Improving diversity in Web search results re-ranking using absorbing random walks

Search result diversification has become important for improving Web search effectiveness and user satisfaction, as redundancy in top ranking results often disappoints users. To solve this problem, many techniques have been proposed to make a tradeoff between the relevance and diversity. Among them, GRASSHOPPER which utilizes the framework of absorbing random walks has shown good performance. In this paper, we propose a novel algorithm named DATAR with a new ranking strategy, which improves the diversification ability of GRASSHOPPER. Also, we make a discussion on the reason why DATAR is better. We evaluated the proposed algorithm with a public dataset ODP239 and a real search result dataset collected from Google. The experiment results show that the proposed DATAR algorithm outperforms GRASSHOPPER in improving diversity in Web search results re-ranking.