To see the other types of publications on this topic, follow the link: Personalized Information Retrieval.

Journal articles on the topic 'Personalized Information Retrieval'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Personalized Information Retrieval.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Zhu, Qiuyu, Dongmei Li, Cong Dai, Qichen Han, and Yi Lin. "PLSA-Based Personalized Information Retrieval with Network Regularization." Journal of Information Technology Research 12, no. 1 (2019): 105–16. http://dx.doi.org/10.4018/jitr.2019010108.

Full text
Abstract:
With the rapid development of the Internet, the information retrieval model based on the keywords matching algorithm has not met the requirements of users, because people with various query history always have different retrieval intentions. User query history often implies their interests. Therefore, it is of great importance to enhance the recall ratio and the precision ratio by applying query history into the judgment of retrieval intentions. For this sake, this article does research on user query history and proposes a method to construct user interest model utilizing query history. Coordinately, the authors design a model called PLSA-based Personalized Information Retrieval with Network Regularization. Finally, the model is applied into academic information retrieval and the authors compare it with Baidu Scholar and the personalized information retrieval model based on the probabilistic latent semantic analysis topic model. The experiment results prove that this model can effectively extract topics and retrieves back results more satisfied for users' requirements. Also, this model improves the effect of retrieval results apparently. In addition, the retrieval model can be utilized not only in the academic information retrieval, but also in the personalized information retrieval on microblog search, associate recommendation, etc.
APA, Harvard, Vancouver, ISO, and other styles
2

Choi, Okkyung, Kangseok Kim, Duksang Wang, Hongjin Yeh, and Manpyo Hong. "Personalized Mobile Information Retrieval System." International Journal of Advanced Robotic Systems 9, no. 1 (2012): 11. http://dx.doi.org/10.5772/50910.

Full text
Abstract:
Building a global Network Relations with the internet has made huge changes in personal information system and even comments left on a webpage of SNS(Social Network Services) are appreciated as important elements that would provide valuable information for someone. Social Network is a relation between individuals or groups, represented in a graph model, which converts the concept of psychological and social relations into a logical structure by using node and link. But, most of the current personalized systems on the basis of Social Network are built and constructed mainly in the PC environment, and the systems are neither designed nor implemented in mobile environment. Hence, the objective of this study is to propose methods of providing Personalized Mobile Information Retrieval System using NFC (Near Field Communication) Smartphone, which will be then used for Smartphone users. Besides, this study aims to verify its efficiency through a comparative analysis of existing studies.
APA, Harvard, Vancouver, ISO, and other styles
3

Zhang, Shu Dong, Yan Chen, and Bei Bei Gao. "Personalized Intelligent Information Retrieval Entrance Mechanism." Advanced Materials Research 108-111 (May 2010): 216–21. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.216.

Full text
Abstract:
In order to solve the problems of difficulties and differences in expression existed in traditional information retrieval, this paper presents a personalized intelligent information retrieval entrance mechanism based on domain ontology, which takes full account of various types of users' domain knowledge level and provide relevant retrieval methods for guiding personalized information, makes all kinds of users in a particular query environment can fully and effectively express their queries intentions.
APA, Harvard, Vancouver, ISO, and other styles
4

El-Ansari, Anas, Abderrahim Beni-Hssane, Mostafa Saadi, and Mohamed El Fissaoui. "PAPIR: privacy-aware personalized information retrieval." Journal of Ambient Intelligence and Humanized Computing 12, no. 10 (2021): 9891–907. http://dx.doi.org/10.1007/s12652-020-02736-y.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Sheng, Zhong Biao, Hua Ping Jia, and Xiao Rong Tong. "Design of Personalized Intelligent Information Retrieval Model Based on Agent." Applied Mechanics and Materials 155-156 (February 2012): 1175–79. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.1175.

