Matches in ESWC 2020 for { ?s ?p ?o. }
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- Author.251.1 label "Vito Walter Anelli, 1st Author for Paper 251".
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- Felice_Antonio_Merra type Person.
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- Rigo_Wenning name "Rigo Wenning".
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- Paper.251_Review.0 issued "2001-01-13T13:41:00.000Z".
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- Paper.251_Review.0 hasContent "A solid paper targeting a typical research situation. The description of the state of the art is complete and well written. The idea of exploiting knowledge to improve shilling attacks is original. The description of the concept and the work done is comprehensible and coherent. With some missing articles, there is a slight sloppiness in the English writing. This makes a solid experiment with a solid outcome. This is the reason for accepting the paper. What I'm missing though is what that actually means. Are the differences of using the attacks big enough to make a difference e.g. in Social media and political campaigns? What would be defensive strategies in RS using the knowledge based approach? Is there any relation between how intelligent the CF-RS is and how much this affects the change from ShA to SAShA? Knowing a system and knowing the similarity to the target items, couldn't the system avoid the random items and only concentrate only on similar items without revealing the target item? It is one thing to target the improvement of state of the art attacks, but with this background, it may be worthwhile to explore new attacks based on semantic knowledge. In this case, the challenge is discoverability."".
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- Paper.251_Review.1 issued "2001-01-15T16:21:00.000Z".
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- Paper.251_Review.1 hasContent "This paper proposed a semantic-aware method for attacking collaborative filtering recommendation models, named SAShA and investigated the impact of publicly available knowledge graph data to generate fake profiles. Novelty: The novelty of this works comes from exploiting publicly available semantic information to develop more effective shilling attack strategies against CF models in terms of overall prediction shift and overall hit ratio. Soundness: Experiments have been designed, conducted and analysed rigorous, convincing, and support the stated claims. The study evaluated SAShA on two real-world datasets by extending three baseline Shilling attacks considering different semantic types of features. In detail, they have extended random, love-hate and average attacks by considering Ontological, Categorical and Factual Knowledge Graph features extracted from DBpedia. Design and execution of the evaluation of the work: This research performed an extensive experimental evaluation in order to investigate whether SAShA is more effective than baseline attacks against Collaborative Filtering models by taking into account the impact of various semantic features. Experimental results evaluated on two real-world datasets to show the usefulness of the proposed strategy . Clarity and quality of presentation: The paper has been written clearly and has been organised in a very good structure. Grounding in the literature: Comprehensive literature review of related works has been provided and the differences between them with this research has been discussed appropriately. Appropriateness: This paper contributes to addressing theoretical, analytical, and empirical aspects of using Semantic Web in recommendation models. Overall Evaluation: I really enjoyed reading this paper and I vote to accept this paper. (3: strong accept)"".
- Paper.254 type SubmissionsPaper.
- Paper.254 label "CompeTics: Visualization and Analysis of Scientific Competences from Big Bibliographic Data with NoSQL".
- Paper.254 title "CompeTics: Visualization and Analysis of Scientific Competences from Big Bibliographic Data with NoSQL".
- Paper.254 issued "2001-12-05T11:19:00.000Z".