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| Predicting Literature Download Behavior by Integrating Features of Behavioral Sequences of Academic Users |
| Zhang Xiaojuan1, Guo Jiarun1, Yang Shihan1, Gui Sisi2 |
1.School of Public Administration, Sichuan University, Chengdu 610065
2.College of Information Management, Nanjing Agricultural University, Nanjing 210095 |
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Abstract In academic search systems, using historical search behavior to predict both the quantity of literature an academic user may need and the timing of that need within a given time period will help improve user satisfaction with literature recommendations. To improve the prediction accuracy of the download behaviors of academic users, the number of downloads in the next download session and the time gap until the next download session are evaluated by mining various behavioral sequence features of academic users. First, this study transforms the problem of predicting user download behavior into a time series prediction problem. Subsequently, based on the mining of the behavioral features from three perspectives, namely the query reformulation behaviors, query expressions, and download behaviors of users, the long short-term memory (LSTM) model is used to model user historical sessions as a time series to predict the download behavior. Finally, a comparative analysis of the predictive performance between the features proposed in the article and those proposed in existing research is conducted, and the predictive performances of different sets of features and individual features are explored. The features proposed in this study can improve the accuracy of prediction tasks. By clustering different users, the LSTM models that were trained on different clusters displayed the best overall predictive performance. Among all feature sets, the query expression-based features achieved the best prediction performance for the number of downloads in the next download session, and the download behavior-based features exhibited the most outstanding performance gain for the prediction of the time interval until the next download session. Owing to the limited availability of more public log datasets on academic user search behavior, this study conducted experimental validation on one dataset. Hence, the insufficient user behavior data provided by the dataset logs poses a limitation to the feature engineering approach employed in this study.
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Received: 07 June 2024
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