A Personalized URL Re-ranking Method using Psychological User Browsing Characteristics
Jason J. Jung, Sanggil Kang, Shohel Ahmed, Sungjoon Park · Zenodo (CERN European Organization for Nuclear Research) · 2020
This paper proposes a personalized URL re-ranking method based on psychological characteristics of users browsing. The characteristics are classified into three groups, which are "common-mind," "uncommon-mind," and "extremely uncommonmind." Our personalization method constructs an index of the anchor text retrieved from the web pages that the user has clicked during his/her past searches. Our method provides different weights to the anchor text according to the psychological characteristics for re-ranking URLs. In the experimental section, we show that our method can provide better performance than Google and another web personalization method in terms of the average rank.