114 research outputs found

    A User Behavior Based Study on Search Engine Ranking

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    In this era of information explosion, finding convenient ways to get the desired information is becoming ever more vital today. With a review of the existing information retrieval and feedback technology, this paper puts forward a method to establish and update user profile model through obtaining user’s implicit feedbacks. The user’s explicit information is not a must. Instead, this method, with the implicit information acquired by observing the behaviors of the users when browsing web pages, establishes and updates the user profile model and thus reduces the workload.Keywords: Information retrieval?Implicit feedback?Relevance feedback; User profile mode

    Novel Modelling Strategies for High-frequency Stock Trading Data

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    Full electronic automation in stock exchanges has recently become popular, generating high-frequency intraday data and motivating the development of near real-time price forecasting methods. Machine learning algorithms are widely applied to mid-price stock predictions. Processing raw data as inputs for prediction models (e.g., data thinning and feature engineering) can primarily affect the performance of the prediction methods. However, researchers rarely discuss this topic. This motivated us to propose three novel modelling strategies for processing raw data. We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks. In these experiments, our strategies often lead to statistically significant improvement in predictions. The three strategies improve the F1 scores of the SVM models by 0.056, 0.087, and 0.016, respectively.Comment: 28 pages, 5 tables, 5 figure
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