An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages

• URL: http://arxiv.org/abs/2206.08604v1
• Date: Fri, 17 Jun 2022 07:59:46 GMT
• Title: An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages
• Authors: Lingyue Fu, Jianghao Lin, Weiwen Liu, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu
• Abstract summary: We develop a novel F-shape Click Model to model user behaviors on multi-block mobile pages. We conduct a user eye-tracking study, and identify users' sequential browsing, block skip and comparison patterns on F-shape pages. Experiments on a large-scale real-world dataset validate the effectiveness of FSCM on user behavior predictions.
• Score: 39.63441649184412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To provide click simulation or relevance estimation based on users' implicit interaction feedback, click models have been much studied during recent years. Most click models focus on user behaviors towards a single list. However, with the development of user interface (UI) design, the layout of displayed items on a result page tends to be multi-block (i.e., multi-list) style instead of a single list, which requires different assumptions to model user behaviors more accurately. There exist click models for multi-block pages in desktop contexts, but they cannot be directly applied to mobile scenarios due to different interaction manners, result types and especially multi-block presentation styles. In particular, multi-block mobile pages can normally be decomposed into interleavings of basic vertical blocks and horizontal blocks, thus resulting in typically F-shape forms. To mitigate gaps between desktop and mobile contexts for multi-block pages, we conduct a user eye-tracking study, and identify users' sequential browsing, block skip and comparison patterns on F-shape pages. These findings lead to the design of a novel F-shape Click Model (FSCM), which serves as a general solution to multi-block mobile pages. Firstly, we construct a directed acyclic graph (DAG) for each page, where each item is regarded as a vertex and each edge indicates the user's possible examination flow. Secondly, we propose DAG-structured GRUs and a comparison module to model users' sequential (sequential browsing, block skip) and non-sequential (comparison) behaviors respectively. Finally, we combine GRU states and comparison patterns to perform user click predictions. Experiments on a large-scale real-world dataset validate the effectiveness of FSCM on user behavior predictions compared with baseline models.

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