The query performance prediction (QPP) task is to estimate the effectiveness of a search performed in response to a query with no relevance judgments. Existing QPP methods do not account for the effectiveness of a query in representing the underlying information need. We demonstrate the far-reaching implications of this reality using standard TREC-based evaluation of QPP methods: their relative prediction quality patterns vary with respect to the effectiveness of queries used to represent the information needs. Motivated by our findings, we revise the basic probabilistic formulation of the QPP task by accounting for the information need and its connection to the query. We further explore this connection by proposing a novel QPP approach that utilizes information about a set of queries representing the same information need. Predictors instantiated from our approach using a wide variety of existing QPP methods post prediction quality that substantially transcends that of applying these methods, as is standard, using a single query representing the information need. Additional in-depth empirical analysis of different aspects of our approach further attests to the crucial role of query effectiveness in QPP.

The cluster hypothesis is a fundamental concept in ad hoc retrieval. Heretofore, cluster hypothesis tests were applied to documents using binary relevance judgments. We present novel tests that utilize graded and focused relevance judgments; the latter are markups of relevant text in relevant documents. Empirical exploration reveals that the cluster hypothesis holds not only for documents, but also for passages, as measured by the proposed tests. Furthermore, the hypothesis holds to a higher extent for highly relevant documents and for those that contain a high fraction of relevant text.

There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.

We present a suite of query expansion methods that are based on word embeddings. Using Word2Vec's CBOW embedding approach, applied over the entire corpus on which search is performed, we select terms that are semantically related to the query. Our methods either use the terms to expand the original query or integrate them with the effective pseudo-feedback-based relevance model. In the former case, retrieval performance is significantly better than that of using only the query, and in the latter case the performance is significantly better than that of the relevance model.

We present a novel study of ad hoc retrieval methods utilizing document-level relevance feedback and/or focused relevance feedback; namely, passages marked as (non-)relevant. The first method uses a novel mixture model that integrates relevant and non-relevant information at the language model level. The second method fuses retrieval scores produced by using relevant and non-relevant information separately. Empirical exploration attests to the merits of our methods, and sheds light on the effectiveness of using and integrating relevance feedback for textual units of varying granularities.

Focused retrieval (a.k.a., passage retrieval) is important at its own right and as an intermediate step in question answering systems. We present a new Web-based collection for focused retrieval. The document corpus is the Category A of the ClueWeb12 collection. Forty-nine queries from the educational domain were created. The $100$ documents most highly ranked for each query by a highly effective learning-to-rank method were judged for relevance using crowdsourcing. All sentences in the relevant documents were judged for relevance.

The task of query performance prediction is to estimate the effectiveness of search performed in response to a query when no relevance judgments are available. We present a novel probabilistic analysis of the performance prediction task. The analysis gives rise to a general prediction framework that uses pseudo-effective or ineffective document lists that are retrieved in response to the query. These lists serve as reference to the result list at hand, the effectiveness of which we want to predict. We show that many previously proposed prediction methods can be explained using our framework. More generally, we shed new light on existing prediction methods and establish formal common grounds to seemingly different prediction approaches. In addition, we formally demonstrate the connection between prediction using reference lists and fusion of retrieved lists, and provide empirical support to this connection. Through an extensive empirical exploration, we study various factors that affect the quality of prediction using reference lists.

The query-performance prediction task is to estimate retrieval effectiveness with no relevance judgments. Pre-retrieval prediction methods operate prior to retrieval time. Hence, these predictors are often based on analyzing the query and the corpus upon which retrieval is performed. We propose a {\em corpus-independent} approach to pre-retrieval prediction which relies on information extracted from Wikipedia. Specifically, we present Wikipedia-based features that can attest to the effectiveness of retrieval performed in response to a query {\em regardless} of the corpus upon which search is performed. Empirical evaluation demonstrates the merits of our approach. As a case in point, integrating the Wikipedia-based features with state-of-the-art pre-retrieval predictors that analyze the corpus yields prediction quality that is consistently better than that of using the latter alone.

We present a novel contextualization approach for passage retrieval. The core principle is to let any occurrence of a query term in a document affect the passage retrieval score, whether the occurrence is in the passage or not. This effect is controlled by the distance between the term occurrence and the passage. Empirical evaluation demonstrates the merits of our approach; the resultant retrieval performance substantially transcends that of previously proposed passage retrieval methods, including those that use various contextualization approaches.

