Learning to Represent Human Motives for Goal-directed Web Browsing
This addresses the challenge of improving web browsing experiences by incorporating psychological motives, though it appears incremental as it builds on existing taxonomies and neural methods.
The paper tackles the problem of leveraging unobserved higher-ordered human goals to assist web browsing by proposing the Goal-directed Web Browsing (GoWeB) framework, which significantly outperforms baselines in tasks like web page recommendation and re-visitation classification on large-scale Microsoft Edge data.
Motives or goals are recognized in psychology literature as the most fundamental drive that explains and predicts why people do what they do, including when they browse the web. Although providing enormous value, these higher-ordered goals are often unobserved, and little is known about how to leverage such goals to assist people's browsing activities. This paper proposes to take a new approach to address this problem, which is fulfilled through a novel neural framework, Goal-directed Web Browsing (GoWeB). We adopt a psychologically-sound taxonomy of higher-ordered goals and learn to build their representations in a structure-preserving manner. Then we incorporate the resulting representations for enhancing the experiences of common activities people perform on the web. Experiments on large-scale data from Microsoft Edge web browser show that GoWeB significantly outperforms competitive baselines for in-session web page recommendation, re-visitation classification, and goal-based web page grouping. A follow-up analysis further characterizes how the variety of human motives can affect the difference observed in human behavioral patterns.