Background: With the increased use of ontologies and ontology mappings in semantically-enabled applications such as ontology-based search and data integration, the issue of detecting and repairing defects in ontologie...
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Background: With the increased use of ontologies and ontology mappings in semantically-enabled applications such as ontology-based search and data integration, the issue of detecting and repairing defects in ontologies and ontology mappings has become increasingly important. These defects can lead to wrong or incomplete results for the applications. Results: We propose a unified framework for debugging the is-a structure of and mappings between taxonomies, the most used kind of ontologies. We present theory and algorithms as well as an implemented system RepOSE, that supports a domain expert in detecting and repairing missing and wrong is-a relations and mappings. We also discuss two experiments performed by domain experts: an experiment on the Anatomy ontologies from the Ontology Alignment Evaluation Initiative, and a debugging session for the Swedish National Food Agency. Conclusions: Semantically-enabled applications need high quality ontologies and ontology mappings. One key aspect is the detection and removal of defects in the ontologies and ontology mappings. Our system RepOSE provides an environment that supports domain experts to deal with this issue. We have shown the usefulness of the approach in two experiments by detecting and repairing circa 200 and 30 defects, respectively.
In the framework of Data Vitalization, data has inner connection. In this paper, we apply it to recommendation system of Social Networking Services. With the development of Social Networking Services, the research and...
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ISBN:
(纸本)9781467329644;9781467329637
In the framework of Data Vitalization, data has inner connection. In this paper, we apply it to recommendation system of Social Networking Services. With the development of Social Networking Services, the research and application of SNS flourish. In SNS, recommendation for users is a significant feature. Recommending items that will be accepted by users is a key point. Considering the feature of SNS data, traditional recommendation based-on content or based-on collaborative filtering can't be fully applied to SNS. In this paper, we combine recommendation based-on content with that based-on collaborative filtering. And propose the concept of user intimacy based on the interaction between users. With these three aspects, we construct a hybrid recommendation model. Experiment is conduct to show advantage of the model in accuracy and verify its practicality.
Recent advancements in cloud computing allow smart phone provide a variety of movable services. This paper proposed a design of service, including human-centred recommendation service and travel-gaming service. The Sm...
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Recent advancements in cloud computing allow smart phone provide a variety of movable services. This paper proposed a design of service, including human-centred recommendation service and travel-gaming service. The Smart-Travel System (STS) aids traveller with personal requires to tour by smart phone. STS provides personalised travelling real-time information, and automatically tells traveller when they show up around the task, which providing by government or local store, to make the trip like a game mission for user. No matter if the traveller deviated from the planned route or not, the STS could provide the new trip itineraries and allows the share on social network in real-time from any computer. According to the cloud-based service, STS is a new way to look up the travel information. The STS provides a whole new experience in travelling for mobile phone users via smart phone, GPS, Google map and Augmented Realty.
online shopping is becoming increasingly popular;two competing companies may also cooperate in certain areas in order to gain more *** example,two e-commerce sites hope to provide users with better recommendation,the ...
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online shopping is becoming increasingly popular;two competing companies may also cooperate in certain areas in order to gain more *** example,two e-commerce sites hope to provide users with better recommendation,the traditional approach is that both sides share each other's ratings ***,this way can’t protect privacy of both *** paper improves recommendation algorithm for the privacy concerns of Two-Party to the specified item rating prediction in the case of collaborative computation based on the algorithm *** is a more effective way to the application of privacy- preserving of collaborative computation involved two-party in practice.
A large number of collaborative filtering algorithms have been proposed in the literature as the foundation of automated recommender systems. However, the underlying justification for these algorithms is lacking, and ...
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A large number of collaborative filtering algorithms have been proposed in the literature as the foundation of automated recommender systems. However, the underlying justification for these algorithms is lacking, and their relative performances are typically domain and data dependent. In this paper, we aim to develop initial understanding of the recommendation model/algorithm validation and selection issues based on the graph topological modeling methodology. By representing the input data in the form of consumer-product interactions as a bipartite graph, the consumer-product graph, we develop bipartite graph topological measures to capture patterns that exist in the input data relevant to the transaction-based recommendation task. We observe the deviations of these topological measures of real-world consumer-product graphs from the expected values for simulated random bipartite graphs. These deviations help explain why certain collaborative filtering algorithms work for particular recommendation data sets. They can also serve as the basis for a comprehensive model selection framework that "recommends" appropriate collaborative filtering algorithms given characteristics of the data set under study. We validate our approach using three real-world recommendation data sets and demonstrate the effectiveness of the proposed bipartite graph topological measures in selection and validation of commonly used heuristic-based recommendation algorithms, the user-based, item-based, and graph-based algorithms.
in this paper, through the study of the existing collaborative filtering technologies and the e-commerce recommendation system concepts, architecture and the main recommendation technology and development, an improved...
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ISBN:
(纸本)9781424455379
in this paper, through the study of the existing collaborative filtering technologies and the e-commerce recommendation system concepts, architecture and the main recommendation technology and development, an improved collaborative filtering algorithm is designed and implemented. This algorithm can solve the scalability problem general collaborative filtering algorithms can not solve to some extent, thus the recommendation quality can be greatly improved.
The phenomenal success of social networking sites, such as Facebook, Twitter and LinkedIn, has revolutionized the way people communicate. This paradigm has attracted the attention of researchers that wish to study the...
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ISBN:
(纸本)9780769541389
The phenomenal success of social networking sites, such as Facebook, Twitter and LinkedIn, has revolutionized the way people communicate. This paradigm has attracted the attention of researchers that wish to study the corresponding social and technological problems. Link recommendation is a critical task that not only helps increase the linkage inside the network and also improves the user experience. In an effective link recommendation algorithm it is essential to identify the factors that influence link creation. This paper enumerates several of these intuitive criteria and proposes an approach which satisfies these factors. This approach estimates link relevance by using random walk algorithm on an augmented social graph with both attribute and structure information. The global and local influences of the attributes are leveraged in the framework as well. Other than link recommendation, our framework can also rank the attributes in the network. Experiments on DBLP and IMDB data sets demonstrate that our method outperforms state-of-the-art methods for link recommendation.
Ms recommendation algorithm based on User-Item Rating Matrix is inefficient m the case of cold-start. The Application of Multi-Attribute Rating Matrix (MARM) can solve the problem effectively. The user and item inform...
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To improve the quality of 3G service recommending technology, a new 3G service recommendation algorithm was prompted based on clustering analysis and collaborative filtering. The algorithm can cluster the users with t...
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ISBN:
(纸本)9781424473281
To improve the quality of 3G service recommending technology, a new 3G service recommendation algorithm was prompted based on clustering analysis and collaborative filtering. The algorithm can cluster the users with their behavior similarity to the commodities, and finds the nearest neighbor of an active user according to the clusters. Then the recommendation to the active user is produced by collaborative filtering. Experimental results show that the algorithm improves the performance of recommendation system and decreases the mean absolute error of 3G services system.
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