We consider the problem of query rewrites in the context of pay-per-click search advertising. Given a three-layer graph consisting of queries, query rewrites, and the corresponding ads that can be served for the rewrites, we formulate a family of graph covering problems whose goals are to suggest a subset of ads with the maximum benefit by suggesting rewrites for a given query. We obtain constant-factor approximation algorithms for these covering problems, under two versions of constraints and a realistic notion of ad benefit. We perform experiments on real data and show that our algorithms are capable of outperforming a competitive baseline algorithm in terms of the benefit of the rewrites.

We focus on the problem of query rewriting for sponsored search. We base rewrites on a historical click graph that records the ads that have been clicked on in response to past user queries. Given a query q, we first consider Simrank [2] as a way to identify queries similar to q, i.e., queries whose ads a user may be interested in. We argue that Simrank fails to properly identify query similarities in our application, and we present two enhanced versions of Simrank: one that exploits weights on click graph edges and another that exploits evidence." We experimentally evaluate our new schemes against Simrank, using actual click graphs and queries form Yahoo!, and using a variety of metrics. Our results show that the enhanced methods can yield more and better query rewrites.

We focus on the problem of query rewriting for sponsored search. We base rewrites on a historical click graph that records the ads that have been clicked on in response to past user queries. Given a query q , we first consider Simrank [7] as a way to identify queries similar to q , i.e., queries whose ads a user may be interested in. We argue that Simrank fails to properly identify query similarities in our application, and we present two enhanced versions of Simrank: one that exploits weights on click graph edges and another that exploits "evidence." We experimentally evaluate our new schemes against Simrank, using actual click graphs and queries from Yahoo!, and using a variety of metrics. Our results show that the enhanced methods can yield more and better query rewrites.

We investigate the robustness of three widely used IR relevance measures for large data collections with incomplete judgments. The relevance measures we consider are the bpref measure introduced by Buckley and Voorhees [7], the inferred average precision (infAP) introduced by Aslam and Yilmaz [4], and the normalized discounted cumulative gain (NDCG) measure introduced by Järvelin and Kekäläinen [8]. Our main results show that NDCG consistently performs better than both bpref and infAP. The experiments are performed on standard TREC datasets, under different levels of incompleteness of judgments, and using two different evaluation methods, namely, the Kendall correlation measures order between system rankings and pairwise statistical significance testing; the latter may be of independent interest.

Contextual search refers to proactively capturing the information need of a user by automatically augmenting the user query with information extracted from the search context; for example, by using terms from the web page the user is currently browsing or a file the user is currently editing.We present three different algorithms to implement contextual search for the Web. The first, it query rewriting (QR), augments each query with appropriate terms from the search context and uses an off-the-shelf web search engine to answer this augmented query. The second, rank-biasing (RB), generates a representation of the context and answers queries using a custom-built search engine that exploits this representation. The third, iterative filtering meta-search (IFM), generates multiple subqueries based on the user query and appropriate terms from the search context, uses an off-the-shelf search engine to answer these subqueries, and re-ranks the results of the subqueries using rank aggregation methods.We extensively evaluate the three methods using 200 contexts and over 24,000 human relevance judgments of search results. We show that while QR works surprisingly well, the relevance and recall can be improved using RB and substantially more using IFM. Thus, QR, RB, and IFM represent a cost-effective design spectrum for contextual search.

Contextual search tries to better capture a user's information need by augmenting the user's query with contextual information extracted from the search context (for example, terms from the web page the user is currently reading or a file the user is currently editing).This paper presents Y!Q---a first of its kind large-scale contextual search system---and provides an overview of its system design and architecture. Y!Q solves two major problems. First, how to capture high quality search context. Second, how to use that context in a way to improve the relevancy of search queries. To address the first problem, Y!Q introduces an information widget that captures precise search context and provides convenient access to its functionality at the point of inspiration. For example, Y!Q can be easily embedded into web pages using a web API, or it can be integrated into a web browser toolbar. This paper provides an overview of Y!Q's user interaction design, highlighting its novel aspects for capturing high quality search context.To address the second problem, Y!Q uses a semantic network for analyzing search context, possibly resolving ambiguous terms, and generating a contextual digest comprising its key concepts. This digest is passed through a query planner and rewriting framework for augmenting a user's search query with relevant context terms to improve the overall search relevancy and experience. We show experimental results comparing contextual Y!Q search results side-by-side with regular Yahoo! web search results. This evaluation suggests that Y!Q results are considered significantly more relevant.The paper also identifies interesting research problems and argues that contextual search may represent the next major step in the evolution of web search engines.

