Personalized Information Retrieval Research Articles

Music Clustering With Features From Different Information Sources

Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying ldquosimilarrdquo artists using features from diverse information sources. In this paper, we first present a clustering algorithm that integrates features from both sources to perform bimodal learning. We then present an approach based on the generalized constraint clustering algorithm by incorporating the instance-level constraints. The algorithms are tested on a data set consisting of 570 songs from 53 albums of 41 artists using artist similarity provided by All Music Guide. Experimental results show that the accuracy of artist similarity identification can be significantly improved.

PERSONALIZING THE SOURCE SELECTION AND THE RESULT MERGING PROCESS

The World Wide Web knows an incessant and very fast development. Currently, finding useful information on the Web is a time consuming process. In this paper, we present PIRS a personalized Information Retrieval System in a distributed environment. Most prior research in distributed information access focused on selecting and merging information that has the most relevant content according to the query but ignored the user's specific needs. The underlying idea is that different users have different backgrounds, goals and interests when seeking information and thus, the same query may cover different specific information needs according to who emitted it. However, with the ever expanding Web, users are faced with a huge number of information resources. Consequently, such query-based information access strategies lead to inaccurate query results. PIRS extends the state of the art in a Web-based information retrieval system in distributed environment. First, it develops models for representing both user and information source using feature based profiles. Second, PIRS expands a user query according to his profile. Third, it develops algorithms for source selection and results merging that personalize the computation of the relevance score of a document in response to the user's query. PIRS has been experimented with several known information source. The experimental results obtained show the effectiveness of our approach.

Personalised Information Retrieval and Access: Concepts, Methods and Practices

Personalised Information Retrieval and Access: Concepts, Methods and Practices

Personalized information retrieval and access: concepts, methods, and practices

Global information retrieval and anywhere, anytime information access has stimulated a need to design and model the personalized information search in a flexible and agile way that can use the specific personalization techniques, algorithms, and available technology infrastructure to satisfy high-level functional requirements for personalization. Personalized Information Retrieval and Access: Concepts, Methods and Practices surveys the main concepts, methods, and practices of personalized information retrieval and access in todays data intensive, dynamic, and distributed environment, and provides students, researchers, and practitioners with authoritative coverage of recent technological advances that are shaping the future of globally distributed information retrieval and anywhere, anytime information access

Agent-Community-based P2P semantic MyPortal information retrieval system architecture

In this paper, we propose a conceptual architecture for a personal semantic Web information retrieval system. It incorporates semantic Web, Web service, P2P and multi-agent technologies to enable n...

A fuzzy clustering method of construction of ontology-based user profiles

With the rapid development of personalized information retrieval, user profile plays an important role. In this paper, we propose a fuzzy clustering method of construction of ontology-based user profiles (FCOU). In the FCOU method, we employ fuzzy clustering techniques combined with optimization techniques to develop ontology-based user profiles. One key feature of FCOU is that it employs an augmented Lagrangian function to create fuzzy clustering model for the construction of user profiles. Another key feature of FCOU is that it employs the combination of FCM, PHR and simulated annealing to develop ontology-based user profiles. The method allows some information to belong to several user profiles simultaneously with different degrees of accuracy.

Personalized information retrieval based on context and ontological knowledge

AbstractContext modeling has long been acknowledged as a key aspect in a wide variety of problem domains. In this paper we focus on the combination of contextualization and personalization methods to improve the performance of personalized information retrieval. The key aspects in our proposed approach are (1) the explicit distinction between historic user context and live user context, (2) the use of ontology-driven representations of the domain of discourse, as a common, enriched representational ground for content meaning, user interests, and contextual conditions, enabling the definition of effective means to relate the three of them, and (3) the introduction of fuzzy representations as an instrument to properly handle the uncertainty and imprecision involved in the automatic interpretation of meanings, user attention, and user wishes. Based on a formal grounding at the representational level, we propose methods for the automatic extraction of persistent semantic user preferences, and live, ad-hoc user interests, which are combined in order to improve the accuracy and reliability of personalization for retrieval.

Phishing forbidden

Most consumers have now been the unfortunate recipient of one of a host of pervasive emails, supposedly sent by their financial institution, requesting personal data. This type of unsolicited correspondence has the intention of directing users to a fake web site, facilitating the unauthorised retrieval of personal financial information which can then be used to fraudulently access a user's bank account.

Personalized information retrieval system in the framework of fuzzy logic

Due to increase in web-based applications, the need for enhanced information retrieval system that accommodate user’s needs become crucial. A wide range of commercial information retrieval systems are based on standard Boolean model and at less scale vector models. Although the deficiencies of these models are now part of text-book knowledge, the development of new models still have to overcome the feasibility and testing challenge. This paper advocates a fuzzy based approach for information retrieval where a new model is put forward. Also, its feasibility and performance are demonstrated through a testing with a large-scale university database and whose results are compared to a standard commercial Boolean model.

