Titlebook: Experimental IR Meets Multilinguality, Multimodality, and Interaction; 7th International Co Norbert Fuhr,Paulo Quaresma,Nicola Ferro Confer

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Random Performance Differences Between Online Recommender System Algorithms a given quality measure and dataset. Validity of the evaluation depends on the assumption that the evaluation does not exhibit artefacts resulting from the process of collecting the dataset. The main difference between online and offline evaluation is that in the online setting, the user’s response

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Chris Park,Gwendolyn E. Kaeser,Jerold Chune parliament. Our main findings are the following. First, we define hierarchical significant words language models as an iterative estimation process across the hierarchy, resulting in tiny models capturing only well grounded text features at each level. Second, we apply the resulting models to part

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Vijay Rani Rajpal,Deepmala Sehgal,S.N. Rainay the set of features appropriate for our problem and to evaluate the effectiveness of our proposed approach. Our results demonstrate both the accuracy of our classification approach in predicting suitable contextual aspects for a venue, and its effectiveness at making better venue recommendations t

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Priyanka Anjoy,Kuldeep Kumar,Kishor Gaikwadned by personal phrases considerably outperformed those from non-personal sentences, indicating their greater suitability for the AP task. We consider these findings could be further applied in the design of strategies for the construction of AP corpora, novel feature selection methods, as well as n

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Natalie Stickle MSc,Neil Winegarden MSced the visual representation of images to learn a feature model, in this way we can match query images with words by simply measuring visual similarity. Second, a query is formed with the retrieved words and candidate captions are retrieved from a reference dataset of sentences. Despite the simplici