User Query Logs Research Articles

Implementation of computer network user behavior forensic analysis system based on speech data system log

With the rapid development of Internet technology and the popularization and application of various terminals, human beings live in the era of big data. In the process of people's daily use of the network, all kinds of data are produced at all times. At the same time, the problem of information security is increasingly prominent and the situation is more and more complex. As the increasing threat of information security has caused irreparable economic losses to human beings, which seriously hinders the further development of information technology, it is urgent to combat computer crime. When users use search engine to get information, the whole process is recorded, including the user's query log. Through the analysis of these query logs, we can indirectly get the real needs of users, so as to provide reference for the optimization and improvement of search engine. The analysis of the network user behavior requires the analysis of the data generated by the network user behavior. The traditional manual data analysis and the single computer data transmission mode have not been able to better analyze the increasing data information. Therefore, we should think of an effective technology, which can effectively analyze the huge and complex data and show the results. Based on the log analysis of voice data system, this paper constructs a user behavior analysis system under the network environment. Experimental results show that the method proposed in this paper can effectively reflect the behavior of network users, and timely feedback.

A Query Understanding Framework for Earth Data Discovery

One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and temporal information from a query or understanding the query with ontology. No research in the geospatial domain has investigated user queries in a systematic way. Here, we propose a query understanding framework and apply it to fill the gap by better interpreting a user’s search intent for Earth data search engines and adopting knowledge that was mined from metadata and user query logs. The proposed query understanding tool contains four components: spatial and temporal parsing; concept recognition; Named Entity Recognition (NER); and, semantic query expansion. Spatial and temporal parsing detects the spatial bounding box and temporal range from a query. Concept recognition isolates clauses from free text and provides the search engine phrases instead of a list of words. Name entity recognition detects entities from the query, which inform the search engine to query the entities detected. The semantic query expansion module expands the original query by adding synonyms and acronyms to phrases in the query that was discovered from Web usage data and metadata. The four modules interact to parse a user’s query from multiple perspectives, with the goal of understanding the consumer’s quest intent for data. As a proof-of-concept, the framework is applied to oceanographic data discovery. It is demonstrated that the proposed framework accurately captures a user’s intent.

User Queries of the Online Standard Slovenian Dictionary and 2001 Slovenian Normative Guide at bos.zrc-sazu.si

This article analyzes the user query logs of the online versions of two Slovenian dictionaries—Slovar slovenskega knjižnega jezika (Standard Slovenian Dictionary, SSKJ) and Slovenski pravopis 2001 (2001 Slovenian Normative Guide, SP 2001)—at bos.zrc-sazu. si (BOS), which for almost a whole decade (2000–2010) served as a portal for searching through various text collections and dictionaries prepared or adapted for the online environment by the ZRC SAZU Fran Ramovš Institute of the Slovenian Language in Ljubljana. We focused on unsuccessful queries. It turned out that for new-generation dictionaries—that is, dictionaries primarily designed for online use—users need search engines with hints, search engines with corrections for unsuccessful queries, and combined search engines for various dictionaries, and in the dictionary itself they need information that is as complete and unabridged as possible.

MINING USER NAVIGATION PATTERNS FOR EFFICIENT RELEVANCE FEEDBACK FOR CBIR

In today’s modernized world, content based image retrieval (CBIR) is considered as a bastion in image retrieval system. For making CBIR most suitable and productive technique, relevance feedback technique is used in conjunction with CBIR for producing more specific results which are obtained by taking feedback from user. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks for production of best search results, particularly in huge database. But this seems of no use in real world applications. In this paper, we propose a novel method, NRPF (Navigation pattern based relevance feedback method is used for enhancing effectiveness and efficiency in CBIR while copying large scale image data. In terms of efficiency, the iterations of feedback will get reduced drastically reduced substantially by using the navigation patterns discovered from the user query log. Effectiveness of our proposed search algorithm NPRF Search makes use of the discovered navigation patterns and it also produces query refinement strategies in other three kinds, Query Point Movement (QPM), Query Expansion (QEX) and Query Reweighting (QR), to converge the search space toward the user’s intention effectively. For this purpose NPRF systems are used for increasing quality of retrieved image. The experimental shows NPRF outperforms other established methods considerably in terms of precision, coverage, and number of feedbacks.

CI-Rank: Collective importance ranking for keyword search in databases

Keyword search over databases, popularized by keyword search in WWW, allows ordinary users to access database information without the knowledge of structured query languages and database schemas. Most of the previous studies in this area use IR-style ranking, which fail to consider the importance of the query answers. In this paper, we propose CI-Rank, a new approach to keyword search in databases, which considers the importance of individual nodes in a query answer and the cohesiveness of the result structure in a balanced way. CI-Rank is built upon a carefully designed model called Random Walk with Message Passing that helps capture the relationships between different nodes in the query answer. This model lends itself well to adaptation to user preferences. We develop a branch and bound algorithm to support the efficient generation of top-k query answers. Indexing methods are also introduced to further speed up the run-time processing of queries. Extensive experiments conducted on two real data sets with a real user query log confirm the effectiveness and efficiency of CI-Rank.

