CARL: Aggregated Search with Context-Aware Module Embedding Learning
X Huang, J Qi, Y Sun, R Zhang, HT Zheng
2019 International Joint Conference on Neural Networks (IJCNN) | IEEE | Published : 2019
Aggregated search aims to construct search result pages (SERPs) from blue-links and heterogeneous modules (such as news, images, and videos). Existing studies have largely ignored the correlations between blue-links and heterogeneous modules when selecting the heterogeneous modules to be presented. We observe that the top ranked blue-links, which we refer to as the context, can provide important information about query intent and helps identify the relevant heterogeneous modules. For example, informative terms like "streamed" and "recorded" in the context imply that a video module may better satisfy the query. To model and utilize the context information for aggregated search, we propose a m..View full abstract
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Awarded by Australian Research Council (ARC)
We would like to thank Xiaojie Wang for his help. This work is supported by China Scholarship Council (CSC) Grant #201808240008, Australian Research Council (ARC) Discovery Project DP180102050, Overseas Cooperation Research Fund of Graduate School at Shenzhen, Tsinghua University (Grant No. HW2018002).