Top-K representative documents query over geo-textual data stream

The increasing popularity of location-based social networks encourages more and more users to share their experiences. It deeply impacts the decision of customers when shopping, traveling, and so on. This paper studies the problem of top-K valuable documents query over geo-textual data stream. Many researchers have studied this problem. However, they do not consider the reliability of documents, where some unreliable documents may mislead customers to make improper decisions. In addition, they lack the ability to prune documents with low representativeness. In order to increase user satisfaction in recommendation systems, we propose a novel framework named PDS. It first employs an efficiently machine learning technique named ELM to prune unreliable documents, and then uses a novel index named \(\mathcal {GH}\) to maintain documents. For one thing, this index maintains a group of pruning values to filter low quality documents. For another, it utilizes the unique property of sliding window to further enhance the PDS performance. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the proposed algorithms.