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1.
Relevance feedback is an effective technique for improving search accuracy in interactive information retrieval. In this paper, we study an interesting optimization problem in interactive feedback that aims at optimizing the tradeoff between presenting search results with the highest immediate utility to a user (but not necessarily most useful for collecting feedback information) and presenting search results with the best potential for collecting useful feedback information (but not necessarily the most useful documents from a user’s perspective). Optimizing such an exploration–exploitation tradeoff is key to the optimization of the overall utility of relevance feedback to a user in the entire session of relevance feedback. We formally frame this tradeoff as a problem of optimizing the diversification of search results since relevance judgments on more diversified results have been shown to be more useful for relevance feedback. We propose a machine learning approach to adaptively optimizing the diversification of search results for each query so as to optimize the overall utility in an entire session. Experiment results on three representative retrieval test collections show that the proposed learning approach can effectively optimize the exploration–exploitation tradeoff and outperforms the traditional relevance feedback approach which only does exploitation without exploration. 相似文献
2.
Web search engines are increasingly deploying many features, combined using learning to rank techniques. However, various practical questions remain concerning the manner in which learning to rank should be deployed. For instance, a sample of documents with sufficient recall is used, such that re-ranking of the sample by the learned model brings the relevant documents to the top. However, the properties of the document sample such as when to stop ranking—i.e. its minimum effective size—remain unstudied. Similarly, effective listwise learning to rank techniques minimise a loss function corresponding to a standard information retrieval evaluation measure. However, the appropriate choice of how to calculate the loss function—i.e. the choice of the learning evaluation measure and the rank depth at which this measure should be calculated—are as yet unclear. In this paper, we address all of these issues by formulating various hypotheses and research questions, before performing exhaustive experiments using multiple learning to rank techniques and different types of information needs on the ClueWeb09 and LETOR corpora. Among many conclusions, we find, for instance, that the smallest effective sample for a given query set is dependent on the type of information need of the queries, the document representation used during sampling and the test evaluation measure. As the sample size is varied, the selected features markedly change—for instance, we find that the link analysis features are favoured for smaller document samples. Moreover, despite reflecting a more realistic user model, the recently proposed ERR measure is not as effective as the traditional NDCG as a learning loss function. Overall, our comprehensive experiments provide the first empirical derivation of best practices for learning to rank deployments. 相似文献
3.
Most current machine learning methods for building search engines are based on the assumption that there is a target evaluation
metric that evaluates the quality of the search engine with respect to an end user and the engine should be trained to optimize
for that metric. Treating the target evaluation metric as a given, many different approaches (e.g. LambdaRank, SoftRank, RankingSVM,
etc.) have been proposed to develop methods for optimizing for retrieval metrics. Target metrics used in optimization act
as bottlenecks that summarize the training data and it is known that some evaluation metrics are more informative than others.
In this paper, we consider the effect of the target evaluation metric on learning to rank. In particular, we question the
current assumption that retrieval systems should be designed to directly optimize for a metric that is assumed to evaluate
user satisfaction. We show that even if user satisfaction can be measured by a metric X, optimizing the engine on a training
set for a more informative metric Y may result in a better test performance according to X (as compared to optimizing the
engine directly for X on the training set). We analyze the situations as to when there is a significant difference in the
two cases in terms of the amount of available training data and the number of dimensions of the feature space. 相似文献
4.
When speaking of information retrieval, we often mean text retrieval. But there exist many other forms of information retrieval applications. A typical example is collaborative filtering that suggests interesting items to a user by taking into account other users’ preferences or tastes. Due to the uniqueness of the problem, it has been modeled and studied differently in the past, mainly drawing from the preference prediction and machine learning view point. A few attempts have yet been made to bring back collaborative filtering to information (text) retrieval modeling and subsequently new interesting collaborative filtering techniques have been thus derived. In this paper, we show that from the algorithmic view point, there is an even closer relationship between collaborative filtering and text retrieval. Specifically, major collaborative filtering algorithms, such as the memory-based, essentially calculate the dot product between the user vector (as the query vector in text retrieval) and the item rating vector (as the document vector in text retrieval). Thus, if we properly structure user preference data and employ the target user’s ratings as query input, major text retrieval algorithms and systems can be directly used without any modification. In this regard, we propose a unified formulation under a common notational framework for memory-based collaborative filtering, and a technique to use any text retrieval weighting function with collaborative filtering preference data. Besides confirming the rationale of the framework, our preliminary experimental results have also demonstrated the effectiveness of the approach in using text retrieval models and systems to perform item ranking tasks in collaborative filtering. 相似文献
5.
Enterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Enterprise data contain both structured and unstructured information. Since these two types of information are complementary and the structured information such as relational databases is designed based on ER (entity-relationship) models, there is a rich body of information about entities in enterprise data. As a result, many information needs of enterprise search center around entities. For example, a user may formulate a query describing a problem that she encounters with an entity, e.g., the web browser, and want to retrieve relevant documents to solve the problem. Intuitively, information related to the entities mentioned in the query, such as related entities and their relations, would be useful to reformulate the query and improve the retrieval performance. However, most existing studies on query expansion are term-centric. In this paper, we propose a novel entity-centric query expansion framework for enterprise search. Specifically, given a query containing entities, we first utilize both unstructured and structured information to find entities that are related to the ones in the query. We then discuss how to adapt existing feedback methods to use the related entities and their relations to improve search quality. Experimental results over two real-world enterprise collections show that the proposed entity-centric query expansion strategies are more effective and robust to improve the search performance than the state-of-the-art pseudo feedback methods for long natural language-like queries with entities. Moreover, results over a TREC ad hoc retrieval collections show that the proposed methods can also work well for short keyword queries in the general search domain. 相似文献
6.
Teaching and learning in information retrieval 总被引:1,自引:1,他引:0
Juan M. Fernández-Luna Juan F. Huete Andrew MacFarlane Efthimis N. Efthimiadis 《Information Retrieval》2009,12(2):201-226
A literature review of pedagogical methods for teaching and learning information retrieval is presented. From the analysis
of the literature a taxonomy was built and it is used to structure the paper. Information Retrieval (IR) is presented from
different points of view: technical levels, educational goals, teaching and learning methods, assessment and curricula. The
review is organized around two levels of abstraction which form a taxonomy that deals with the different aspects of pedagogy
as applied to information retrieval. The first level looks at the technical level of delivering information retrieval concepts,
and at the educational goals as articulated by the two main subject domains where IR is delivered: computer science (CS) and
library and information science (LIS). The second level focuses on pedagogical issues, such as teaching and learning methods,
delivery modes (classroom, online or e-learning), use of IR systems for teaching, assessment and feedback, and curricula design.
The survey, and its bibliography, provides an overview of the pedagogical research carried out in the field of IR. It also
provides a guide for educators on approaches that can be applied to improving the student learning experiences. 相似文献
7.
The Cross-Language Evaluation Forum has encouraged research in text retrieval methods for numerous European languages and has developed durable test suites that allow language-specific techniques to be investigated and compared. The labor associated with crafting a retrieval system that takes advantage of sophisticated linguistic methods is daunting. We examine whether language-neutral methods can achieve accuracy comparable to language-specific methods with less concomitant software complexity. Using the CLEF 2002 test set we demonstrate empirically how overlapping character n-gram tokenization can provide retrieval accuracy that rivals the best current language-specific approaches for European languages. We show that n = 4 is a good choice for those languages, and document the increased storage and time requirements of the technique. We report on the benefits of and challenges posed by n-grams, and explain peculiarities attendant to bilingual retrieval. Our findings demonstrate clearly that accuracy using n-gram indexing rivals or exceeds accuracy using unnormalized words, for both monolingual and bilingual retrieval. 相似文献
8.