Full text
Abstract:
The features of vast distributed dynamic information on Web caused the problem of “overload” and “mislead” while query. Intelligent agent is a way to solve it. After considering the problems of users’ personal interests during the information retrieve adequately, the paper proposes an intelligent information retrieval model based-on Agent. This system integrated domain knowledge and used many arithmetic of learning user’s interest. Each Agent co-operates to finish information retrieval task, manifest the characteristics of intellectualization and individuality of in information retrieval. It is a good way to realize the highly effective intelligent retrieval system research.
APA, Harvard, Vancouver, ISO, and other styles
6

Hong, Ying. "Research of Personalized Intelligent Information Retrieval System Based on Agent." Advanced Materials Research 945-949 (June 2014): 3406–9. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.3406.

Full text
Abstract:
To efficiently retrieve information from the vast source of the internet, search engines are required. There are some search engines that can help people to search for needed information, but they are difficult to ensure precision rate and personalization of information. To solve these problems, this paper proposed a personalized information retrieval system based on meta-search engine. This paper used multi-agent technology to construct the personalized information retrieval system, adopted user knowledge database to create and update user model and improved vector space model algorithm combining with user knowledge database which used in results ranking. Analysis and experiment show that personalized information retrieval system implemented in this paper can improve the precision ratio and can meet the needs of the user's personality requirements.
APA, Harvard, Vancouver, ISO, and other styles
7

Saravanakumar, K., and Mahesh Moturi. "Semantic based Personalized Framework for Information Retrieval." International Journal of Computer Applications 20, no. 4 (2011): 14–17. http://dx.doi.org/10.5120/2423-3254.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

G.Sastry, Hanumat, Venkatadri M, and Lokanatha C. Reddy. "Personalized Information Retrieval Services for Digital Libraries." International Journal of Computer Applications 97, no. 11 (2014): 33–35. http://dx.doi.org/10.5120/17054-7295.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Wu, Zongda, Chenglang Lu, Youlin Zhao, Jian Xie, Dongdong Zou, and Xinning Su. "The Protection of User Preference Privacy in Personalized Information Retrieval: Challenges and Overviews." Libri 71, no. 3 (2021): 227–37. http://dx.doi.org/10.1515/libri-2019-0140.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
10

FU, Chong-guo, and Zhi-zhong TANG. "Peer-to-peer based personalized Web information retrieval." Journal of Computer Applications 30, no. 1 (2010): 114–17. http://dx.doi.org/10.3724/sp.j.1087.2010.00114.

Full text
APA, Harvard, Vancouver, ISO, and other styles
11

MYLONAS, PH, D. VALLET, P. CASTELLS, M. FERNÁNDEZ, and Y. AVRITHIS. "Personalized information retrieval based on context and ontological knowledge." Knowledge Engineering Review 23, no. 1 (2008): 73–100. http://dx.doi.org/10.1017/s0269888907001282.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
12

Khalifi, Hamid, Walid Cherif, Abderrahim El Qadi, and Youssef Ghanou. "Query expansion based on clustering and personalized information retrieval." Progress in Artificial Intelligence 8, no. 2 (2019): 241–51. http://dx.doi.org/10.1007/s13748-019-00178-y.

Full text
APA, Harvard, Vancouver, ISO, and other styles
13

Gong, Songjie. "The Personalized Information Retrieval Model Based on User Interest." Physics Procedia 24 (2012): 817–21. http://dx.doi.org/10.1016/j.phpro.2012.02.122.

Full text
APA, Harvard, Vancouver, ISO, and other styles
14

Wang, Xuyang. "User Interest Model Update Mechanism in Personalized Information Retrieval." Journal of Information and Computational Science 10, no. 7 (2013): 2117–23. http://dx.doi.org/10.12733/jics20101719.

Full text
APA, Harvard, Vancouver, ISO, and other styles
15

Vicente-López, Eduardo, Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Antonio Tagua-Jiménez, and Carmen Tur-Vigil. "An automatic methodology to evaluate personalized information retrieval systems." User Modeling and User-Adapted Interaction 25, no. 1 (2014): 1–37. http://dx.doi.org/10.1007/s11257-014-9148-9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
16

A. Tabrizi, Shayan, Azadeh Shakery, Hamed Zamani, and Mohammad Ali Tavallaei. "PERSON: Personalized information retrieval evaluation based on citation networks." Information Processing & Management 54, no. 4 (2018): 630–56. http://dx.doi.org/10.1016/j.ipm.2018.04.004.

