Titlebook: Information Retrieval Technology; 15th Asia Informatio Fu Lee Wang,Haoran Xie,Xiaohui Tao Conference proceedings 2020 Springer Nature Switz

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0302-9743 e conference covers applications, systems, technologies and theory aspects of information retrieval in text, audio, image, video and .multimedia data. .978-3-030-42834-1978-3-030-42835-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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0302-9743 in November 2019..The 14 full papers presented together with 3 short papers were carefully reviewed and selected from 27 submissions. The scope of the conference covers applications, systems, technologies and theory aspects of information retrieval in text, audio, image, video and .multimedia data.

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Cross-Level Matching Model for Information Retrievalces the basic matching signals by allowing terms to match hidden representation states within a sentence. A gating mechanism aggregates the learned matching patterns of different matching channels and outputs a global matching score. Our model provides a simple and effective way for word-phrase matching.

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Unsupervised Answer Retrieval with Data Fusion for Community Question Answeringbest answer flags in the cQA data cannot be utilized, our combination of AR and QR scores with data fusion outperforms a base AR model on average. When best answer flags can be utilized, the retrieval performance can be improved further. While our results lack statistical significance, we discuss ef

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Context-Aware Collaborative Rankingg. In particular, we propose a novel recommendation model termed context-aware collaborative ranking (CCR), which adopts a logistic loss function to measure the predicted error of ranking and exploits the inherent preference context derived from the explicit feedback. Moreover, we design an elegant

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Context-Aware Helpfulness Prediction for Online Product Reviewslutional neural network (CNN) and a context-aware encoding mechanism which can directly capture relationships between words irrespective of their distance in a long sequence. We validated our model on human annotated dataset and the result shows that our model significantly outperforms existing mode