Learning temporal-dependent ranking models

Miguel Costa, Francisco M. Couto, Mário J. Silva

Abstract: Web archives already hold together more than 534 billion files and this number continues to grow as new initiatives arise. Searching on all versions of these files acquired throughout time is challenging, since users expect as fast and precise answers from web archives as the ones provided by current web search engines. This work studies, for the first time, how to improve the search effectiveness of web archives, including the creation of novel temporal features that explore the correlation found between web document persistence and relevance. The persistence was analyzed over 14 years of web snapshots. Additionally, we propose a temporal-dependent ranking framework that exploits the variance of web characteristics over time influencing ranking models. Based on the assumption that closer periods are more likely to hold similar web characteristics, our framework learns multiple models simultaneously, each tuned for a specific period. Experimental results show significant improvements over the search effectiveness of single-models that learn from all data independently of its time. Thus, our approach represents an important step forward on the state-of-the-art IR technology usually employed in web archives.