Temporal Social Tagging Based Collaborative Filtering Recommender for Digital Library

Wu, Dengneng; Yuan, Zhigang; Yu, Kai; Pan, Hong

doi:10.1007/978-3-642-34752-8_26

Cited by 10 publications

(2 citation statements)

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“…where W j (τ) is the labeling frequency of the jth tag, τ is time, and α j and β j are parameters of the curve (Wu et al, 2012). Considering that the heat of the same tag for different users may result in different curves, the interest evolution curve is redefined as…”

Section: Tag Optimization Based On the Interest Evolution Modelmentioning

confidence: 99%

A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Sun

et al. 2015

Frontiers Inf Technol Electronic Eng

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Personalized microblog recommendations face challenges of user cold-start problems and the interest evolution of topics. In this paper, we propose a collaborative filtering recommendation algorithm based on a temporal interest evolution model and social tag prediction. Three matrices are first prepared to model the relationship between users, tags, and microblogs. Then the scores of the tags for each microblog are optimized according to the interest evolution model of tags. In addition, to address the user cold-start problem, a social tag prediction algorithm based on community discovery and maximum tag voting is designed to extract candidate tags for users. Finally, the joint probability of a tag for each user is calculated by integrating the Bayes probability on the set of candidate tags, and the top n microblogs with the highest joint probabilities are recommended to the user. Experiments using datasets from the microblog of Sina Weibo showed that our algorithm achieved good recall and precision in terms of both overall and temporal performances. A questionnaire survey proved user satisfaction with recommendation results when the cold-start problem occurred.

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Section: Tag Optimization Based On the Interest Evolution Modelmentioning

confidence: 99%

A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Sun

et al. 2015

Frontiers Inf Technol Electronic Eng

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“…Diversas são as propostas para a implementação de mecanismos de aging em redes de avaliações (DING;LI, 2005;WU et al, 2012;LI et al, 2013;KOWALD et al, 2014).…”

Section: Decaimento Temporal De Avaliaçõesunclassified

Modelo de avaliação de conjuntos de dados científicos por meio da dimensão de veracidade dos dados.

Batista¹

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Science is a social organization: independent collaboration groups work to generate knowledge as a public good. The credibility of the scientific work is entrenched in the evidence that supports it, which includes the applied methodology, the acquired data, the processes to execute the experiments, the data analysis, and the interpretation of the obtained results. The flood of data under which current science is embedded revolutionizes the way surveys are conducted, resulting in a new paradigm of data-driven science. Under such a paradigm, new activities are inserted into the scientific method to organize the process of generation, curation, and publication of data, benefiting the scientific community with the reuse and reproducibility of scientific datasets. In this context, new approaches to problem solving are being presented, obtaining results that previously were considered of relevant difficulty, as well as making possible the generation of new knowledge. Several portals are providing datasets resulting from scientific research. However, such portals do little to address the context upon which datasets are created, making it difficult to understand the data and opening up space for misuse or misinterpretation. In the Big Data area, the dimension that proposes to deal with this aspect is called Veracity. Few studies in the literature approach such a theme, focusing on other dimensions, such as volume, variety, and velocity of data. This research aimed to define a model of collaborative assessment of scientific datasets, through the establishment of an application profile, which standardizes the description of scientific datasets. This standardization of the description is based on the veracity dimension concept, which is defined throughout the research and allows the development of metrics that form the Veracity Index of scientific datasets. This index seeks to reflect the level of detail of a dataset based on the use of the descriptive elements, which will facilitate the reuse and reproducibility of the data. The index is weighted by the evaluation of the scientific community in a collaborative sense, which assess the level of description, comprehension capacity, and suitability of the dataset for a given research or application area. For the proposed collaborative evaluation model, a case study was developed that described a dataset from an international scientific project, the GoAmazon project, in order to validate the proposed model among the peers, demonstrating the potential of the solution in the reuse and reproducibility of datasets, showing that such an index can be incorporated into scientific data portals.

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Enhancing User Rating Database Consistency Through Pruning

Margaris¹,

Vassilakis

2017

Transactions on Large-Scale Data- And Knowledge-Centered Systems XXXIV

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Temporal Social Tagging Based Collaborative Filtering Recommender for Digital Library

Cited by 10 publications

References 10 publications

A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Modelo de avaliação de conjuntos de dados científicos por meio da dimensão de veracidade dos dados.

Enhancing User Rating Database Consistency Through Pruning

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Temporal Social Tagging Based Collaborative Filtering Recommender for Digital Library

Cited by 10 publications

References 10 publications

A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Modelo de avaliação de conjuntos de dados cientí­ficos por meio da dimensão de veracidade dos dados.

Enhancing User Rating Database Consistency Through Pruning

Contact Info

Product

Resources

About

Modelo de avaliação de conjuntos de dados científicos por meio da dimensão de veracidade dos dados.