Dynamic Query Intent Prediction from a Search Log Stream

The users that used search engines are obligated to express their goals in few words (queries). Sometimes search queries are ambiguous. Moreover, the users' intents are dynamically evolving. This paper analyzes the user's query logs to classify the related queries, the related intent topic categories and the related intent types and use this classification to dynamically predict the users' future queries, its intent topic and its intent type. AOL Search Query Log is taken as an experimental data set. Then use evaluation metrics to evaluate the prediction results.

Top-K Search Query Grouping using SOM Clustering for Search Engine

is important task for any recommendation system. Clustering method suggested by many researchers for search engine optimization. Search engine help user for better searching by user's query recommendation. Clustering is helpful for finding actual relation between different queries which are not same as they seems. But do clustering of user query is also a difficult task because of user enters lots of type and varying queries. Many time these queries may very short to get their real meaning and also can generate different meanings. Any single query may have various meaning on other hand many different query words may have common meaning for searching contents. Lots of clustering methods are given in last decades for search engine optimization but these methods unable to proper utilization various information hidden in user query log. This paper gives a novel clustering approach based on to identify query similarity and apply SOM clustering for effective clustering results. We propose a novel similarity matrix for user queries by uses of URL clicked by user trough searching results. Text similarity and time similarity are also measure for calculating similarity between two queries. This method shows good results within clustering performance to compare with other existing methods.

Chinese Organization Name Recognition in User Query Log

Chinese Organization Name Recognition in User Query Log

Intent-Based User Segmentation with Query Enhancement

With the rapid advancement of the internet, accurate prediction of user's online intent underlying their search queries has received increasing attention from online advertising community. This paper aims to address the major challenges with user queries in the context of behavioral targeting advertising by proposing a query enhancement mechanism that augments user's queries by leveraging a user query log. The empirical evaluation demonstrates that the authors' methodology for query enhancement achieves greater improvement than the baseline models in both intent-based user classification and user segmentation. Different from traditional user segmentation methods, which take little semantics of user behaviors into consideration, the authors propose a novel user segmentation strategy by incorporating the query enhancement mechanism with a topic model to mine the relationships between users and their behaviors in order to segment users in a semantic manner. Comparing with a classical clustering algorithm, K-means, the experimental results indicate that the proposed user segmentation strategy helps improve behavioral targeting effectiveness significantly. This paper also proposes an alternative to define user's search intent for the evaluation purpose, in the case that the dataset is sanitized. This approach automatically labels users in a click graph, which are then used in training an intent-based user classifier.

Assisting web search using query suggestion based on word similarity measure and query modification patterns

One of the useful tools offered by existing web search engines is query suggestion (QS), which assists users in formulating keyword queries by suggesting keywords that are unfamiliar to users, offering alternative queries that deviate from the original ones, and even correcting spelling errors. The design goal of QS is to enrich the web search experience of users and avoid the frustrating process of choosing controlled keywords to specify their special information needs, which releases their burden on creating web queries. Unfortunately, the algorithms or design methodologies of the QS module developed by Google, the most popular web search engine these days, is not made publicly available, which means that they cannot be duplicated by software developers to build the tool for specifically-design software systems for enterprise search, desktop search, or vertical search, to name a few. Keyword suggested by Yahoo! and Bing, another two well-known web search engines, however, are mostly popular currently-searched words, which might not meet the specific information needs of the users. These problems can be solved by WebQS, our proposed web QS approach, which provides the same mechanism offered by Google, Yahoo!, and Bing to support users in formulating keyword queries that improve the precision and recall of search results. WebQS relies on frequency of occurrence, keyword similarity measures, and modification patterns of queries in user query logs, which capture information on millions of searches conducted by millions of users, to suggest useful queries/query keywords during the user query construction process and achieve the design goal of QS. Experimental results show that WebQS performs as well as Yahoo! and Bing in terms of effectiveness and efficiency and is comparable to Google in terms of query suggestion time.

Pragmatic correlation analysis for probabilistic ranking over relational data

It is widely recognized that effective ranking methods for relational data (e.g., tuples) enable users to overcome the limitations of the traditional Boolean retrieval model and the hardness of structured query writing. To determine the rank of a tuple, term frequency-based methods, such as tf×idf (term frequency×inverse document frequency) schemes, have been commonly adopted in the literature by simply considering a tuple as a single document. However, in many cases, we have noted that tf×idf schemes may not produce effective rankings or specific orderings for relational data with categorical attributes, which is pervasive today. To support fundamental aspects of relational data, we apply the notions of correlation analysis to estimate the extent of relationships between queries and data. This paper proposes a probabilistic ranking model to exploit statistical relationships that exist in relational data of categorical attributes. Given a set of query terms, information on correlative attribute values to the query terms is used to estimate the relevance of the tuple to the query. To quantify the information, we compute the extent of the dependency between correlative attribute values on a Bayesian network. Moreover, we avoid the prohibitive cost of computing insignificant ranking features based on a limited assumption of node independence. Our probabilistic ranking model is domain-independent and leverages only data statistics without any prior knowledge such as user query logs. Experimental results show that our work improves the effectiveness of rankings for real-world datasets and has a reasonable query processing efficiency compared to related work.