Research productivity in the internet era

The present study investigated the relationship between the use of different internet applications and research productivity, controlling for other influences on the latter. The control variables included dummies for country, discipline, gender and type of organization of the respondent; as well as variables for age, recognition, the degree of society-related and career-related motivation for research, and the size of the collaboration network. Simple variance analyses and more complex negative binomial hurdle models point to a positive relationship between internet use (for personal communication, information retrieval and information dissemination) and research productivity. However, the results should be interpreted with caution as it was not possible to test the role of the internet against other pre-internet tools which fulfil the same functions. Thus instance it may not be the use of e-mail per se, but the degree of communicating with colleagues that makes a productive scientist.

Toward intelligent music information retrieval

Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of intelligent music information retrieval. Huron points out that since the preeminent functions of music are social and psychological, the most useful characterization would be based on four types of information: genre, emotion, style,and similarity. This paper introduces Daubechies Wavelet Coefficient Histograms (DWCH)for music feature extraction for music information retrieval. The histograms are computed from the coefficients of the db/sub 8/ Daubechies wavelet filter applied to 3 s of music. A comparative study of sound features and classification algorithms on a dataset compiled by Tzanetakis shows that combining DWCH with timbral features (MFCC and FFT), with the use of multiclass extensions of support vector machine,achieves approximately 80% of accuracy, which is a significant improvement over the previously known result on this dataset. On another dataset the combination achieves 75% of accuracy. The paper also studies the issue of detecting emotion in music. Rating of two subjects in the three bipolar adjective pairs are used. The accuracy of around 70% was achieved in predicting emotional labeling in these adjective pairs. The paper also studies the problem of identifying groups of artists based on their lyrics and sound using a semi-supervised classification algorithm. Identification of artist groups based on the Similar Artist lists at All Music Guide is attempted. The semi-supervised learning algorithm resulted in nontrivial increases in the accuracy to more than 70%. Finally, the paper conducts a proof-of-concept experiment on similarity search using the feature set.

Using cocitation information to estimate political orientation in web documents

This paper introduces a simple method for estimating cultural orientation, the affiliation of online entities in a polarized field of discourse. In particular, cocitation information is used to estimate the political orientation of hypertext documents. A type of cultural orientation, the political orientation of a document is the degree to which it participates in traditionally left- or fight-wing beliefs. Estimating documents' political orientation is of interest for personalized information retrieval and recommender systems. In its application to politics, the method uses a simple probabilistic model to estimate the strength of association between a document and left- and right-wing communities. The model estimates the likelihood of cocitation between a document of interest and a small number of documents of known orientation. The model is tested on three sets of data, 695 partisan web documents, 162 political weblogs, and 198 nonpartisan documents. Accuracy above 90% is obtained from the cocitation model, outperforming lexically based classifiers at statistically significant levels.

Searching for people on Web search engines

The Web is a communication and information technology that is often used for the distribution and retrieval of personal information. Many people and organizations mount Web sites containing large amounts of information on individuals, particularly about celebrities. However, limited studies have examined how people search for information on other people, using personal names, via Web search engines. Explores the nature of personal name searching on Web search engines. The specific research questions addressed in the study are: “Do personal names form a major part of queries to Web search engines?”; “What are the characteristics of personal name Web searching?”; and “How effective is personal name Web searching?”. Random samples of queries from two Web search engines were analyzed. The findings show that: personal name searching is a common but not a major part of Web searching with few people seeking information on celebrities via Web search engines; few personal name queries include double quotations or additional identifying terms; and name searches on Alta Vista included more advanced search features relative to those on AlltheWeb.com. Discusses the implications of the findings for Web searching and search engines, and further research.

SEFT: a search engine for text

AbstractWe present a software tool called seft which balances the convenience of search tools such as grep with the functionality of full‐text index‐based information retrieval. Based on a novel retrieval heuristic which uses term locality as a guide to relevance, seft combines the freedom of natural language queries with the benefits of a ranked answer list and easy inspection of retrieval results. While not as fast as grep‐style tools, seft provides a valuable facility for impromptu personal information retrieval tasks. Copyright © 2004 John Wiley & Sons, Ltd.

