摘要 :
The design and evaluation of tag recommendation methods have focused only on relevance. However, other aspects such as novelty and diversity may be as important to evaluate the usefulness of the recommendations. In this work, we d...
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The design and evaluation of tag recommendation methods have focused only on relevance. However, other aspects such as novelty and diversity may be as important to evaluate the usefulness of the recommendations. In this work, we define these two aspects in the context of tag recommendation and propose a novel recommendation strategy that considers them jointly with relevance. This strategy extends a state-ofthe- art method based on Genetic Programming to include novelty and diversity metrics both as attributes and as part of the objective function. We evaluate the proposed strategy using data collected from 3 popular Web 2.0 applications: LastFM, YouTube and YahooVideo. Our experiments show that our strategy outperforms the state-of-the-art alternative in terms of novelty and diversity, without harming relevance.
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摘要 :
Researchers who start delving into a new research area often resort to reading lists of scientific articles in order to familiarize themselves with the existing literature. Generally, these reading lists are manually built and rec...
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Researchers who start delving into a new research area often resort to reading lists of scientific articles in order to familiarize themselves with the existing literature. Generally, these reading lists are manually built and recommended by experts. In this paper we tackle the problem of automatically generating these lists. We start by stating five requirements for a comprehensive initial reading list, four of which were previously proposed and one is a contribution of ours. We then assess the extent to which these requirements are redundant or complementary. By performing a correlation analysis on a large dataset, we find that the five requirements are indeed mostly conflicting, which suggests that simultaneously meeting all of them is a difficult task. We then perform an extensive set of experiments, comparing twenty-five different approaches to automatically generate initial reading lists, most of which are new proposals of ours which exploit learning to rank (L2R) and aggregation methods to combine multiple pieces of evidence and objectives. Our experimental results indicate that, though no method outperforms the others in all five requirements, our new L2R and aggregation methods significantly outperform the state-of-the-art when jointly considering all requirements. Moreover, we identify a subset of six new techniques which offer the best tradeoff (in a Pareto-efficient sense) across all five requirements.
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摘要 :
Researchers who start delving into a new research area often resort to reading lists of scientific articles in order to familiarize themselves with the existing literature. Generally, these reading lists are manually built and rec...
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Researchers who start delving into a new research area often resort to reading lists of scientific articles in order to familiarize themselves with the existing literature. Generally, these reading lists are manually built and recommended by experts. In this paper we tackle the problem of automatically generating these lists. We start by stating five requirements for a comprehensive initial reading list, four of which were previously proposed and one is a contribution of ours. We then assess the extent to which these requirements are redundant or complementary. By performing a correlation analysis on a large dataset, we find that the five requirements are indeed mostly conflicting, which suggests that simultaneously meeting all of them is a difficult task. We then perform an extensive set of experiments, comparing twenty-five different approaches to automatically generate initial reading lists, most of which are new proposals of ours which exploit learning to rank (L2R) and aggregation methods to combine multiple pieces of evidence and objectives. Our experimental results indicate that, though no method outperforms the others in all five requirements, our new L2R and aggregation methods significantly outperform the state-of-the-art when jointly considering all requirements. Moreover, we identify a subset of six new techniques which offer the best tradeoff (in a Pareto-efficient sense) across all five requirements.
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摘要 :
Tags tornaram-se bastante populares na Web. Elas tipicamente possibilitam a organiza??o do conteúdo, provendo boas descri??es e refletindo os interesses dos usuários. Tags também representam uma boa fonte de dados para servi?os...
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Tags tornaram-se bastante populares na Web. Elas tipicamente possibilitam a organiza??o do conteúdo, provendo boas descri??es e refletindo os interesses dos usuários. Tags também representam uma boa fonte de dados para servi?os de Recupera??o de Informa??o, particularmente para mídias ricas (imagem, áudio, vídeo) . Nesse contexto, servi?os de recomenda??o de tags objetivam sugerir tags relevantes e úteis para o usuário, ajudando-o na tarefa de descrever o conteúdo e melhorando a qualidade das tags geradas, de forma que elas descrevam o conteúdo de forma mais precisa e completa.
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摘要 :
Tags tornaram-se bastante populares na Web. Elas tipicamente possibilitam a organização do conteúdo, provendo boas descrições e refletindo os interesses dos usuários. Tags também representam uma boa fonte de dados para serv...
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Tags tornaram-se bastante populares na Web. Elas tipicamente possibilitam a organização do conteúdo, provendo boas descrições e refletindo os interesses dos usuários. Tags também representam uma boa fonte de dados para serviços de Recuperação de Informação, particularmente para mídias ricas (imagem, áudio, vídeo) . Nesse contexto, serviços de recomendação de tags objetivam sugerir tags relevantes e úteis para o usuário, ajudando-o na tarefa de descrever o conteúdo e melhorando a qualidade das tags geradas, de forma que elas descrevam o conteúdo de forma mais precisa e completa.
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