Full text
APA, Harvard, Vancouver, ISO, and other styles
17

GASPARETTI, FABIO, and ALESSANDRO MICARELLI. "PERSONALIZED SEARCH BASED ON A MEMORY RETRIEVAL THEORY." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 02 (2007): 207–24. http://dx.doi.org/10.1142/s0218001407005429.

Full text
Abstract:
Personalization is the ability to retrieve information content related to users' profile and facilitate their information-seeking activities. Several environments, such as the Web, take advantage of personalization techniques because of the large amount of available information. For this reason, there is a growing interest in providing automated personalization processes during the human-computer interaction. In this paper we introduce a new approach for user modeling, which grounds in the Search of Associative Memory (SAM) theory. By means of implicit feedback techniques, the approach is able to unobtrusively recognize user needs and monitor the user working context in order to provide important information useful to personalize traditional search tools and implement recommender systems. Experimental results based on precision and recall measures indicate improvements in comparison with traditional user models.
APA, Harvard, Vancouver, ISO, and other styles
18

Zhou, Dong, Séamus Lawless, Xuan Wu, Wenyu Zhao, and Jianxun Liu. "A study of user profile representation for personalized cross-language information retrieval." Aslib Journal of Information Management 68, no. 4 (2016): 448–77. http://dx.doi.org/10.1108/ajim-06-2015-0091.

Full text
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.
APA, Harvard, Vancouver, ISO, and other styles
19

Ghosh Dastidar, Bhaskar, Devanjan Banerjee, and Subhabrata Sengupta. "An Intelligent Survey of Personalized Information Retrieval using Web Scraper." International Journal of Education and Management Engineering 6, no. 5 (2016): 24–31. http://dx.doi.org/10.5815/ijeme.2016.05.03.

Full text
APA, Harvard, Vancouver, ISO, and other styles
20

Helmy, Tarek, Ahmed Al-Nazer, Saeed Al-Bukhitan, and Ali Iqbal. "Health, Food and User's Profile Ontologies for Personalized Information Retrieval." Procedia Computer Science 52 (2015): 1071–76. http://dx.doi.org/10.1016/j.procs.2015.05.114.

Full text
APA, Harvard, Vancouver, ISO, and other styles
21

Cai, Fei, Wanyu Chen, and Xinliang Ou. "Learning search popularity for personalized query completion in information retrieval." Journal of Intelligent & Fuzzy Systems 33, no. 4 (2017): 2427–35. http://dx.doi.org/10.3233/jifs-17565.

Full text
APA, Harvard, Vancouver, ISO, and other styles
22

Naderi, Hassan, and Beatrice Rumpler. "PERCIRS: a system to combine personalized and collaborative information retrieval." Journal of Documentation 66, no. 4 (2010): 532–62. http://dx.doi.org/10.1108/00220411011052948.

Full text
APA, Harvard, Vancouver, ISO, and other styles
23

OUSSALAH, M., S. KHAN, and S. NEFTI. "Personalized information retrieval system in the framework of fuzzy logic." Expert Systems with Applications 35, no. 1-2 (2008): 423–33. http://dx.doi.org/10.1016/j.eswa.2007.07.060.

Full text
APA, Harvard, Vancouver, ISO, and other styles
24

da Costa Pereira, Célia, Mauro Dragoni, and Gabriella Pasi. "Multidimensional relevance: Prioritized aggregation in a personalized Information Retrieval setting." Information Processing & Management 48, no. 2 (2012): 340–57. http://dx.doi.org/10.1016/j.ipm.2011.07.001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
25

Yu, Yang Xin, and Yi Zhou Zhang. "Personalization Information Retrieval Based on Topic Directory." Advanced Materials Research 712-715 (June 2013): 2659–63. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2659.