The Virtual Interface (VI) architecture has become the industry standard for user-level network interfaces. This paper presents the implementation and evaluation of Javia, a Java interface to the VI architecture. Javia explores two points in the design space. The first approach manages buffers in C and requires data copies between the Java heap and native buffers. The second approach relies on a Java-level buffer abstraction that eliminates the copies in the first approach. Javia achieves an effective bandwidth of 80 Mbytes s−1 for 8 kbyte messages, which is within 1% of those achieved by C programs. Performance evaluations of parallel matrix multiplication and of the active messages communication protocol show that Javia can serve as an efficient building block for Java cluster applications. Copyright © 2000 John Wiley & Sons, Ltd.

MRPC is an RPC system that is designed and optimized for MPMD parallel computing. Existing systems based on standard RPC incur an unnecessarily high cost when used on high-performance multi-computers, limiting the appeal of RPC-based languages in the parallel computing community. MRPC combines the efficient control and data transfer provided by Active Messages (AM) with a minimal multithreaded runtime system that extends AM with the features required to support MPMD. This approach introduces only the necessary RPC overheads for an MPMD environment. MRPC has been integrated into Compositional C++ (CC++), a parallel extension of C++ that offers an MPMD programming model. Basic performance in MRPC is within a factor of two from those of Split-C, a highly tuned SPMD language, and other messaging layers. CC++ applications perform within a factor of two to six from comparable Split-C versions, which represent an order of magnitude improvement over previous CC++ implementations. Copyright © 1999 John Wiley & Sons, Ltd.

Global addressing of shared data simplifies parallel programming and complements message passing models commonly found in distributed memory machines. A number of programming systems have been designed that synthesize global addressing purely in software on such machines. These systems provide a number of communication mechanisms to mitigate the effect of high communication latencies and overheads. This study compares the mechanisms in two representative all-software systems: CRL and Split-C. CRL uses region-based caching while Split-C uses split-phase and push-based data transfers for optimizing communication performance. Both systems take advantage of bulk data transfers. By implementing a set of parallel applications in both CRL and Split-C, and running them on the IBM SP2, Meiko CS-2 and two simulated architectures, we find that split-phase and push-based bulk data transfers are essential for good performance. Regionbased caching benefits applications with irregular structure and w...

The MPMD approach for parallel computing is attractive for programmers who seek fast development cycles, high code re-use, and modular programming, or whose applications exhibit irregular computation loads and communication patterns. RPC is widely adopted as the communication abstraction for crossing address space boundaries. However, the communication overheads of existing RPC-based systems are usually an order of magnitude higher than those found in highly tuned SPMD systems. This problem has thus far limited the appeal of high-level programming languages based on MPMD models in the parallel computing community.This paper investigates the fundamental limitations of MPMD communication using a case study of two parallel programming languages, Compositional C++ (CC++) and Split-C, that provide support for a global name space. To establish a common comparison basis, our implementation of CC++ was developed to use MRPC, a RPC system optimized for MPMD parallel computing and based on Active Messages. Basic RPC performance in CC++ is within a factor of two from those of Split-C and other messaging layers. CC++ applications perform within a factor of two to six from comparable Split-C versions, which represent an order of magnitude improvement over previous CC++ implementations. The results suggest that RPC-based communication can be used effectively in many high-performance MPMD parallel applications.

The IBM SP is one of the most powerful commercial MPPs, yet, in spite of its fast processors and high network bandwidth, the SP's communication latency is inferior to older machines such as the TMC CM-5 or Meiko CS-2. This paper investigates the use of Active Messages (AM) communication primitives as an alternative to the standard message passing in order to reduce communication overheads and to offer a good building block for higher layers of software. The first part of this paper describes an implementation of Active Messages (SP AM) which is layered directly on top of the SP's network adapter (TB2). With comparable bandwidth, SP AM's low overhead yields a round-trip latency that is 40% lower than IBM MPL's. The second part of the paper demonstrates the power of AM as a communication substrate by layering Split-C as well as MPI over it. Split-C benchmarks are used to compare the SP to other MPPs and show that low message overhead and high throughput compensate for SP's high network latency. The MPI implementation is based on the freely available MPICH version and achieves performance equivalent to IBM's MPI-F on the NAS benchmarks.