Region-action LSTM for mouse interaction sequence based search satisfaction evaluation. Issue 6 (November 2020)

• Record Type: Journal Article
• Authors: Chen, Ling; Fan, Alin; Shi, Hongyu; Chen, Gencai
• Is Part Of: Information processing & management. Volume 57:Issue 6(2020:Nov.)
• Publication Date: 2020-11
• Subjects: Search satisfaction evaluation -- Mouse interaction sequence -- Region-action LSTM -- Multi-factor perturbation; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2020.102349
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: Mouse interaction data contain a lot of interaction information between users and Search Engine Result Pages (SERPs), which can be useful for evaluating search satisfaction. Existing studies use aggregated features or anchor elements to capture the spatial information in mouse interaction data, which might lose valuable mouse cursor movement patterns for estimating search satisfaction. In this paper, we leverage regions together with actions to extract sequences from mouse interaction data. Using regions to capture the spatial information in mouse interaction data would reserve more details of the interaction processes between users and SERPs. To modeling mouse interaction sequences for search satisfaction evaluation, we propose a novel LSTM unit called Region-Action LSTM (RALSTM), which could capture the interactive relations between regions and actions without subjecting the network to higher training complexity. Simultaneously, we propose a data augmentation strategy Multi-Factor Perturbation (MFP) to increase the pattern variations on mouse interaction sequences. We evaluate the proposed approach on open datasets. The experimental results show that the proposed approach achieves significant performance improvement compared with the state-of-the-art search satisfaction evaluation approach.