Blog feed search aims to identify a blog feed of recurring interest to users on a given topic. A blog feed, the retrieval
unit for blog feed search, comprises blog posts of diverse topics. This topical diversity of blog feeds often causes performance
deterioration of blog feed search. To alleviate the problem, this paper proposes several approaches based on passage retrieval,
widely regarded as effective to handle topical diversity at document level in ad-hoc retrieval. We define the global and local
evidence for blog feed search, which correspond to the document-level and passage-level evidence for passage retrieval, respectively,
and investigate their influence on blog feed search, in terms of both initial retrieval and pseudo-relevance feedback. For
initial retrieval, we propose a retrieval framework to integrate global evidence with local evidence. For pseudo-relevance
feedback, we gather feedback information from the local evidence of the top K ranked blog feeds to capture diverse and accurate information related to a given topic. Experimental results show that our
approaches using local evidence consistently and significantly outperform traditional ones. 相似文献
9.
ABSTRACTThis study is an exploration of how Association of Research Libraries (ARL) member institutions encourage and support their librarians engaged in research and publication. Using an online survey sent to members of the ARL library directors’ listserv, the authors gathered information about the role of research and publication in the respondents’ evaluation systems, the approaches used to support this activity, the respondents’ own records of research and publication, and their opinions about which approaches have the most impact. The results indicate that funding, time, and mentoring are the most frequently used approaches to promote research and publication productivity. 相似文献
10.
This paper investigates the impact of three approaches to XML retrieval: using Zettair, a full-text information retrieval system; using eXist, a native XML database; and using a hybrid system that takes full article answers from Zettair and uses eXist to extract elements from those articles. For the content-only topics, we undertake a preliminary analysis of the INEX 2003 relevance assessments in order to identify the types of highly relevant document components. Further analysis identifies two complementary sub-cases of relevance assessments (General and Specific) and two categories of topics (Broad and Narrow). We develop a novel retrieval module that for a content-only topic utilises the information from the resulting answer list of a native XML database and dynamically determines the preferable units of retrieval, which we call Coherent Retrieval Elements. The results of our experiments show that—when each of the three systems is evaluated against different retrieval scenarios (such as different cases of relevance assessments, different topic categories and different choices of evaluation metrics)—the XML retrieval systems exhibit varying behaviour and the best performance can be reached for different values of the retrieval parameters. In the case of INEX 2003 relevance assessments for the content-only topics, our newly developed hybrid XML retrieval system is substantially more effective than either Zettair or eXist, and yields a robust and a very effective XML retrieval. 相似文献
11.
Social tagging systems have gained increasing popularity as a method of annotating and categorizing a wide range of different web resources. Web search that utilizes social tagging data suffers from an extreme example of the vocabulary mismatch problem encountered in traditional information retrieval (IR). This is due to the personalized, unrestricted vocabulary that users choose to describe and tag each resource. Previous research has proposed the utilization of query expansion to deal with search in this rather complicated space. However, non-personalized approaches based on relevance feedback and personalized approaches based on co-occurrence statistics only showed limited improvements. This paper proposes a novel query expansion framework based on individual user profiles mined from the annotations and resources the user has marked. The underlying theory is to regularize the smoothness of word associations over a connected graph using a regularizer function on terms extracted from top-ranked documents. The intuition behind the model is the prior assumption of term consistency: the most appropriate expansion terms for a query are likely to be associated with, and influenced by terms extracted from the documents ranked highly for the initial query. The framework also simultaneously incorporates annotations and web documents through a Tag-Topic model in a latent graph. The experimental results suggest that the proposed personalized query expansion method can produce better results than both the classical non-personalized search approach and other personalized query expansion methods. Hence, the proposed approach significantly benefits personalized web search by leveraging users’ social media data. 相似文献
12.
Knowledge transfer for cross domain learning to rank 总被引:1,自引:1,他引:0
Depin Chen Yan Xiong Jun Yan Gui-Rong Xue Gang Wang Zheng Chen 《Information Retrieval》2010,13(3):236-253
Recently, learning to rank technology is attracting increasing attention from both academia and industry in the areas of machine
learning and information retrieval. A number of algorithms have been proposed to rank documents according to the user-given
query using a human-labeled training dataset. A basic assumption behind general learning to rank algorithms is that the training
and test data are drawn from the same data distribution. However, this assumption does not always hold true in real world
applications. For example, it can be violated when the labeled training data become outdated or originally come from another
domain different from its counterpart of test data. Such situations bring a new problem, which we define as cross domain learning
to rank. In this paper, we aim at improving the learning of a ranking model in target domain by leveraging knowledge from
the outdated or out-of-domain data (both are referred to as source domain data). We first give a formal definition of the
cross domain learning to rank problem. Following this, two novel methods are proposed to conduct knowledge transfer at feature
level and instance level, respectively. These two methods both utilize Ranking SVM as the basic learner. In the experiments,
we evaluate these two methods using data from benchmark datasets for document retrieval. The results show that the feature-level
transfer method performs better with steady improvements over baseline approaches across different datasets, while the instance-level
transfer method comes out with varying performance depending on the dataset used. 相似文献
13.
14.
Bevan Koopman Guido Zuccon Peter Bruza Laurianne Sitbon Michael Lawley 《Information Retrieval》2016,19(1-2):6-37
This paper presents a Graph Inference retrieval model that integrates structured knowledge resources, statistical information retrieval methods and inference in a unified framework. Key components of the model are a graph-based representation of the corpus and retrieval driven by an inference mechanism achieved as a traversal over the graph. The model is proposed to tackle the semantic gap problem—the mismatch between the raw data and the way a human being interprets it. We break down the semantic gap problem into five core issues, each requiring a specific type of inference in order to be overcome. Our model and evaluation is applied to the medical domain because search within this domain is particularly challenging and, as we show, often requires inference. In addition, this domain features both structured knowledge resources as well as unstructured text. Our evaluation shows that inference can be effective, retrieving many new relevant documents that are not retrieved by state-of-the-art information retrieval models. We show that many retrieved documents were not pooled by keyword-based search methods, prompting us to perform additional relevance assessment on these new documents. A third of the newly retrieved documents judged were found to be relevant. Our analysis provides a thorough understanding of when and how to apply inference for retrieval, including a categorisation of queries according to the effect of inference. The inference mechanism promoted recall by retrieving new relevant documents not found by previous keyword-based approaches. In addition, it promoted precision by an effective reranking of documents. When inference is used, performance gains can generally be expected on hard queries. However, inference should not be applied universally: for easy, unambiguous queries and queries with few relevant documents, inference did adversely affect effectiveness. These conclusions reflect the fact that for retrieval as inference to be effective, a careful balancing act is involved. Finally, although the Graph Inference model is developed and applied to medical search, it is a general retrieval model applicable to other areas such as web search, where an emerging research trend is to utilise structured knowledge resources for more effective semantic search. 相似文献
15.
When people are connected together over ad hoc social networks, it is possible to ask questions and retrieve answers using the wisdom of the crowd. However, locating a suitable candidate for answering a specific unique question within larger ad hoc groups is non-trivial, especially if we wish to respect the privacy of users by providing deniability. All members of the network wish to source the best possible answers from the network, while at the same time controlling the levels of attention required to generate them by the collective group of individuals and/or the time taken to read all the answers. Conventional expert retrieval approaches rank users for a given query in a centralised indexing process, associating users with material they have previously published. Such an approach is antithetical to privacy, so we have looked to distribute the routing of questions and answers, converting the indexing process into one of building a forwarding table. Starting from the simple operation of flooding the question to everyone, we compare a number of different routing options, where decisions must be made based on past performance and exploitation of the knowledge of our immediate neighbours. We focus on fully decentralised protocols using ant-inspired tactics to route questions towards members of the network who may be able to answer them well. Simultaneously, privacy concerns are acknowledged by allowing both question asking and answering to be plausibly deniable. We have found that via our routing method, it is possible to improve answer quality and also reduce the total amount of user attention required to generate those answers. 相似文献
16.
Transaction logs from online search engines are valuable for two reasons: First, they provide insight into human information-seeking behavior. Second, log data can be used to train user models, which can then be applied to improve retrieval systems. This article presents a study of logs from PubMed®, the public gateway to the MEDLINE® database of bibliographic records from the medical and biomedical primary literature. Unlike most previous studies on general Web search, our work examines user activities with a highly-specialized search engine. We encode user actions as string sequences and model these sequences using n-gram language models. The models are evaluated in terms of perplexity and in a sequence prediction task. They help us better understand how PubMed users search for information and provide an enabler for improving users’ search experience. 相似文献
17.
Searching online information resources using mobile devices is affected by small screens which can display only a fraction
of ranked search results. In this paper we investigate whether the search effort can be reduced by means of a simple user
feedback: for a screenful of search results the user is encouraged to indicate a single most relevant document. In our approach
we exploit the fact that, for small display sizes and limited user actions, we can construct a user decision tree representing
all possible outcomes of the user interaction with the system. Examining the trees we can compute an upper limit on relevance
feedback performance. In this study we consider three standard feedback algorithms: Rocchio, Robertson/Sparck-Jones (RSJ)
and a Bayesian algorithm. We evaluate them in conjunction with two strategies for presenting search results: a document ranking
that attempts to maximize information gain from the user’s choices and the top-D ranked documents. Experimental results indicate
that for RSJ feedback which involves an explicit feature selection policy, the greedy top-D display is more appropriate. For
the other two algorithms, the exploratory display that maximizes information gain produces better results. We conducted a
user study to compare the performance of the relevance feedback methods with real users and compare the results with the findings
from the tree analysis. This comparison between the simulations and real user behaviour indicates that the Bayesian algorithm,
coupled with the sampled display, is the most effective.
Extended version of “Evaluating Relevance Feedback Algorithms for Searching on Small Displays, ” Vishwa Vinay, Ingemar J.
Cox, Natasa Milic-Frayling, Ken Wood published in the proceedings of ECIR 2005, David E. Losada, Juan M. Fernández-Luna (Eds.),
Springer 2005, ISBN 3-540-25295-9 相似文献
18.
基于用户信息检索相关性反馈模型的研究 总被引:1,自引:0,他引:1
提出通过获取用户建立和更新信息相关反馈模型的思想.通过观察用户在浏览Web页面时所采取的动作来获取的反馈信息,利用检索算法将用户信息量化,并利用这些信息建立与更新用户模型.一方面用户对检索结果的评价输入到用户模型上,另一方面,检索系统通过机器学习跟踪用户信息并优化用户模型. 相似文献
19.
The application of relevance feedback techniques has been shown to improve retrieval performance for a number of information retrieval tasks. This paper explores incremental relevance feedback for ad hoc Japanese text retrieval; examining, separately and in combination, the utility of term reweighting and query expansion using a probabilistic retrieval model. Retrieval performance is evaluated in terms of standard precision-recall measures, and also using number-to-view graphs. Experimental results, on the standard BMIR-J2 Japanese language retrieval collection, show that both term reweighting and query expansion improve retrieval performance. This is reflected in improvements in both precision and recall, but also a reduction in the average number of documents which must be viewed to find a selected number of relevant items. In particular, using a simple simulation of user searching, incremental application of relevance information is shown to lead to progressively improved retrieval performance and an overall reduction in the number of documents that a user must view to find relevant ones. 相似文献
20.
Robert W. P. Luk 《Information Retrieval》2008,11(6):539-561
This paper discusses various issues about the rank equivalence of Lafferty and Zhai between the log-odds ratio and the query
likelihood of probabilistic retrieval models. It highlights that Robertson’s concerns about this equivalence may arise when
multiple probability distributions are assumed to be uniformly distributed, after assuming that the marginal probability logically
follows from Kolmogorov’s probability axioms. It also clarifies that there are two types of rank equivalence relations between
probabilistic models, namely strict and weak rank equivalence. This paper focuses on the strict rank equivalence which requires
the event spaces of the participating probabilistic models to be identical. It is possible that two probabilistic models are
strict rank equivalent when they use different probability estimation methods. This paper shows that the query likelihood,
p(q|d, r), is strict rank equivalent to p(q|d) of the language model of Ponte and Croft by applying assumptions 1 and 2 of Lafferty and Zhai. In addition, some statistical
component language model may be strict rank equivalent to the log-odds ratio, and that some statistical component model using
the log-odds ratio may be strict rank equivalent to the query likelihood. Finally, we suggest adding a random variable for
the user information need to the probabilistic retrieval models for clarification when these models deal with multiple requests. 相似文献