Exhaustive evaluation of ranked queries can be expensive, particularly when only a small subset of the overall ranking is required, or when queries contain common terms. This concern gives rise to techniques for dynamic query pruning, that is, methods for eliminating redundant parts of the usual exhaustive evaluation, yet still generating a demonstrably “good enough ” set of answers to the query. In this work we propose new pruning methods that make use of impact-sorted indexes. Compared to exhaustive evaluation, the new methods reduce the amount of computation performed, reduce the amount of memory required for accumulators, reduce the amount of data transferred from disk, and at the same time allow performance guarantees in terms of precision and mean average precision. These strong claims are backed by experiments using the TREC Terabyte collection and queries. Categories and Subject Descriptors H.3.1 [Information Storage and Retrieval]: Content analysis and indexing – indexing methods; H.3.2 [Information Storage and Retrieval]:

The business of Web search, a $10 billion industry, relies heavily on sponsored search, whereas a few carefully-selected paid advertisements are displayed alongside algorithmic search results. A key technical challenge in sponsored search is to select ads that are relevant for the user’s query. Identifying relevant ads is challenging because queries are usually very short, and because users, consciously or not, choose terms intended to lead to optimal Web search results and not to optimal ads. Furthermore, the ads themselves are short and usually formulated to capture the reader’s attention rather than to facilitate query matching. Traditionally, matching of ads to queries employed standard information retrieval techniques using the bag of words approach. Here we propose to go beyond the bag of words, and augment both queries and ads with additional knowledgerich features. We use Web search results initially returned for the query to create a pool of relevant documents. Classifying these documents with respect to an external taxonomy and identifying salient named entities give rise to two new feature types. Empirical evaluation based on over 9,000 query-ad pairwise judgments confirms that using augmented queries produces highly relevant ads. Our methodology also relaxes the requirement for each ad to explicitly specify the exhaustive list of queries (“bid phrases”) that can trigger it.

We consider the problem of efficiently sampling Web search engine query results. In turn, using a small random sample instead of the full set of results leads to efficient approximate algorithms for several applications, such as: . Determining the set of categories in a given taxonomy spanned by the search results; . Finding the range of metadata values associated to the result set in order to enable "multi-faceted search;" . Estimating the size of the result set; . Data mining associations to the query terms. We present

We study the problem of efficiently computing diverse query results in online shopping applications, where users specify queries through a form interface that allows a mix of structured and content-based selection conditions. Intuitively, the goal of diverse query answering is to return a representative set of top-k answers from all the tuples that satisfy the user selection condition. For example, if a user is searching for Honda cars and we can only display five results, we wish to return cars from five different Honda models, as opposed to returning cars from only one or two Honda models. A key contribution of this paper is to formally define the notion of diversity, and to show that existing score based techniques commonly used in web applications are not sufficient to guarantee diversity. Another contribution of this paper is to develop novel and efficient query processing techniques that guarantee diversity. Our experimental results using Yahoo! Autos data show that our proposed techniques are scalable and efficient. I.

Abstract. Modern document collections often contain groups of documents with overlapping or shared content. However, most information retrieval systems process each document separately, causing shared content to be indexed multiple times. In this paper, we describe a new document representation model where related documents are organized as a tree, allowing shared content to be indexed just once. We show how this representation model can be encoded in an inverted index and we describe algorithms for evaluating free-text queries based on this encoding. We also show how our representation model applies to web, email, and newsgroup search. Finally, we present experimental results showing that our methods can provide a significant reduction in the size of an inverted index as well as in the time to build and query it. 1

Contextual advertising supports much of the Web’s ecosystem today. User experience and revenue (shared by the site publisher ad the ad network) depend on the relevance of the displayed ads to the page content. As with other document retrieval systems, relevance is provided by scoring the match between individual ads (documents) and the content of the page where the ads are shown (query). In this paper we show how this match can be improved significantly by augmenting the ad-page scoring function with extra parameters from a logistic regression model on the words in the pages and ads. A key property of the proposed model is that it can be mapped to standard cosine similarity matching and is suitable for efficient and scalable implementation over inverted indexes. The model parameter values are learnt from logs containing ad impressions and clicks, with shrinkage estimators being used to combat sparsity. To scale our computations to train on an extremely large training corpus consisting of several gigabytes of data, we parallelize our fitting algorithm in a Hadoop [10] framework. Experimental evaluation is provided showing improved click prediction over a holdout set of impression and click events from a large scale real-world ad placement engine. Our best model achieves a 25 % lift in precision relative to a traditional information retrieval model which is based on cosine similarity, for recalling 10 % of the clicks in our test data.