Detecting context-differentiating terms using competitive learning

Personal information agents monitor ongoing user information accesses in order to provide users with context-relevant information. Providing the needed information requires effective methods for identifying the user's task context, based on available information. For user browsing tasks, one approach to context identification is to extract context-determining terms from the documents that the user consults. The thesis of this article is (1) that term extraction for personal information agents can be done by learning terms whose occurrence frequencies have a large variance over time, (2) that indexing and retrieval based on these terms can be at least as effective as standard information retrieval techniques, and (3) that this information can be learned without comprehensive corpus analysis, making it suitable for use in personal information retrieval.We have developed an unsupervised term extraction algorithm, WordSieve, that learns individualized context-differentiating terms for document indexing and retrieval. This article presents a new version of WordSieve, compares its design and performance to our initial approach, and assesses its effectiveness for a controlled personal information retrieval task, compared to three common indexing techniques requiring statistics about the global corpus. In the experiments, the new version of WordSieve generates task-relevant indices of comparable or better quality to common indexing techniques, using only local information.

Towards personalized Internet health information: the STEPPS architecture.

STEPPS (STructured Evaluated Personalized Patient Support) proposes a strategy for integration of electronic patient records with Internet health-related content and its consequent use in personalized information retrieval for patient education. The application domain is the post-discharge support of burn patients in the Netherlands. We developed an electronic patient record interface for structured data collection in burn care. The system's thesaurus was projected to UMLS terms and the corresponding codes were incorporated in our software. A list of topics central to burn patient education was identified and a collection of related Web pages was compiled using meta-search software (Copernic2001Pro). The HTML pages were filed into catalogues by the Collexis indexing-matching software, using the UMLS Metathesaurus as indexing vocabulary. The bilingual (English and Dutch) structured data interface is currently used to create a database of retrospective patient data. Each patient's profile, i.e. set of characteristics employed to personalize information retrieval, can be automatically extracted. We have assembled a collection of more than 2500 Internet pages containing relevant information for burn patients. When patient data is available, the Collexis matching engine will accept the patient's profile as input and retrieve the most relevant HTML documents available in the catalogues. We have addressed some basic issues around the technical feasibility of linking electronic patient record data to online content. Although the functionality of STEPPS is not yet optimal, it contributes to the efforts towards improved relevance of information retrieval. Electronic patient record applications in conjunction with Internet resources can give a significant boost to the availability of tailored health education material. In this context, quality assurance of online health information is an indispensable element.

The constituent structure of autobiographical memory: Autobiographical fluency in people with chronic epilepsy

A total of 59 people with chronic epilepsy recalled autobiographical episodes and personal facts (such as the names of friends or teachers) from various lifetime periods. Also tested was their verbal fluency and their fluency in generating items from semantic categories (animals, vegetables, US presidents, and British prime ministers). Results of a cluster analysis and a common factor analysis confirmed a dissociation between the retrieval of autobiographical information and that of nonpersonal semantic information. There was a similar dissociation between the recall of personal episodes and the recall of the personal information, but the corresponding factors were highly correlated with one another. Finally, verbal fluency performance was significantly correlated with the retrieval of personal information, personal episodes, and common objects, but not with that of public figures. The constituent structure of autobiographical fluency is extremely robust across different populations.

Personalised information retrieval in specialised virtual libraries

The New Information Technology in Engineering Science Field (NIT‐ESF) project focuses on the virtual library’s contribution to the education and research field. Different teams are involved in this project. The team specialised in information retrieval aims to design an intelligent tutoring system capable of performing personalised information retrieval, depending on users’ interests. The proposed system is specially designed for people who have a sight deficiency. These specific persons use slow devices to access scientific textual information, so they have a critical need for systems able to retrieve relevant information quickly. For these reasons, we need intelligent systems to define and manage particular user interests. We aim to improve answer quality by personalising the retrieval and by performing user profile management. For implementation, we have based the information filtering and extraction on SMART features. Some system results are shown in this paper.

Event-based personal retrieval

People who work in a research, academic or business envi ronment often have personal information collections which are large enough to need retrieval aids. A major difference between personal information retrieval and normal docu ment retrieval is that the items to be retrieved are often associated with events in the searcher's life and can be retrieved by their relationship to other events as well as by content. This paper describes some of the background to event-based retrieval and then describes a prototype graph ical event-based retrieval system.

Traveling the New Information Highway

Every new technical advance in communication, as it becomes practical, has been adopted by medicine. As a consequence, the article on the Internet by Glowniak and Bushway in this issue¹ofThe Journalis worthy of the reader's attention, even if the subject of personal and direct information retrieval and message exchange by networked computers is not a part of his or her practice or present mode of continuing medical education. At many levels, interactive long-distance computing is becoming a phenomenon that is difficult to ignore. Like the physical highways before it, the electronic information highway is stretching itself across the nation and around the world, this time by glass fiber networks and satellite, thereby changing how work will be done and, ultimately, how professions will be organized. Clearly, information and communication technologies are converging, so that information exchange is different today than it has ever been in the