Matches in ESWC 2020 for { ?s ?p 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)". }
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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)"".