We consider the problem of efficiently indexing Disjunctive Normal Form (DNF) and Conjunctive Normal Form (CNF) Boolean expressions over a high-dimensional multi-valued attribute space. The goal is to rapidly find the set of Boolean expressions that evaluate to true for a given assignment of values to attributes. A solution to this problem has applications in online advertising (where a Boolean expression represents an advertiser’s user targeting requirements, and an assignment of values to attributes represents the characteristics of a user visiting an online page) and in general any publish/subscribe system (where a Boolean expression represents a subscription, and an assignment of values to attributes represents an event). All existing solutions that we are aware of can only index a specialized sub-set of conjunctive and/or disjunctive expressions, and cannot efficiently handle general DNF and CNF expressions (including NOTs) over multi-valued attributes. In this paper, we present a novel solution based on the inverted list data structure that enables us to index arbitrarily complex DNF and CNF Boolean expressions over multi-valued attributes. An interesting aspect of our solution is that, by virtue of leveraging inverted lists traditionally used for ranked information retrieval, we can efficiently return the top-N matching Boolean expressions. This capability enables emerging applications such as ranked publish/subscribe systems [16], where only the top subscriptions that match an event are desired. For example, in online advertising there is a limit on the number of advertisements that can be shown on a given page and only the “best ” advertisements can be displayed. We have evaluated our proposed technique based on data from an online advertising application, and the results show a dramatic performance improvement over prior techniques. 1.

Traditional document classification frameworks, which apply the learned classifier to each document in a corpus one by one, are infeasible for extremely large document corpora, like the Web or large corporate intranets. We consider the classification problem on a corpus that has been processed primarily for the purpose of searching, and thus our access to documents is solely through the inverted index of a large scale search engine. Our main goal is to build the “best” short query that characterizes a document class using operators normally available within large engines. We show that surprisingly good classification accuracy can be achieved on average over multiple classes by queries with as few as 10 terms. Moreover, we show that optimizing the efficiency of query execution by careful selection of these terms can further reduce the query costs. More precisely, we show that on our set-up the best 10 terms query can achieve 90 % of the accuracy of the best SVM classifier (14000 terms), and if we are willing to tolerate a reduction to 86 % of the best SVM, we can build a 10 terms query that can be executed more than twice as fast as the best 10 terms query.

We tackle the computational problem of query-conditioned search. Given a machine-learned scoring rule and a query distribution, we build a predictive index by precomputing lists of potential results sorted based on an expected score of the result over future queries. The predictive index datastructure supports an anytime algorithm for approximate retrieval of the top elements. The general approach is applicable to webpage ranking, internet advertisement, and approximate nearest neighbor search. It is particularly effective in settings where standard techniques (e.g., inverted indices) are intractable. We experimentally find substantial improvement over existing methods for internet advertisement and approximate nearest neighbors. 1

Large search engines process thousands of queries per second over billions of documents, making query processing a major performance bottleneck. An important class of optimization techniques called early termination achieves faster query processing by avoiding the scoring of documents that are unlikely to be in the top results. We study new algorithms for early termination that outperform previous methods. In particular, we focus on safe techniques for disjunctive queries, which return the same result as an exhaustive evaluation over the disjunction of the query terms. The current state-of-the-art methods for this case, the WAND algorithm by Broder et al. [11] and the approach of Strohman and Croft [30], achieve great benefits but still leave a large performance gap between disjunctive and (even non-early terminated) conjunctive queries. We propose a new set of algorithms by introducing a simple augmented inverted index structure called a block-max index. Essentially, this is a structure that stores the maximum impact score for each block of a compressed inverted list in uncompressed form, thus enabling us to skip large parts of the lists. We show how to integrate this structure into the WAND approach, leading to considerable performance gains. We then describe extensions to a layered index organization, and to indexes with reassigned document IDs, that achieve additional gains that narrow the gap between disjunctive and conjunctive top-k query processing.