Full text
Abstract:
Personalization information retrieval is very useful in information retrieval system, the user profile can be used to represent the favorites or interests of user. This paper introduces how to automatically learn user interests, build user profiles and re-rank search results.A topic directory method is proposed to calculate the semantic similarity, which takes multi-inheritance into consideration, and then optimize the computing process based on the tree structure of inheritance relationship. Experiments are conducted to compare our method with the popular directory based search methods (e.g., Google Directory Search). Experimental results show that the proposed method in this paper can effectively capture personalization and improve the accuracy of personalized search over existing approaches.
APA, Harvard, Vancouver, ISO, and other styles
26

KUO, YAU-HWANG, JANG-PONG HSU, and MONG-FONG HORNG. "NEURO-FUZZY BASED SEARCH ROBOT FOR SOFTWARE COMPONENTS." International Journal on Artificial Intelligence Tools 08, no. 02 (1999): 119–35. http://dx.doi.org/10.1142/s0218213099000099.

Full text
Abstract:
A personalized search robot is developed as one major mechanism of a personalized software component retrieval system. This search robot automatically finds out the Web servers providing reusable software components, extracts needed software components from servers, classifies the extracted components, and finally establishes their indexing information for local component retrieval in the future. For adaptively tuning the performance of software component extraction and classification, an adaptive thesaurus and an adaptive classifier, realized by neuro-fuzzy models, are embedded in this search robot, and their learning algorithms are also developed. A prototype of the personalized software component retrieval system including the search robot has been implemented to confirm its validity and evaluate the performance. Furthermore, the framework of proposed personalized search robot could be extended to the search and classification of other kinds of Internet documents.
APA, Harvard, Vancouver, ISO, and other styles
27

Bingmei Zhao, and Lifeng Wei. "A Kind of Personalized Employment Information Retrieval System Based on J2EE." International Journal of Digital Content Technology and its Applications 7, no. 6 (2013): 174–81. http://dx.doi.org/10.4156/jdcta.vol7.issue6.20.

Full text
APA, Harvard, Vancouver, ISO, and other styles
28

Ye, Shuyun, John A. Dawson, and Christina Kendziorski. "Extending Information Retrieval Methods to Personalized Genomic-Based Studies of Disease." Cancer Informatics 13s7 (January 2014): CIN.S16354. http://dx.doi.org/10.4137/cin.s16354.

Full text
Abstract:
Genomic-based studies of disease now involve diverse types of data collected on large groups of patients. A major challenge facing statistical scientists is how best to combine the data, extract important features, and comprehensively characterize the ways in which they affect an individual's disease course and likelihood of response to treatment. We have developed a survival-supervised latent Dirichlet allocation (survLDA) modeling framework to address these challenges. Latent Dirichlet allocation (LDA) models have proven extremely effective at identifying themes common across large collections of text, but applications to genomics have been limited. Our framework extends LDA to the genome by considering each patient as a “document” with “text” detailing his/her clinical events and genomic state. We then further extend the framework to allow for supervision by a time-to-event response. The model enables the efficient identification of collections of clinical and genomic features that co-occur within patient subgroups, and then characterizes each patient by those features. An application of survLDA to The Cancer Genome Atlas ovarian project identifies informative patient subgroups showing differential response to treatment, and validation in an independent cohort demonstrates the potential for patient-specific inference.
APA, Harvard, Vancouver, ISO, and other styles
29

Yoo, Donghee. "Hybrid query processing for personalized information retrieval on the Semantic Web." Knowledge-Based Systems 27 (March 2012): 211–18. http://dx.doi.org/10.1016/j.knosys.2011.10.004.

Full text
APA, Harvard, Vancouver, ISO, and other styles
30

Hochul Jeon, Taehwan Kim, and Joongmin Choi. "Personalized Information Retrieval by Using Adaptive User Profiling and Collaborative Filtering." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 2, no. 4 (2010): 134–42. http://dx.doi.org/10.4156/aiss.vol2.issue4.14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
31

Sadesh, S., and R. C. Suganthe. "Effective Filtering of Query Results on Updated User Behavioral Profiles in Web Mining." Scientific World Journal 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/829126.