The query-performance prediction (QPP) task is estimating retrieval effectiveness with no relevance judgments. We present a novel probabilistic framework for QPP that gives rise to an important aspect that was not addressed in previous work; namely, the extent to which the query effectively represents the information need for retrieval. Accordingly, we devise a few query-representativeness measures that utilize relevance language models. Experiments show that integrating the most effective measures with state-of-the-art predictors in our framework often yields prediction quality that significantly transcends that of using the predictors alone.

We show that two tasks which were independently addressed in the information retrieval literature actually amount to the exact same task. The first is query performance prediction; i.e., estimating the effectiveness of a search performed in response to a query in the absence of relevance judgments. The second task is cluster ranking, that is, ranking clusters of similar documents by their presumed effectiveness (i.e., relevance) with respect to the query. Furthermore, we show that several state-of-the-art methods that were independently devised for each of the two tasks are based on the same principles. Finally, we empirically demonstrate that using insights gained in work on query-performance prediction can help, in many cases, to improve the performance of a previously proposed cluster ranking method.

The query-performance prediction task is estimating the effectiveness of a search performed in response to a query when no relevance judgments are available. Although there exist many effective prediction methods, these differ substantially in their basic principles, and rely on diverse hypotheses about the characteristics of effective retrieval. We present a novel fundamental probabilistic prediction framework. Using the framework, we derive and explain various previously proposed prediction methods that might seem completely different, but turn out to share the same formal basis. The derivations provide new perspectives on several predictors (e.g., Clarity). The framework is also used to devise new prediction approaches that outperform the state-of-the-art.

Estimating the effectiveness of a search performed in response to a query in the absence of relevance judgments is the goal of query-performance prediction methods. Post-retrieval predictors analyze the result list of the most highly ranked documents. We address the prediction challenge for retrieval approaches wherein the final result list is produced by fusing document lists that were retrieved in response to a query. To that end, we present a novel fundamental prediction framework that accounts for this special characteristics of the fusion setting; i.e., the use of intermediate retrieved lists. The framework is based on integrating prediction performed upon the final result list with that performed upon the lists that were fused to create it; prediction integration is controlled based on inter-list similarities. We empirically demonstrate the merits of various predictors instantiated from the framework. A case in point, their prediction quality substantially transcends that of applying state-of-the-art predictors upon the final result list.

We present a novel interpretation of Clarity [5], a widely used query performance predictor. While Clarity is commonly described as a measure of the "distance" between the language model of the top-retrieved documents and that of the collection, we show that it actually quantifies an additional property of the result list, namely, its diversity. This analysis, along with empirical evaluation, helps to explain the low prediction quality of Clarity for large-scale Web collections.

Community-based Question Answering sites, such as Yahoo! Answers or Baidu Zhidao, allow users to get answers to complex, detailed and personal questions from other users. However, since answering a question depends on the ability and willingness of users to address the asker's needs, a significant fraction of the questions remain unanswered. We measured that in Yahoo! Answers, this fraction represents 15% of all incoming English questions. At the same time, we discovered that around 25% of questions in certain categories are recurrent, at least at the question-title level, over a period of one year.

Predicting query performance , that is, the effectiveness of a search performed in response to a query, is a highly important and challenging problem. We present a novel approach to this task that is based on measuring the standard deviation of retrieval scores in the result list of the documents most highly ranked. We argue that for retrieval methods that are based on document-query surface-level similarities, the standard deviation can serve as a surrogate for estimating the presumed amount of query drift in the result list, that is, the presence (and dominance) of aspects or topics not related to the query in documents in the list. Empirical evaluation demonstrates the prediction effectiveness of our approach for several retrieval models. Specifically, the prediction quality often transcends that of current state-of-the-art prediction methods.

We present a novel framework for the query-performance prediction task. That is, estimating the effectiveness of a search performed in response to a query in lack of relevance judgments. Our approach is based on using statistical decision theory for estimating the utility that a document ranking provides with respect to an information need expressed by the query. To address the uncertainty in inferring the information need, we estimate utility by the expected similarity between the given ranking and those induced by relevance models; the impact of a relevance model is based on its presumed representativeness of the information need. Specific query-performance predictors instantiated from the framework substantially outperform state-of-the-art predictors over five TREC corpora.