Search (3733 results, page 1 of 187)

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Abstract

'My Yahoo!' (http://www.my.yahoo.com), 'Apple Personalized Internet Launcher' (http://myhome.apple.com/home/welcome/guest), and 'Your Personal Net' (http://www.ypn.com), are personalized WWW search services that could be useful for selecting Internet resources for the library. Outline the services, how to register and use them and how they could be used in the library

Object

Apple Personalized Internt Launcher

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Abstract

Purpose - With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non-native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross-language information retrieval (CLIR) systems through the means of personalized query expansion. Design/methodology/approach - The user profiles consist of weighted terms computed by using frequency-based methods such as tf-idf and BM25, as well as various latent semantic models trained on monolingual documents and cross-lingual comparable documents. This paper also proposes an automatic evaluation method for comparing various user profile generation techniques and query expansion methods. Findings - Experimental results suggest that latent semantic-weighted user profile representation techniques are superior to frequency-based methods, and are particularly suitable for users with a sufficient amount of historical data. The study also confirmed that user profiles represented by latent semantic models trained on a cross-lingual level gained better performance than the models trained on a monolingual level. Originality/value - Previous studies on personalized information retrieval systems have primarily investigated user profiles and personalization strategies on a monolingual level. The effect of utilizing such monolingual profiles for personalized CLIR remains unclear. The current study fills the gap by a comprehensive study of user profile representation for personalized CLIR and a novel personalized CLIR evaluation methodology to ensure repeatable and controlled experiments can be conducted.

Date

20. 1.2015 18:30:22

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Abstract

Library OPACs have long been the gateways between users and information. They present to users the achievements of library automation, and are the most widely available automated retrieval systems and the first that many user encounter. Current trends in OPAC design are toward a user oriented, individual information service which can meet the different needs of users with a variety of background and interests. Compared with the rather inactive, short term and general information service of conventional systems, this type of system focuses on active, long term and personalized service. Proposes a framework for the design of such an OPAC and discusses some recent developments in personalized information service

Date

4. 8.1998 19:36:22

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Date

1. 8.1996 22:08:06

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Abstract

This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for user preference privacy protection in terms of security, usability, efficiency, and accuracy. Then, we comprehensively review the technical features for all kinds of popular methods for user privacy protection, and analyze their application limitations in personalized information retrieval, according to the constraints of preference privacy protection. The results show that personalized information retrieval has higher requirements for users' privacy protection, i.e., it is required to comprehensively improve the security of users' preference privacy on the untrusted server-side, under the precondition of not changing the platform, algorithm, efficiency, and accuracy of personalized information retrieval. However, all kinds of existing privacy methods still cannot meet the above requirements. This paper is an important study attempt to the problem of user preference privacy protection of personalized information retrieval, which can provide a basic reference and direction for the further study of the problem.

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Abstract

The widely used Web search engines index and recommend individual Web pages in response to a few keywords queries to assist users in locating relevant documents. However, the Web search engines give different users the same answer set, although the users may have different preferences. A personalized Web search would carry out the search for each user according to his or her preferences. To conduct the personalized Web search, the authors provide a novel approach to model the user profile with a self-organizing map (SOM). Their results indicate that SOM is capable of helping the user to find the related category for each query used in the Web search to make a personalized Web search effective.

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Date

22. 2.1999 17:50:10

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Abstract

Bioinformatics is a multifaceted discipline combining many scientific fields including computational biology, statistics, mathematics, molecular biology, and genetics. Bioinformatics enables biomedical investigators to exploit existing and emerging computational technologies to seamlessly store, mine, retrieve, and analyze data from genomics and proteomics technologies. This is achieved by creating unified data models, standardizing data interfaces, developing structured vocabularies, generating new data visualization methods, and capturing detailed metadata that describes various aspects of the experimental design and analysis methods. Already there are a number of related undertakings that are dividing the field into more specialized groups. Clinical Bioinformatics and Biomedical Informatics are emerging as transitional fields to promote the utilization of genomics and proteomics data combined with medical history and demographic data towards personalized medicine, molecular diagnostics, pharmacogenomics and predicting outcomes of therapeutic interventions. The field of bioinformatics will continue to evolve through the incorporation of diverse technologies and methodologies that draw experts from disparate fields to create the latest computational and informational tools specifically design for the biomedical research enterprise.

Date

22. 7.2006 14:21:27

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Abstract

The potential of summary personalization is high, because a summary that would be useless to decide the relevance of a document if summarized in a generic manner, may be useful if the right sentences are selected that match the user interest. In this paper we defend the use of a personalized summarization facility to maximize the density of relevance of selections sent by a personalized information system to a given user. The personalization is applied to the digital newspaper domain and it used a user-model that stores long and short term interests using four reference systems: sections, categories, keywords and feedback terms. On the other side, it is crucial to measure how much information is lost during the summarization process, and how this information loss may affect the ability of the user to judge the relevance of a given document. The results obtained in two personalization systems show that personalized summaries perform better than generic and generic-personalized summaries in terms of identifying documents that satisfy user preferences. We also considered a user-centred direct evaluation that showed a high level of user satisfaction with the summaries.