Full text
Abstract:
Web with tremendous volume of information retrieves result for user related queries. With the rapid growth of web page recommendation, results retrieved based on data mining techniques did not offer higher performance filtering rate because relationships between user profile and queries were not analyzed in an extensive manner. At the same time, existing user profile based prediction in web data mining is not exhaustive in producing personalized result rate. To improve the query result rate on dynamics of user behavior over time, Hamilton Filtered Regime Switching User Query Probability (HFRS-UQP) framework is proposed. HFRS-UQP framework is split into two processes, where filtering and switching are carried out. The data mining based filtering in our research work uses the Hamilton Filtering framework to filter user result based on personalized information on automatic updated profiles through search engine. Maximized result is fetched, that is, filtered out with respect to user behavior profiles. The switching performs accurate filtering updated profiles using regime switching. The updating in profile change (i.e., switches) regime in HFRS-UQP framework identifies the second- and higher-order association of query result on the updated profiles. Experiment is conducted on factors such as personalized information search retrieval rate, filtering efficiency, and precision ratio.
APA, Harvard, Vancouver, ISO, and other styles
32

Zhang, Yan. "E-Commerce Personalized Recommendation." Advanced Materials Research 989-994 (July 2014): 4996–99. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4996.

Full text
Abstract:
With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site, the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.
APA, Harvard, Vancouver, ISO, and other styles
33

Tang, Hai, and Xian Cheng Zhu. "Research on Personalized Document Retrieval and Ranking Strategy." Applied Mechanics and Materials 697 (November 2014): 456–61. http://dx.doi.org/10.4028/www.scientific.net/amm.697.456.

Full text
Abstract:
In order to obtain proper text information consistent with the users’ cognitive level, a new personalized retrieval method is proposed for the distant learners. At first the user’s personalized cognitive structure is established and this structure can represent the users’ cognitive level formally. Then related concept can be extended around the keywords, which usually reflect their focus of attention. How to extend depennd on the semantic distance which should be measured by some new formula in this article. At last these queried documents should be ranked according to the user’s cognitive level and the difficulty of the document containt. Experiments show that even if some different learners use the same keywords, the query result is consistent with their cognitive level respectively.
APA, Harvard, Vancouver, ISO, and other styles
34

Li, Juntao, Chang Liu, Chongyang Tao, et al. "Dialogue History Matters! Personalized Response Selection in Multi-Turn Retrieval-Based Chatbots." ACM Transactions on Information Systems 39, no. 4 (2021): 1–25. http://dx.doi.org/10.1145/3453183.

Full text
Abstract:
Existing multi-turn context-response matching methods mainly concentrate on obtaining multi-level and multi-dimension representations and better interactions between context utterances and response. However, in real-place conversation scenarios, whether a response candidate is suitable not only counts on the given dialogue context but also other backgrounds, e.g., wording habits, user-specific dialogue history content. To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN). Our contributions are two-fold: (1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information; (2) we perform hybrid representation learning on context-response utterances and explicitly incorporate a customized attention mechanism to extract vital information from context-response interactions so as to improve the accuracy of matching. We evaluate our model on two large datasets with user identification, i.e., personalized Ubuntu dialogue Corpus (P-Ubuntu) and personalized Weibo dataset (P-Weibo). Experimental results confirm that our method significantly outperforms several strong models by combining personalized attention, wording behaviors, and hybrid representation learning.
APA, Harvard, Vancouver, ISO, and other styles
35

Xu, Hong Hua, Ye Tian, Lin Hu, and Quan Bao Gao. "Personalized Layer Retrieve for M-Learning Resources on Users Interest." Applied Mechanics and Materials 448-453 (October 2013): 3596–600. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3596.