Spoken Document Retrieval Leveraging Unsupervised and Supervised Topic Modeling Techniques

This paper describes the application of two attractive categories of topic modeling techniques to the problem of spoken document retrieval (SDR), viz. document topic model (DTM) and word topic model (WTM). Apart from using the conventional unsupervised training strategy, we explore a supervised training strategy for estimating these topic models, imagining a scenario that user query logs along with click-through information of relevant documents can be utilized to build an SDR system. This attempt has the potential to associate relevant documents with queries even if they do not share any of the query words, thereby improving on retrieval quality over the baseline system. Likewise, we also study a novel use of pseudo-supervised training to associate relevant documents with queries through a pseudo-feedback procedure. Moreover, in order to lessen SDR performance degradation caused by imperfect speech recognition, we investigate leveraging different levels of index features for topic modeling, including words, syllable-level units, and their combination. We provide a series of experiments conducted on the TDT (TDT-2 and TDT-3) Chinese SDR collections. The empirical results show that the methods deduced from our proposed modeling framework are very effective when compared with a few existing retrieval approaches. keywords: spoken document retrieval, topic model, supervised training, pseudo-supervised training, subword-level indexing.

Query- And User-Dependent Approach for Ranking Query Results in Web Databases

Internet has paved the way for the emergence of web databases. Querying such databases for required information has become a common task. Ranking such query results is an open problem to be addressed. The existing solutions such as user profiles, query logs, and database values perform ranking in user independent and/or query independent fashion. This can't provide efficient ranking. This paper presents a new approach known as Query and User Dependent Ranking for giving ranking to query results of deep web. The proposed ranking framework is based on two fundamental aspects to the problem of ranking query results. They are query similarity and user similarity. These similarities are exploited to make efficient ranking of query results. A prototype application is built to test the efficiency of our model. The empirical results revealed that our approach is efficient and can be used in real world applications. Index Terms: Deep web, ranking query results, user similarity, query similarity

Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns

Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. In this paper, we propose a novel method, Navigation-Pattern-based Relevance Feedback (NPRF), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. In terms of efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search algorithm NPRFSearch makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user's intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks. The experimental results reveal that NPRF outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.

Query Rewriting Using Monolingual Statistical Machine Translation

Long queries often suffer from low recall in Web search due to conjunctive term matching. The chances of matching words in relevant documents can be increased by rewriting query terms into new terms with similar statistical properties. We present a comparison of approaches that deploy user query logs to learn rewrites of query terms into terms from the document space. We show that the best results are achieved by adopting the perspective of bridging the “lexical chasm” between queries and documents by translating from a source language of user queries into a target language of Web documents. We train a state-of-the-art statistical machine translation model on query-snippet pairs from user query logs, and extract expansion terms from the query rewrites produced by the monolingual translation system. We show in an extrinsic evaluation in a real-world Web search task that the combination of a query-to-snippet translation model with a query language model achieves improved contextual query expansion compared to a state-of-the-art query expansion model that is trained on the same query log data.

Incremental sequence-based frequent query pattern mining from XML queries

Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i +, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency and scalability and outperform state-of-the-art methods significantly.

WEB IMAGE INFORMATION MINING SYSTEM USING UUP FOR E-BUSINESS INTELLIGENCE

Recently, the web sites such as e-business sites and shopping mall sites deal with a lot of image information. To find a specific image from these image sources, we usually use web search engines or image database engines which rely on keyword only retrievals or color based retrievals with limited search capabilities. This paper presents an intelligent web e-catalog image retrieval system using metadata and user log. We propose the system architecture, the texture and color based image classification and indexing techniques, and representation schemes of user usage patterns. The query can be given by providing keywords, by selecting one or more sample texture patterns, by assigning color values within positional color blocks, or by combining some or all of these factors. The system keeps track of user’s preferences by generating user query logs and automatically adds more search information to subsequent user queries. To demonstrate the usefulness of the proposed system, some experimental results showing recall and precision are also explained.

Refining component description by leveraging user query logs

How to help reusers retrieve components efficiently and conveniently is critical to the success of the component-based software development (CBSD). In the literature, many research efforts have been devoted to the improvement of component retrieval mechanisms. Although various retrieval methods have been proposed, nowadays retrieving software component by the description text is still prevalent in most real-world scenarios. Therefore, the quality of the component description text is vital for the component retrieval. Unfortunately, the descriptions of components often contain improper or even noisy information which could deteriorate the effectiveness of the retrieval mechanism. To alleviate the problem, in this paper, we propose an approach which can improve the component description by leveraging user query logs. The key idea of our approach is to refine the description of a component by extracting proper information from the user query logs. Two different strategies are proposed to carry out the information extraction. The first strategy extracts information for a component only from its own related query logs. Whereas our second strategy further takes logs from similar components into consideration. We performed an experimental study on two different data sets to evaluate the effectiveness of our approach. The experimental results demonstrate that by using either extraction strategy our approach can improve retrieval performance and our approach can be more effective by leveraging the second strategy which utilizes logs from similar components.