In from the Edge: The Progressive Evolution of Publishing in the Age of Digital Abundance

The forces for change in the 600 hundred-year history of book publishing have never been more powerful and fundamental than in 2011. Barely 12 months ago, what publishers viewed as “innovation” was the creation of digitized versions of physical books to be ‘consumed’ on e-reading devices. As 2011 begins, that change is largely regarded as accepted business practice, and the edge of “innovation” is moving in more complex directions. Publishing finds itself in the midst of a ‘phase shift’ from the scarcity model of print to a complex, new world of digital abundance. The dimensions of change, moreover, its speed, and its extent are still unknown. While book publishing begins with the creative work of authors, digital technologies are turning the business models inside out. Among the forces for change acting on book publishing, this article examines several phenomena that are working their way in from the edge to the center of the industry, including complex interwoven considerations around container, context, and customer.     

The sum of its parts: reducing sparsity in click estimation with query segments

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.     

Variational bayes for modeling score distributions

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.     

Retrieval constraints and word frequency distributions a log-logistic model for IR

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.     

Fusing different information retrieval systems according to query-topics: a study based on correlation in information retrieval systems and TREC topics

To evaluate Information Retrieval Systems on their effectiveness, evaluation programs such as TREC offer a rigorous methodology as well as benchmark collections. Whatever the evaluation collection used, effectiveness is generally considered globally, averaging the results over a set of information needs. As a result, the variability of system performance is hidden as the similarities and differences from one system to another are averaged. Moreover, the topics on which a given system succeeds or fails are left unknown. In this paper we propose an approach based on data analysis methods (correspondence analysis and clustering) to discover correlations between systems and to find trends in topic/system correlations. We show that it is possible to cluster topics and systems according to system performance on these topics, some system clusters being better on some topics. Finally, we propose a new method to consider complementary systems as based on their performances which can be applied for example in the case of repeated queries. We consider the system profile based on the similarity of the set of TREC topics on which systems achieve similar levels of performance. We show that this method is effective when using the TREC ad hoc collection.     

Intent-based diversification of web search results: metrics and algorithms

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 user-L.s 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 altogether. We argue that this is a better metric than some previously proposed intent aware metrics and show that it has a better correlation with abandonment rate. We then propose an algorithm to rerank web search results based on optimizing an objective function corresponding to this metric and evaluate it on shopping related queries.     

Managing the Progressive Publishing Company During Market and Technology Transitions

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.     

Popular E-Content at The New York Public Library: Successes and Challenges

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.     

Is there an ethics of algorithms?

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.     

Ethics in e-trust and e-trustworthiness: the case of direct computer-patient interfaces

In this paper, I examine the ethics of e-trust and e-trustworthiness in the context of health care, looking at direct computer-patient interfaces (DCPIs), information systems that provide medical information, diagnosis, advice, consenting and/or treatment directly to patients without clinicians as intermediaries. Designers, manufacturers and deployers of such systems have an ethical obligation to provide evidence of their trustworthiness to users. My argument for this claim is based on evidentialism about trust and trustworthiness: the idea that trust should be based on sound evidence of trustworthiness. Evidence of trustworthiness is a broader notion than one might suppose, including not just information about the risks and performance of the system, but also interactional and context-based information. I suggest some sources of evidence in this broader sense that make it plausible that designers, manufacturers and deployers of DCPIs can provide evidence to users that is cognitively simple, easy to communicate, yet rationally connected with actual trustworthiness.