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Existing personalized recommendation systems are facing many problems such as cold start, data sparseness and high complexity. Users' interests exist more widely and are more personalized compared with purchasing history in traditional recommendation systems. Thus, applying the interest graph in the recommendation process can make up certain shortages. This paper builds the mechanism of a user-interest-goods...
The reduced cost of the next generation sequencing technologies provides opportunities to study non-model organisms. However, one challenge is the large volume of data generated and, thus, the need to use automated approaches to annotate these data. Machine learning algorithms could provide a cost-effective solution but they need lots of labeled data and informative features to represent these data...
Predictability of home energy usage forms the basis of many home energy management and demand-response systems. While existing studies focus on designing more accurate prediction algorithms, a comprehensive energy management solution requires a broad understanding of prediction accuracy at different granularities, for example appliance and home, as well as different time horizons, for example an hour,...
Appearing social networks these days, the capacity of produced information has an increasing growing. The usual learning techniques don't have an efficient performance and the need of utilizing increasing learning methods is seen as a necessary factor. In mining the text in social networks we can see that text mining and social analyzing in texts are new topics in data analyzing which are considered...
Appearing social networks these days, the capacity of produced information has an increasing growing. The usual learning techniques don't have an efficient performance and the need of utilizing increasing learning methods is seen as a necessary factor. In mining the text in social networks we can see that text mining and social analyzing in texts are new topics in data analyzing which are considered...
Original K-medoid algorithm use to take initial medoids arbitrarily that bears on the resulting clusters and it leads to unstable and empty clusters which are no meaningful and also amount of iterations can be rather high so K-Medoid is not a substitute for big databases because of its computational complexity. Also the original k-means algorithm is computationally. Though existing algorithms usually...
In this paper we address prediction of the communication link quality for Vehicle-to-Vehicle (V2V) applications. We focus on the prediction at the receiver vehicle and suggest two novel frameworks, which allow real-time and short-term prediction whether a predefined application-specific QoS will be maintained in the near future. First framework makes use of machine learning approach, and the second...
Collaborative filtering (CF) predicts user preferences in item selection based on the known user ratings of items. As one of the most common approach to recommender systems, CF has been proved to be effective for solving the information overload problem. CF can be divided into two main branches: memory-based and model-based. Most of the present researches improve the accuracy of Memory-based algorithms...
This paper aims on collaborative filtering (CF) in TV recommendation system which combines content-based and collaborative filtering recommendation mechanism, we propose an algorithm that using the self-organizing mapping (SOM) to optimize the improved k-means (IK) clustering in collaborative filtering. The whole clustering algorithm is divided into two phases: at the first stage, the quantity of...
Data stored in educational database is increasing day by day. Data mining algorithms can be used to find hidden patterns from the student's database. These patterns can be used to find academic performance of students. The main aim of this study was to determine factors that influence the student's performance. This paper proposes Generalized Sequential Pattern mining algorithm for finding frequent...
The high latency nature of mobile network limited the use of mobile cloud storage, especially for these applications with frequent random accesses to a large set of small files. Prefetching techniques are usually utilized to overcome the latency problem, but most existing prefetching algorithm cannot prefetch enough data to alleviate the negative effect of high latency in a random access sequence...
Mixed Fruit Fly Optimization Algorithm LGM-FOA (Logistic Mapping-FOA) is an improved mixed fruit fly algorithm on the basis of the Logistic map, but the algorithm was showing an ideal state about convergence precision and stability in the optimization process, because there are three discontinuous points from the Logistic map. To solve this problem, the author proposed a new mixed fruit fly algorithm...
Traditional recommender systems use collaborative filtering or content-based methods to recommend new items for users. New users and items are continuously updated to the system bringing changes in user's preferences, as well as the additional context in form of temporal information. The continuous system updates change not just individual user's preferences, but also group user's preferences affecting...
A waybill is a document that accompanies the freight during transportation. The document contains essential information such as, origin and destination of the freight, involved actors, and the type of freight being transported. We believe, the information from a waybill, when presented in an electronic format, can be utilized for building knowledge about the freight movement. The knowledge may be...
In this paper, we study the learning mechanisms that facilitate autonomous discovery of an effective affordance prediction structure with multiple actions of different levels of complexity. A robot can benefit from a hierarchical structure where pre-learned basic affordances are used as inputs to bootstrap learning of complex affordances. In a developmental setting, links from basic affordances to...
Collaborative filtering (CF) is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it suffers from several inherent deficiencies such as data sparsity and cold start. To better show user preferences for the cold users additional information (e.g., trust) is often applied. We describe the stages based on which the ratings of an active...
Local features have been widely used in visual object tracking for their robustness in illumination, deformation, rotation and partial occlusion. Traditional feature selection algorithms based on accumulated knowledge of previous frames usually adopt the perspective of continuity of changes, which could lead to degradation. Exploiting discrimination and uniqueness of local sub-blocks, we build an...
Efficiency of general classification models in various problems is different according to the characteristics and the space of the problem. Even in a particular issue, it may not be distinguished a special privilege for a classifier method than the others. Ensemble classifier methods aim to combine the results of several classifiers to cover the deficiency of each classifier by others. This combination...
Recommender system is a solution to the information overload problem in websites that allow users to express their interests about items. Collaborative filtering is one of the most important methods in recommender systems which predicts ratings for active user based on opinions and interests of other users who are similar to the active user. Accuracy of ratings prediction can be considerably improved...
In petrochemical industries, one of the most concerned problems is the leaking of toxic gas. Once leaking occurs, the safety of equipments located in production site is greatly threatened, thereby affecting surrounding environment. In order to solve this problem, it is necessary to predict the possible location of leak points from sensors which are located in gas pipe. On the other hand, data from...
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