Full text
Abstract:
Because of a big amount of m-learning resources, the thesis puts forward the technique of learning resources integration based on granular computing. Then introducing personalized concept into the system, the paper puts forward a personalized layer retrieval method based on users interest. Users information retrieval is opposed to ant colonys foraging action. One-time scanning process of each node of the website is opposed to once ant colonys foraging action period. According to users scanning log information, users interest mode can be dynamically identified. This method is easy to realize and can capture the short-term and long-term changes of users interest quickly and accurately.
APA, Harvard, Vancouver, ISO, and other styles
36

Chen, Hua Yue, and Jing Pu. "To Adapt to Changes in the User Long-Term and Recent Interest of Personalized Recommendation Model." Advanced Materials Research 791-793 (September 2013): 2143–46. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.2143.

Full text
Abstract:
With the development and popularization of the information superhighway, people are surrounded by the sea of information. Exponential expansion of Internet information resources, is the vast amounts of information source, its information organization is heterogeneous, diverse, distribution and other features. therefore, can provide users with effective information recommendation, help users to find the valuable information you need the personalized recommendation system won wide attention in the field of Web information retrieval, and also in actual personalization service system has been widely applied in this paper, the personalized services recommendation system architecture to do some research, proposed a distinguishing the user long-term interests and immediate interests provide information to recommend a new model of personalized recommendation.
APA, Harvard, Vancouver, ISO, and other styles
37

D.Jayaweera, Y., Md Gapar Md. Johar, and S. N. Perera. "Enabling Effective Personalized Learning: Determinants for Knowledge based Web Information Retrieval Systems." International Journal of Computer Applications 116, no. 2 (2015): 19–24. http://dx.doi.org/10.5120/20309-2352.

Full text
APA, Harvard, Vancouver, ISO, and other styles
38

Al-Nazer, Ahmed, and Tarek Helmy. "Semantic Query-manipulation and Personalized Retrieval of Health, Food and Nutrition Information." Procedia Computer Science 19 (2013): 163–70. http://dx.doi.org/10.1016/j.procs.2013.06.026.

Full text
APA, Harvard, Vancouver, ISO, and other styles
39

Chang, Chia-Hui, and Ching-Chi Hsu. "Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval." Computer Networks and ISDN Systems 30, no. 1-7 (1998): 621–23. http://dx.doi.org/10.1016/s0169-7552(98)00076-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
40

Nogueira Junior, Darcio Costa, Isadora Valle Sousa, Frederico Cordeiro Martins, and Marta Macedo Kerr Pinheiro. "LEARNING ANALYTICS FOR WHOM? A REFLEXION ON THE RETRIEVAL OF LEARNING INFORMATION BY THE STUDENT." International Journal for Innovation Education and Research 9, no. 2 (2021): 282–94. http://dx.doi.org/10.31686/ijier.vol9.iss2.2959.

Full text
Abstract:
The present work aims at addressing how the use of Learning Analytics (LA) has enabled the retrieval of learning information by the student oneself, by analyzing data availability, self-management and student autonomy in learning processes inside and outside virtual environments. The bibliographic research conducted had a qualitative nature and consisted of a narrative literature review anchored in the theoretical foundations of information (information retrieval and representation) and Learning Analytics. Two relevant user case studies that dealt with LA were selected from the researched articles - the first analyzed the user approach in an adapted learning context with LA whereas the second analyzed the user approach in a personalized learning context with LA. One concluded that the student, as an information user, still has little access to an effective retrieval of what was consolidated throughout one’s own learning process. Besides, in relation to the effectiveness of LA, in the context of adapted and personalized learning, there was a perceived increase in student performance with regard to the use of activities and tasks.
APA, Harvard, Vancouver, ISO, and other styles
41

Gu, Xiaolong, and Jie Zhang. "Probability model of sensitive similarity measures in information retrieval." International Journal of Advanced Robotic Systems 17, no. 1 (2020): 172988142090142. http://dx.doi.org/10.1177/1729881420901425.

