Learn More
Some commercial web search engines rely on sophisticated machine learning systems for ranking web documents. Due to very large collection sizes and tight constraints on query response times, online efficiency of these learning systems forms a bottleneck. An important problem in such systems is to speedup the ranking process without sacrificing much from the(More)
We study the problem of web search result diversification in the case where intent based relevance scores are available. A diversified search result will hopefully satisfy the information need of users who may have different intents. In this context, we first analyze the properties of an intent-based metric, ERR-IA, to measure relevance and diversity(More)
In web search, recency ranking refers to ranking documents by relevance which takes freshness into account. In this paper, we propose a retrieval system which automatically detects and responds to recency sensitive queries. The system detects recency sensitive queries using a high precision classifier. The system responds to recency sensitive queries by(More)
We propose a new model to interpret the clickthrough logs of a web search engine. This model is based on explicit assumptions on the user behavior. In particular, we draw conclusions on a document relevance by observing the user behavior after he examined the document and not based on whether a user clicks or not a document url. This results in a model(More)
It is now widely recognized that user interactions with search results can provide substantial relevance information on the documents displayed in the search results. In this paper, we focus on extracting relevance information from one source of user interactions, i.e., user click data, which records the sequence of documents being clicked and not clicked(More)
Traditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance of documents for a given query. Although such a batch-learning framework has been tremendously successful in commercial search engines, in scenarios where relevance of documents to a query changes over time, such as ranking recent documents(More)
Recency ranking refers to the ranking of web results by accounting for both relevance and freshness. This is particularly important for " recency sensitive " queries such as breaking news queries. In this study, we propose a set of novel click features to improve machine learned recency ranking. Rather than computing simple aggregate click through rates, we(More)
We explore the potential of using users click-through logs where no editorial judgment is available to improve the ranking function of a vertical search engine. We base our analysis on the <i>Cumulate Relevance Model</i>, a user behavior model recently proposed as a way to extract relevance signal from click-through logs. We propose a novel way of directly(More)
Oracle's objective in TREC-10 was to study the behavior of Oracle information retrieval in previously unex-plored application areas. The software used was Oracle9i Text[1], Oracle's full-text retrieval engine integrated with the Oracle relational database management system, and the Oracle PL/SQL procedural programming language. Runs were submitted in(More)
Web search ranking functions are typically learned to rank search results based on features of individual documents, i.e., pointwise features. Hence, the rich relationships among documents, which contain multiple types of useful information, are either totally ignored or just explored very limitedly. In this paper, we propose to explore <i>multiple</i>(More)