Accumulative time-based ranking method to reputation evaluation in information networks

TL;DR

This paper introduces an accumulative time-based ranking (ATR) algorithm that evaluates user reputation and item quality over time, improving relevance filtering in information networks by incorporating behavioral weights.

Contribution

The paper proposes a novel ATR algorithm that dynamically updates user reputation and item quality using behavioral weights, enhancing ranking accuracy over existing methods.

Findings

ATR outperforms state-of-the-art algorithms in precision

ATR demonstrates robustness across diverse datasets

Behavioral weighting improves relevance evaluation

Abstract

With the rapid development of modern technology, the Web has become an important platform for users to make friends and acquire information. However, since information on the Web is over-abundant, information filtering becomes a key task for online users to obtain relevant suggestions. As most Websites can be ranked according to users' rating and preferences, relevance to queries, and recency, how to extract the most relevant item from the over-abundant information is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed accumulative time-based ranking (ATR) algorithm, we incorporate two behavioral weighting factors which are updated when users select or rate items, to reflect the evolution of user reputation and item quality over time. We showed that our algorithm outperforms state-of-the-art ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications.