waashk/dblp huggingface dataset:
easiest-first search: towards comprehension-based web search. although web search engines have become information gateways to the internet, for queries containing technical terms, search results often contain pages that are difficult to be understood by non-expert users. therefore, re-ranking search results in a descending order of their comprehensibility should be effective for non-expert users. in our approach, the comprehensibility of web pages is estimated considering both the document readability and the difficulty of technical terms in the domain of search queries. to extract technical terms, we exploit the domain knowledge extracted from wikipedia. our proposed method can be applied to general web search engines as wikipedia includes nearly every field of human knowledge. we demonstrate the usefulness of our approach by user experiments.
feature engineering on event-centric surrogate documents to improve search results. we investigate the task of re-ranking search results based on query log information. prior work has considered this problem as either the task of learning document rankings of using features based on user behavior, or as the task of enhancing documents and queries using log data. our contribution combines both. we distill log information into event-centric surrogate documents (esds), and extract features from these esds to be used in a learned ranking function. our experiments on a legal corpus demonstrate that features engineered on surrogate documents lead to improved rankings, in particular when the original ranking is of poor quality.
multi-task learning for learning to rank in web search. both the quality and quantity of training data have significant impact on the performance of ranking functions in the context of learning to rank for web search. due to resource constraints, training data for smaller search engine markets are scarce and we need to leverage existing training data from large markets to enhance the learning of ranking function for smaller markets. in this paper, we present a boosting framework for learning to rank in the multi-task learning context for this purpose. in particular, we propose to learn non-parametric common structures adaptively from multiple tasks in a stage-wise way. an algorithm is developed to iteratively discover super-features that are effective for all the tasks. the estimation of the functions for each task is then learned as a linear combination of those super-features. we evaluate the performance of this multi-task learning method for web search ranking using data from a search engine. our results demonstrate that multi-task learning methods bring significant relevance improvements over existing baseline methods.
cluster based rank query over multidimensional data streams. many data stream monitoring applications involve rank queries and hence a number of efficient evaluation algorithms are proposed recently. most of these techniques assume that rank queries are executed directly over the whole data space. however, we observe that many applications often require to perform clustering over the data streams before rank queries are run on each cluster. to address the problem, we propose a novel algorithm for integral clustering and ranking processing and we refer to such integrated queries as cluster-based rank queries. the algorithm includes two phases, namely the online phase which maintains the required data structures and statistics, and the query phase which uses these data structures to process queries. extensive experiments indicate that the proposed algorithm is efficient in both space consumption and query processing.
efficient multi-class unlabeled constrained semi-supervised svm. semi-supervised learning has been successfully applied to many fields such as knowledge management, information retrieval and data mining as it can utilize both labeled and unlabeled data. in this paper, we propose a general semi-supervised framework for multi-class categorization. many classical supervised and semi-supervised method dealing with binary classification or multi-class classification including the standard regularization and the manifold regularization can be viewed as special cases of this framework. based on this framework, we propose a novel method called multi-class unlabeled constrained svm(mcucsvm) and its special case: multi-class laplacian svm(mclapsvm). we then put forward a general kernel version semi-supervised dual coordinate descent algorithm to efficiently solve mcucsvm and makes it more applicable to problems with large number of classes and large scale labeled data. both rigorous theory and promising experimental results on four real datasets show the great performance and remarkable efficiency of mcucsvm and mclapsvm.
web search result summarization: title selection algorithms and user satisfaction. eye tracking experiments have shown that titles of web search results play a crucial role in guiding a user's search process. we present a machine-learned algorithm that trains a boosted tree to pick the most relevant title for a web search result. we compare two modeling approaches: i) using absolute editorial judgments and ii) using pairwise preference judgments. we find that the pairwise modeling approach gives better results in terms of three offline metrics. we present results of our models in four regions. we also describe a hybrid user satisfaction evaluation process -- search success -- that combines page relevance and user click behavior, and show that our machine-learned algorithm improves in search success.
a term dependency-based approach for query terms ranking. formulating appropriate and effective queries has been regarded as a challenging issue, since a large number of candidate words or phrases could be chosen as query terms to convey users' information needs. in this paper, we propose an approach to rank a set of given query terms according their effectiveness, wherein top ranked terms will be selected as an effective query. our ranking approach exploits and benefits from the underlying relationship between the query terms, and thereby the effective terms can be properly combined into the query. two regression models which capture a rich set of linguistic and statistical properties are used in our approach. experiments on ntcir-4 ad-hoc retrieval tasks demonstrate that the proposed approach can significantly improve retrieval performance, and can be well applied to other problems such as query expansion and querying by text segments.
scalable continuous range monitoring of moving objects in symbolic indoor space. indoor spaces accommodate large populations of individuals. the continuous range monitoring of such objects can be used as a foundation for a wide variety of applications, e.g., space planning, way finding, and security. indoor space differs from outdoor space in that symbolic locations, e.g., rooms, rather than euclidean positions or spatial network locations are important. in addition, positioning based on presence sensing devices, rather than, e.g., gps, is assumed. such devices report the objects in their activation ranges. we propose an incremental, query-aware continuous range query processing technique for objects moving in this setting. a set of critical devices is determined for each query, and only the observations from those devices are used to continuously maintain the query result. due to the limitations of the positioning devices, queries contain certain and uncertain results. a maximum-speed constraint on object movement is used to refine the latter results. a comprehensive experimental study with both synthetic and real data suggests that our proposal is efficient and scalable.
p-rank: a comprehensive structural similarity measure over information networks. with the ubiquity of information networks and their broad applications, the issue of similarity computation between entities of an information network arises and draws extensive research interests. however, to effectively and comprehensively measure "how similar two entities are within an information network" is nontrivial, and the problem becomes even more challenging when the information network to be examined is massive and diverse. in this paper, we propose a new similarity measure, p-rank (penetrating rank), toward effectively computing the structural similarities of entities in real information networks. p-rank enriches the well-known similarity measure, simrank, by jointly encoding both in- and out-link relationships into structural similarity computation. p-rank is proven to be a unified structural similarity framework, under which all state-of-the-art similarity measures, including cocitation, coupling, amsler and simrank, are just its special cases. based on its recursive nature of p-rank, we propose a fixed point algorithm to reinforce structural similarity of vertex pairs beyond the localized neighborhood scope toward the entire information network. our experimental studies demonstrate the power of p-rank as an effective similarity measure in different information networks. meanwhile, under the same time/space complexity, p-rank outperforms simrank as a comprehensive and more meaningful structural similarity measure, especially in large real information networks.
spider: a system for scalable, parallel / distributed evaluation of large-scale rdf data. rdf is a data model for representing labeled directed graphs, and it is used as an important building block of semantic web. due to its flexibility and applicability, rdf has been used in applications, such as semantic web, bioinformatics, and social networks. in these applications, large-scale graph datasets are very common. however, existing techniques are not effectively managing them. in this paper, we present a scalable, efficient query processing system for rdf data, named spider, based on the well-known parallel/distributed computing framework, hadoop. spider consists of two major modules (1) the graph data loader, (2) the graph query processor. the loader analyzes and dissects the rdf data and places parts of data over multiple servers. the query processor parses the user query and distributes sub queries to cluster nodes. also, the results of sub queries from multiple servers are gathered (and refined if necessary) and delivered to the user. both modules utilize the mapreduce framework of hadoop. in addition, our system supports some features of sparql query language. this prototype will be foundation to develop real applications with large-scale rdf graph data.
empirical justification of the gain and discount function for ndcg. the ndcg measure has proven to be a popular measure of retrieval effectiveness utilizing graded relevance judgments. however, a number of different instantiations of ndcg exist, depending on the arbitrary definition of the gain and discount functions used (1) to dictate the relative value of documents of different relevance grades and (2) to weight the importance of gain values at different ranks, respectively. in this work we discuss how to empirically derive a gain and discount function that optimizes the efficiency or stability of ndcg. first, we describe a variance decomposition analysis framework and an optimization procedure utilized to find the efficiency- or stability-optimal gain and discount functions. then we use trec data sets to compare the optimal gain and discount functions to the ones that have appeared in the ir literature with respect to (a) the efficiency of the evaluation, (b) the induced ranking of systems, and (c) the discriminative power of the resulting ndcg measure.
exploring relevance for clicks. mining feedback information from user click-through data is an important issue for modern web retrieval systems in terms of architecture analysis, performance evaluation and algorithm optimization. for commercial search engines, user click-through data contains useful information as well as large amount of inevitable noises. this paper proposes an approach to recognize reliable and meaningful user clicks (referred to as relevant clicks, rcs) in click-through data. by modeling user click-through behavior on search result lists, we propose several features to separate rcs from click noises. a learning algorithm is presented to estimate the quality of user clicks. experimental results on large scale dataset show that: 1) our model effectively identifies rcs in noisy click-through data; 2) different from previous click-through analysis efforts, our approach works well for both hot queries and long-tail queries.
text summarization model based on the budgeted median problem. we propose a multi-document generic summarization model based on the budgeted median problem. our model selects sentences to generate a summary so that every sentence in the document cluster can be assigned to and be represented by a sentence in the summary as much as possible. the advantage of this model is that it covers the entire relevant part of the document cluster through sentence assignment and can incorporate asymmetric relations between sentences such as textual entailment.
analyzing and evaluating query reformulation strategies in web search logs. users frequently modify a previous search query in hope of retrieving better results. these modifications are called query reformulations or query refinements. existing research has studied how web search engines can propose reformulations, but has given less attention to how people perform query reformulations. in this paper, we aim to better understand how web searchers refine queries and form a theoretical foundation for query reformulation. we study users' reformulation strategies in the context of the aol query logs. we create a taxonomy of query refinement strategies and build a high precision rule-based classifier to detect each type of reformulation. effectiveness of reformulations is measured using user click behavior. most reformulation strategies result in some benefit to the user. certain strategies like add/remove words, word substitution, acronym expansion, and spelling correction are more likely to cause clicks, especially on higher ranked results. in contrast, users often click the same result as their previous query or select no results when forming acronyms and reordering words. perhaps the most surprising finding is that some reformulations are better suited to helping users when the current results are already fruitful, while other reformulations are more effective when the results are lacking. our findings inform the design of applications that can assist searchers; examples are described in this paper.
message family propagation for ising mean field based on iteration tree. ising mean field is a basic variational inference method for ising model, which can provide an effective approximate solution for large-scale inference problem. the main idea is to transform a probabilistic inference problem into a functional extremum problem by variational calculus, and solve the functional extremum problem to obtain approximate marginal distributions. the process of solving the functional extremum is an important step and a computational core for variational inference. but the traditional full variational iteration methods make the variable information intercross with each other deeply. from the view of incomplete variational iterations, we propose a message family propagation method for ising mean field to compute a marginal distribution family of object variable. first we define the concepts of iteration tree and pruning iteration tree to describe the iteration computation process of ising mean field inference. then we design the message family propagation method based on the iteration trees. the method propagates mean field message families and belief message families from bottom to top of the iteration tree, and presents a marginal distribution family of variable in root node. finally we prove the marginal distribution bound theorem, which shows that the marginal distribution family computed by the method in the pruning iteration tree contains the exact marginal stribution. theoretical and experimental results illustrate that the message family propagation method is valid and the marginal distribution bounds are tight.
xqgen: an algebra-based xpath query generator for micro-benchmarking. we propose xqgen, a stand-alone, algebra-based xpath generator to aid engineers in testing and improving the design of xml query engines. xqgen takes an xml schema sketch and user configurations, such as number of queries, query types, duplication factors, and branching factors as input, and generates a set of queries that comform to the schema and configurations. in addition, given a set of label-paths as workload input, xqgen is capable of generating query sets that honor the workload.
graph-based transfer learning. transfer learning is the task of leveraging the information from labeled examples in some domains to predict the labels for examples in another domain. it finds abundant practical applications, such as sentiment prediction, image classification and network intrusion detection. in this paper, we propose a graph-based transfer learning framework. it propagates the label information from the source domain to the target domain via the example-feature-example tripartite graph, and puts more emphasis on the labeled examples from the target domain via the example-example bi-partite graph. our framework is semi-supervised and non-parametric in nature and thus more flexible. we also develop an iterative algorithm so that our framework is scalable to large-scale applications. it enjoys the theoretical property of convergence. compared with existing transfer learning methods, the proposed framework propagates the label information to both the features irrelevant to the source domain and the unlabeled examples in the target omain via the common features in a principled way. experimental results on 3 real data sets demonstrate the effectiveness of our algorithm.
navigational path privacy protection: navigational path privacy protection. navigational path query, one of the most popular location-based services (lbss), determines a route from a source to a destination on a road network. however, issuing path queries to some non-trustworthy service providers may pose privacy threats to the users. for instance, given a query requesting for a path from a residential address to a psychiatrist, some adversaries may deduce "who is related to what disease". in this paper, we present an obfuscator framework that reduces the likelihood of path queries being revealed, while supporting different user privacy protection needs and retaining query evaluation efficiency. the framework consists of two major components, namely, an obfuscator and an obfuscated path query processor. the former formulates obfuscated path queries by intermixing true and fake sources and destinations and the latter facilitates efficient evaluation of the obfuscated path queries in an lbs server. the framework supports three types of obfuscated path queries, namely, independent obfuscated path query, shared obfuscated path query, and anti-collusion obfuscated path query. our proposal strikes a balance between privacy protection strength and query processing overheads, while enhancing privacy protection against collusion attacks. finally, we validate the proposed ideas and evaluate the performance of our framework based on an extensive set of empirical experiments.
matching stream patterns of various lengths and tolerances. continuously identifying pre-defined patterns in a streaming time series has strong demand in various applications. while most existing works assume the patterns are in equal length and tolerance, this work focuses on the problem where the patterns have various lengths and tolerances, a common situation in the real world. the challenge of this problem roots on the strict space and time requirements of processing the arriving and expiring data in high-speed stream, combined with difficulty of coping with a large number of patterns with various lengths and tolerances. we introduce a novel concept of converging envelope which bounds the tolerance of a group of patterns in various tolerances and equal length and thus dramatically reduces the number of patterns for similarity computation. the basic idea of converging envelope has potential to more general index problems. to index patterns in various lengths and tolerances, we partition patterns into sub-patterns in equal length and an multi-tree index is developed in this paper.
exploiting term relationship to boost text classification. document classification provides an effective way to handle the explosive online textual data. however, in practical classification settings, we face the so-called feature sparsity problem caused by a lack of training documents or the shortness of text to be classified. in this paper, we solve the sparsity problem by exploiting term relationships along with naive bayes classifiers. the first method is to estimate term relationships based on the co-occurrence information of two terms in a certain context. the second method estimates the term relationships based on the distribution of terms over different hierarchical categories in a publicly available document taxonomy. thereafter, term relationship is used to augment naive bayes classifiers. we test our methods on two open-domain data sets to demonstrate its advantages. the experimental results show that our method can significantly improve the classification performance, especially when we do not have enough training data or the texts are web search queries.
effective anonymization of query logs. user search query logs have proven to be very useful, but have vast potential for misuse. several incidents have shown that simple removal of identifiers is insufficient to protect the identity of users. publishing such inadequately anonymized data can cause severe breach of privacy. while significant effort has been expended on coming up with anonymity models and techniques for microdata, there is little corresponding work for query log data. query logs are different in several important aspects, such as the diversity of queries and the causes of privacy breach. this necessitates the need to design privacy models and techniques specific to this environment. this paper takes a first cut at tackling this challenge. our main contribution is to define effective anonymization models for query log data along with proposing techniques to achieve such anonymization. we analyze the inherent utility and privacy tradeoff, and experimentally validate the performance of our techniques.
matchsim: a novel neighbor-based similarity measure with maximum neighborhood matching. the problem of measuring similarity between web pages arises in many important web applications, such as search engines and web directories. in this paper, we propose a novel neighbor-based similarity measure called matchsim, which uses only the neighborhood structure of web pages. technically, matchsim recursively defines similarity between web pages by the average similarity of the maximum matching between their neighbors. our method extends the traditional methods which simply count the numbers of common and/or different neighbors. it also successfully overcomes a severe counterintuitive loophole in simrank, due to its strict consistency with the intuitions of similarity. we give the computational complexity of matchsim iteration. the accuracy of matchsim is compared against others on two real datasets. the results show that our method performs best in most cases.