Full text
Abstract:
In today’s Internet age, a lot of data is stored and used, which is very important. In people’s daily life, if these data are sorted, information retrieval technology will be used, and in information retrieval, some information retrieval inaccuracies often appear. Information retrieval model is an important framework and method for fast, complete, and accurate user information retrieval. With the rapid development of information technology, great changes have taken place in people’s production and life. Various information network technologies are widely used in people’s lives. The resulting flow of information shows explosive growth, information retrieval. User requirements are getting higher and higher. How to complete personalized information retrieval in a large amount of mixed information, so that retrieval technology can help us obtain effective retrieval results, has become a realistic problem worth exploring. In this article, the application of probability model based on sensitive similarity measure in information retrieval model is analyzed, and a similarity measure algorithm based on spectral clustering is proposed. By improving the similarity measure, the sensitivity problem of scale parameters is overcome and the retrieval precision is improved. In order to better reflect the superiority of the proposed algorithm, this article compares with ng-jordan-weiss (NJW) and deep sparse subspace clustering (DSSC) algorithms. The experimental results show that the proposed algorithm is superior to NJW and DSSC algorithms for different data sets in different evaluation indicators (Rand and F-measure).
APA, Harvard, Vancouver, ISO, and other styles
42

Zhang, Hong, Yan Hong Ma, Wei Jun Ma, and Zhong Xian Bao. "Study of Distributed Personalized Search Engine." Advanced Materials Research 756-759 (September 2013): 1035–39. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1035.

Full text
Abstract:
Combining distributed computing and data mining techniques, a distributed personalized search engine is put forward to solve the the problems current search engines faced. It has described the theoretical model and algorithmic processing. Under the Hadoop, a distributed platform processing information with Java, the key parts are programmed and implemented. The experimental results show that this theoretical model can improve the accuracy and speed of the user's queries so it can improve the retrieval performance of the search engine.
APA, Harvard, Vancouver, ISO, and other styles
43

Valcarce, Daniel. "Information retrieval models for recommender systems." ACM SIGIR Forum 53, no. 1 (2019): 44–45. http://dx.doi.org/10.1145/3458537.3458545.

Full text
Abstract:
Information retrieval addresses the information needs of users by delivering relevant pieces of information but requires users to convey their information needs explicitly. In contrast, recommender systems offer personalized suggestions of items automatically. Ultimately, both fields help users cope with information overload by providing them with relevant items of information. This thesis aims to explore the connections between information retrieval and recommender systems. Our objective is to devise recommendation models inspired in information retrieval techniques. We begin by borrowing ideas from the information retrieval evaluation literature to analyze evaluation metrics in recommender systems [2]. Second, we study the applicability of pseudo-relevance feedback models to different recommendation tasks [1]. We investigate the conventional top-N recommendation task [5, 4, 6, 7], but we also explore the recently formulated user-item group formation problem [3] and propose a novel task based on the liquidation of long tail items [8]. Third, we exploit ad hoc retrieval models to compute neighborhoods in a collaborative filtering scenario [9, 10, 12]. Fourth, we explore the opposite direction by adapting an effective recommendation framework to pseudo-relevance feedback [13, 11]. Finally, we discuss the results and present our conclusions. In summary, this doctoral thesis adapts a series of information retrieval models to recommender systems. Our investigation shows that many retrieval models can be accommodated to deal with different recommendation tasks. Moreover, we find that taking the opposite path is also possible. Exhaustive experimentation confirms that the proposed models are competitive. Finally, we also perform a theoretical analysis of some models to explain their effectiveness. Advisors : Álvaro Barreiro and Javier Parapar. Committee members : Gabriella Pasi, Pablo Castells and Fidel Cacheda. The dissertation is available at: https://www.dc.fi.udc.es/~dvalcarce/thesis.pdf.
APA, Harvard, Vancouver, ISO, and other styles
44

Lau, Raymond Y. K., and Wenping Zhang. "Non-Monotonic Modeling for Personalized Services Retrieval and Selection." International Journal of Systems and Service-Oriented Engineering 1, no. 2 (2010): 55–68. http://dx.doi.org/10.4018/jssoe.2010040104.