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Abstract

The delivery of personalized news content depends on the ability to predict user interests. We evaluated different methods for acquiring user profiles based on declared and actual interest in various news topics and items. In an experiment, 36 students rated their interest in six news topics and in specific news items and read on 6 days standard, nonpersonalized editions and personalized (basic or adaptive) news editions. We measured subjective satisfaction with the editions and expressed preferences, along with objective measures, to infer actual interest in items. Users' declared interest in news topics did not strongly predict their actual interest in specific news items. Satisfaction with all news editions was high, but participants preferred the personalized editions. User interest was weakly correlated with reading duration, article length, and reading order. Different measures predicted interest in different news topics. Explicit measures predicted interest in relatively clearly defined topics such as sports, but were less appropriate for broader topics such as science and technology. Our results indicate that explicit and implicit methods should be combined to generate user profiles. We suggest that a personalized newspaper should contain both general information and personalized items, selected based on specific combinations of measures for each of the different news topics. Based on the findings, we present a general model to decide on the personalization of news content to generate personalized editions for readers.

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Abstract

As the volume and breadth of online information is rapidly increasing, ad hoc search systems become less and less efficient to answer information needs of modern users. To support the growing complexity of search tasks, researchers in the field of information developed and explored a range of approaches that extend the traditional ad hoc retrieval paradigm. Among these approaches, personalized search systems and exploratory search systems attracted many followers. Personalized search explored the power of artificial intelligence techniques to provide tailored search results according to different user interests, contexts, and tasks. In contrast, exploratory search capitalized on the power of human intelligence by providing users with more powerful interfaces to support the search process. As these approaches are not contradictory, we believe that they can re-enforce each other. We argue that the effectiveness of personalized search systems may be increased by allowing users to interact with the system and learn/investigate the problem in order to reach the final goal. We also suggest that an interactive visualization approach could offer a good ground to combine the strong sides of personalized and exploratory search approaches. This paper proposes a specific way to integrate interactive visualization and personalized search and introduces an adaptive visualization based search system Adaptive VIBE that implements it. We tested the effectiveness of Adaptive VIBE and investigated its strengths and weaknesses by conducting a full-scale user study. The results show that Adaptive VIBE can improve the precision and the productivity of the personalized search system while helping users to discover more diverse sets of information.

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Abstract

The high adoption of smart mobile devices among consumers provides an opportunity for e-commerce retailers to increase their sales by recommending consumers with real time, personalized coupons that take into account the specific contextual situation of the consumer. Although context-aware recommender systems (CARS) have been widely analyzed, personalized pricing or discount optimization in recommender systems to improve recommendations' accuracy and commercial KPIs has hardly been researched. This article studies how to model user-item personalized discount sensitivity and incorporate it into a real time contextual recommender system in such a way that it can be integrated into a commercial service. We propose a novel approach for modeling context-aware user-item personalized discount sensitivity in a sparse data scenario and present a new CARS algorithm that combines coclustering and random forest classification (CBRF) to incorporate the personalized discount sensitivity. We conducted an experimental study with real consumers and mobile discount coupons to evaluate our solution. We compared the CBRF algorithm to the widely used context-aware matrix factorization (CAMF) algorithm. The experimental results suggest that incorporating personalized discount sensitivity significantly improves the consumption prediction accuracy and that the suggested CBRF algorithm provides better prediction results for this use case.

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Abstract

The high adoption of smart mobile devices among consumers provides an opportunity for e-commerce retailers to increase their sales by recommending consumers with real time, personalized coupons that take into account the specific contextual situation of the consumer. Although context-aware recommender systems (CARS) have been widely analyzed, personalized pricing or discount optimization in recommender systems to improve recommendations' accuracy and commercial KPIs has hardly been researched. This article studies how to model user-item personalized discount sensitivity and incorporate it into a real time contextual recommender system in such a way that it can be integrated into a commercial service. We propose a novel approach for modeling context-aware user-item personalized discount sensitivity in a sparse data scenario and present a new CARS algorithm that combines coclustering and random forest classification (CBRF) to incorporate the personalized discount sensitivity. We conducted an experimental study with real consumers and mobile discount coupons to evaluate our solution. We compared the CBRF algorithm to the widely used context-aware matrix factorization (CAMF) algorithm. The experimental results suggest that incorporating personalized discount sensitivity significantly improves the consumption prediction accuracy and that the suggested CBRF algorithm provides better prediction results for this use case.

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Abstract

Develops frameworks to help Internet media designers address end user information presentation preferences by advancing structures for assessing metadata design variables. Design variables are then linked to user cognitive styles. An underlying theme is that artificial intelligence methodologies may be used to help automate the Internet media design process and to provide personalized and customized experiences. User preferences concerning knowledge acquisition in online experiences provide the basis for discussions of cognitive analysis, and are extended into structural implications for media design and interaction

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Abstract

The explosive growth of the Web and the consequent exigency of the Web personalization domain have gained a key position in the direction of customization of the Web information to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. This work presents an agent-based framework designed to help a user in achieving personalized navigation, by recommending related documents according to the user's responses in similar-pages searching mode. Our agent-based approach is grounded in the integration of different techniques and methodologies into a unique platform featuring user profiling, fuzzy multisets, proximity-oriented fuzzy clustering, and knowledge-based discovery technologies. Each of these methodologies serves to solve one facet of the general problem (discovering documents relevant to the user by searching the Web) and is treated by specialized agents that ultimately achieve the final functionality through cooperation and task distribution.

Date

22. 7.2006 16:59:13

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Abstract

Purpose Continuous knowledge sharing by active users, who are highly active in answering questions, is crucial to the sustenance of social question-and-answer (Q&A) sites. The purpose of this paper is to examine such knowledge sharing considering reason-based elaborate decision and habit-based automated cognitive processes. Design/methodology/approach To verify the research hypotheses, survey data on subjective intentions and web-crawled data on objective behavior are utilized. The sample size is 337 with the response rate of 27.2 percent. Negative binomial and hierarchical linear regressions are used given the skewed distribution of the dependent variable (i.e. the number of answers). Findings Both elaborate decision (linking satisfaction, intentions and continuance behavior) and automated cognitive processes (linking past and continuance behavior) are significant and substitutable. Research limitations/implications By measuring both subjective intentions and objective behavior, it verifies a detailed mechanism linking continuance intentions, past behavior and continuous knowledge sharing. The significant influence of automated cognitive processes implies that online knowledge sharing is habitual for active users. Practical implications Understanding that online knowledge sharing is habitual is imperative to maintaining continuous knowledge sharing by active users. Knowledge sharing trends should be monitored to check if the frequency of sharing decreases. Social Q&A sites should intervene to restore knowledge sharing behavior through personalized incentives. Originality/value This is the first study utilizing both subjective intentions and objective behavior data in the context of online knowledge sharing. It also introduces habit-based automated cognitive processes to this context. This approach extends the current understanding of continuous online knowledge sharing behavior.

Date

20. 1.2015 18:30:22

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Abstract

Purpose - This paper aims to discuss and test the claim that utilization of the personalization techniques can be valuable to improve the efficiency of collaborative information retrieval (CIR) systems. Design/methodology/approach - A new personalized CIR system, called PERCIRS, is presented based on the user profile similarity calculation (UPSC) formulas. To this aim, the paper proposes several UPSC formulas as well as two techniques to evaluate them. As the proposed CIR system is personalized, it could not be evaluated by Cranfield, like evaluation techniques (e.g. TREC). Hence, this paper proposes a new user-centric mechanism, which enables PERCIRS to be evaluated. This mechanism is generic and can be used to evaluate any other personalized IR system. Findings - The results show that among the proposed UPSC formulas in this paper, the (query-document)-graph based formula is the most effective. After integrating this formula into PERCIRS and comparing it with nine other IR systems, it is concluded that the results of the system are better than the other IR systems. In addition, the paper shows that the complexity of the system is less that the complexity of the other CIR systems. Research limitations/implications - This system asks the users to explicitly rank the returned documents, while explicit ranking is still not widespread enough. However it believes that the users should actively participate in the IR process in order to aptly satisfy their needs to information. Originality/value - The value of this paper lies in combining collaborative and personalized IR, as well as introducing a mechanism which enables the personalized IR system to be evaluated. The proposed evaluation mechanism is very valuable for developers of personalized IR systems. The paper also introduces some significant user profile similarity calculation formulas, and two techniques to evaluate them. These formulas can also be used to find the user's community in the social networks.

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Abstract

In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.

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Abstract

Active users of Twitter are often overwhelmed with the vast amount of tweets. In this work we attempt to help users browsing a large number of accumulated posts. We propose a personalized word cloud generation as a means for users' navigation. Various user past activities such as user published tweets, retweets, and seen but not retweeted tweets are leveraged for enhanced personalization of word clouds. The best personalization results are attained with user past retweets. However, users' own past tweets are not as useful as retweets for personalization. Negative preferences derived from seen but not retweeted tweets further enhance personalized word cloud generation. The ranking combination method outperforms the preranking approach and provides a general framework for combined ranking of various user past information for enhanced word cloud generation. To better capture subtle differences of generated word clouds, we propose an evaluation of word clouds with a mean average precision measure.