective treatments are made according...
Algorithm selection has been studied to specify the best possible algorithm(s) for a given problem instance. One of the major drawbacks of the algorithm selection methods is their need for the performance data. The performance data involves the performance of a set of algorithms on a group of problem instances. Depending on the problem domain, algorithms and the experimental settings, generating such...
The collaborative information in horizontal collaborative fuzzy clustering is transmitted by partition matrix, which requires that the dimensions of collaborating partition matrix and collaborated partition matrix must be the same. It requires that the collaborative datasets are clustered into the same number of clusters, but in many cases it is not suitable or difficult to do. In this paper, a new...
Multi-source clustering is common data mining task the aim of which is to use several clustering algorithms to analyze different aspects of the same data. Well known applications of multi-source clustering include horizontal collaborative clustering and multi-view clustering, where several algorithms combine their strengths by exchanging information about their finding on local structures with a goal...
Because of the popularity of Internet and mobile Internet, people are facing serious information overloading problems nowadays. Recommendation engine is very useful to help people to reach the Internet news they want through the network. Collaborative filtering (CF), such as item-based CF, is the most popular branch in recommendation domain. But the data's high-dimension as well as data sparsity are...
Analyzing social iterations in a scientific environment will assist researchers in expanding their collaborative networks. Scientific social networks represent the researchers' social iterations in an academic environment. The analysis of these networks requires a detailed study of their structure and it is important the use of visual resources in order to a better understanding of how the social...
Conventional pedestrian detection methods construct models based on hand-crafted features or deep learning. They are powerful but limited due to finite capabilities of single classifiers. Ensemble models escape these problems by assembling multiple classifiers using some man-made criteria which synthetically utilize information from all combined models. However, these criteria lack theoretical support...
With the rapid growth of the internet and the spread of the information contained therein, the volume of information available on the web is more than the ability of users to manage, capture and keep the information up to date. One solution to this problem are personalization and recommender systems. Recommender systems use the comments of the group of users so that, to help people in that group more...
The increasing volume of information about goods and services has been growing confusion for online buyers in cyberspace and this problem still continues. One of the most important ways to deal with the information overload is using a system called recommender system. The task of a recommender system is to offer the most appropriate and the nearest product to the user's demands and needs. In this...
We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, that...
Based on the advantages of data smoothing, this paper presents a new collaborative filtering algorithm to solve the problem of data sparsity in recommender system. The key innovation of the algorithm consists of clustering and data smoothing. Clustering is used to find out the similarity between users. This paper adopts a new method to cluster users. Data smoothing is designed to solve the problem...
Traditional Collaborative Filtering (CF) based Recommender Systems (RS) typically do not consider the contextual attributes of users or items while making recommendations. However, there are plenty of applications in day to day life, where the choices made by a person may not only depend on his or her earlier preferences, but more on the context. In this work, we incorporate the contextual attributes...
Firstly, according to the Hadoop platform the novel data-analysis architecture is designed, then the paper builds the Item-based clustering collaborative filtering algorithm based on Hadoop. And it takes advantage of the MapReduce parallel programming model to improve the traditional collaborative filtering recommendation algorithm, and resolves the problems of poor system performance of traditional...
In this paper, we present need-based clustering (NbC) with dynamic sink mobility (DSM-NbC) scheme for WSNs using a collaborative compressed sensing approach. The scheme incorporates dynamic sink mobility in a way that mobile sink moves from dense regions towards sparse regions. Intelligently moving the sink to high density regions ensure maximum collection of data. As more number of nodes are able...
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