Learning to Represent Human Motives for Goal-directed Web Browsing

TL;DR

This paper introduces GoWeB, a neural framework that models human motives to improve web browsing experiences, demonstrating significant performance gains in recommendation, re-visitation, and grouping tasks based on large-scale browser data.

Contribution

It presents a novel goal representation framework based on psychological taxonomy, enhancing web activity modeling and user experience in goal-directed browsing.

Findings

GoWeB outperforms baselines in web page recommendation.

It improves re-visitation classification accuracy.

It enables effective goal-based web page grouping.

Abstract

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.