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891.
Dustin Hillard Eren Manavoglu Hema Raghavan Chris Leggetter Erick Cantú-Paz Rukmini Iyer 《Information Retrieval》2011,14(3):315-336
The critical task of predicting clicks on search advertisements is typically addressed by learning from historical click data.
When enough history is observed for a given query-ad pair, future clicks can be accurately modeled. However, based on the
empirical distribution of queries, sufficient historical information is unavailable for many query-ad pairs. The sparsity
of data for new and rare queries makes it difficult to accurately estimate clicks for a significant portion of typical search
engine traffic. In this paper we provide analysis to motivate modeling approaches that can reduce the sparsity of the large
space of user search queries. We then propose methods to improve click and relevance models for sponsored search by mining
click behavior for partial user queries. We aggregate click history for individual query words, as well as for phrases extracted
with a CRF model. The new models show significant improvement in clicks and revenue compared to state-of-the-art baselines
trained on several months of query logs. Results are reported on live traffic of a commercial search engine, in addition to
results from offline evaluation. 相似文献
892.
Empirical modeling of the score distributions associated with retrieved documents is an essential task for many retrieval
applications. In this work, we propose modeling the relevant documents’ scores by a mixture of Gaussians and the non-relevant
scores by a Gamma distribution. Applying Variational Bayes we automatically trade-off the goodness-of-fit with the complexity
of the model. We test our model on traditional retrieval functions and actual search engines submitted to TREC. We demonstrate
the utility of our model in inferring precision-recall curves. In all experiments our model outperforms the dominant exponential-Gaussian
model. 相似文献
893.
We first present in this paper an analytical view of heuristic retrieval constraints which yields simple tests to determine
whether a retrieval function satisfies the constraints or not. We then review empirical findings on word frequency distributions
and the central role played by burstiness in this context. This leads us to propose a formal definition of burstiness which
can be used to characterize probability distributions with respect to this phenomenon. We then introduce the family of information-based
IR models which naturally captures heuristic retrieval constraints when the underlying probability distribution is bursty
and propose a new IR model within this family, based on the log-logistic distribution. The experiments we conduct on several
collections illustrate the good behavior of the log-logistic IR model: It significantly outperforms the Jelinek-Mercer and
Dirichlet prior language models on most collections we have used, with both short and long queries and for both the MAP and
the precision at 10 documents. It also compares favorably to BM25 and has similar performance to classical DFR models such
as InL2 and PL2. 相似文献
894.
As new media technologies and platforms emerge and take hold in our society, traditional publishers are wondering: What’s in this new content climate for me? The simple answer is: a lot. The digital world, mobile content delivery mechanisms and the public’s increasing comfort—even preference for—a media menu from which they can pick and choose what they want and how they want to receive it, brings exciting and potentially lucrative opportunities. For publishers who understand how to leverage their brand and create authentic, identifiable value in the eyes of the customer, risk can be reduced and new revenue streams built. Here are four best practices to position your publishing company for growth. 相似文献
895.
Christopher Platt 《Publishing Research Quarterly》2011,27(3):247-253
Updated from a presentation given at Biblionext.it in Rome in April 2011, this article will highlight The New York Public
Library’s success with e-books and other forms of popular e-content and our efforts to stay one step ahead of the consumer
shift from print reading to e-reading. Consumer e-reading is dominated by Amazon.com in the US, followed by one of the largest
chain bookstores, BarnesandNoble.com. The availability of digital versions of very popular titles, coupled with the explosion
of e-readers, tablets, and smartphones that are priced competitively and fairly easy to use, are helping move a lot of Americans
into the e-book world. Last month, Amazon.com announced they sold more e-books than physical books for the first time ever.
Print books are not going away, but our experience is that it is clear e-books are no longer just an extra format to offer,
they are integral to our future. 相似文献
896.
Felicitas Kraemer Kees van Overveld Martin Peterson 《Ethics and Information Technology》2011,13(3):251-260
We argue that some algorithms are value-laden, and that two or more persons who accept different value-judgments may have
a rational reason to design such algorithms differently. We exemplify our claim by discussing a set of algorithms used in
medical image analysis: In these algorithms it is often necessary to set certain thresholds for whether e.g. a cell should
count as diseased or not, and the chosen threshold will partly depend on the software designer’s preference between avoiding
false positives and false negatives. This preference ultimately depends on a number of value-judgments. In the last section
of the paper we discuss some general principles for dealing with ethical issues in algorithm-design. 相似文献
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900.
Guy Trainin Kathleen M. Wilson Malinda Murphy-Yagil Joan L. Rankin-Erickson 《Journal of Education for Students Placed at Risk》2014,19(3-4):183-195
This study compared the impact of 2 types of small-group interventions targeting below-level 3rd-grade students. The study compared WordWork decoding and spelling instruction (Calfee, Miller, Norman, Wilson, &; Trainin, 2006; Calfee &; Patrick, 1995), which includes attention to articulation and metacognition, with a more traditional phonological awareness program (Torgesen &; Bryant, 1993). University education students delivered the interventions with equal instructional time and fidelity, to both groups. Students in both intervention groups engaged in repeated readings of connected text to promote transfer. The impact of training was assessed through decoding, spelling, and oral reading fluency measures. Results indicated that WordWork produced more positive results in decoding, spelling, and fluency, and had significantly fewer treatment resisters. 相似文献