Full Paper (8 pages)
Official ACM published version: http://doi.acm.org/10.1145/1835449.1835471
Author's version: PDF (232KB)
The collective contributions of billions of users across the globe each day result in an ever-changing web. In verticals like news and real-time search, recency is an obvious significant factor for ranking. However, traditional link-based web ranking algorithms typically run on a single web snapshot without concern for user activities associated with the dynamics of web pages and links. Therefore, a stale page popular many years ago may still achieve a high authority score due to its accumulated in-links. To remedy this situation, we propose a temporal web link-based ranking scheme, which incorporates features from historical author activities. We quantify web page freshness over time from page and in-link activity, and design a web surfer model that incorporates web freshness, based on a temporal web graph composed of multiple web snapshots at different time points. It includes authority propagation among snapshots, enabling link structures at distinct time points to influence each other when estimating web page authority. Experiments on a real-world archival web corpus show our approach improves upon PageRank in both relevance and freshness of the search results.
In Proceedings of the 33rd Annual ACM SIGIR Conference on Research and Development in Information Retrieval, pages 114-121, Geneva, Switzerland, ACM Press, July 2010.
© ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
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