Full text
Abstract:
With growing interest in Semantic Web services and emerging standards, such as OWL, WSMO, and SWSL in particular, the importance of applying logic-based models to develop core elements of the intelligent Semantic Web has been more closely examined. However, little research has been conducted in Semantic Web services on issues of non-mono-tonicity and uncertainty of Web services retrieval and selection. In this paper, the authors propose a non-monotonic modeling and uncertainty reasoning framework to address problems related to adaptive and personalized services retrieval and selection in the context of micro-payment processing of electronic commerce. As intelligent payment service agents are faced with uncertain and incomplete service information available on the Internet, non-monotonic modeling and reasoning provides a robust and powerful framework to enable agents to make service-related decisions quickly and effectively with reference to an electronic payment processing cycle.
APA, Harvard, Vancouver, ISO, and other styles
45

Wang, Wenjie, Ling-Yu Duan, Hao Jiang, Peiguang Jing, Xuemeng Song, and Liqiang Nie. "Market2Dish: Health-aware Food Recommendation." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1 (2021): 1–19. http://dx.doi.org/10.1145/3418211.

Full text
Abstract:
With the rising incidence of some diseases, such as obesity and diabetes, the healthy diet is arousing increasing attention. However, most existing food-related research efforts focus on recipe retrieval, user-preference-based food recommendation, cooking assistance, or the nutrition and calorie estimation of dishes, ignoring the personalized health-aware food recommendation. Therefore, in this work, we present a personalized health-aware food recommendation scheme, namely, Market2Dish, mapping the ingredients displayed in the market to the healthy dishes eaten at home. The proposed scheme comprises three components, namely, recipe retrieval, user health profiling, and health-aware food recommendation. In particular, recipe retrieval aims to acquire the ingredients available to the users and then retrieve recipe candidates from a large-scale recipe dataset. User health profiling is to characterize the health conditions of users by capturing the textual health-related information crawled from social networks. Specifically, to solve the issue that the health-related information is extremely sparse, we incorporate a word-class interaction mechanism into the proposed deep model to learn the fine-grained correlations between the textual tweets and pre-defined health concepts. For the health-aware food recommendation, we present a novel category-aware hierarchical memory network–based recommender to learn the health-aware user-recipe interactions for better food recommendation. Moreover, extensive experiments demonstrate the effectiveness of the health-aware food recommendation scheme.
APA, Harvard, Vancouver, ISO, and other styles
46

Zhang, Yan, and Tao Kuang. "The Research of E-Commerce Personalized Recommendation." Applied Mechanics and Materials 556-562 (May 2014): 6762–65. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6762.

Full text
Abstract:
With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site,the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.
APA, Harvard, Vancouver, ISO, and other styles
47

Kelly, Diane, and Nicholas J. Belkin. "A user modeling system for personalized interaction and tailored retrieval in interactive IR." Proceedings of the American Society for Information Science and Technology 39, no. 1 (2005): 316–25. http://dx.doi.org/10.1002/meet.1450390135.

Full text
APA, Harvard, Vancouver, ISO, and other styles
48

Kim, Jee Hyun, Qian Gao, and Young Im Cho. "A Context-Awareness Modeling User Profile Construction Method for Personalized Information Retrieval System." International Journal of Fuzzy Logic and Intelligent Systems 14, no. 2 (2014): 122–29. http://dx.doi.org/10.5391/ijfis.2014.14.2.122.

Full text
APA, Harvard, Vancouver, ISO, and other styles
49

Lee, Seok-Cheol, and Chang-Soo Kim. "Development of User Oriented Geographic Information Retrieval Service Module Based on Personalized Service." Journal of the Korean Association of Geographic Information Studies 14, no. 1 (2011): 49–58. http://dx.doi.org/10.11108/kagis.2011.14.1.049.

Full text
APA, Harvard, Vancouver, ISO, and other styles
50

Vicente-López, Eduardo, Luis M. de Campos, Juan M. Fernández-Luna, and Juan F. Huete. "Use of textual and conceptual profiles for personalized retrieval of political documents." Knowledge-Based Systems 112 (November 2016): 127–41. http://dx.doi.org/10.1016/j.knosys.2016.09.005.

Full text
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!