An Improved Method for Ranking of Search Results Based on User Interest

2008 ◽  
Author(s):  
Hong-Rong Yang ◽  
Ming Xu ◽  
Ning Zheng
Keyword(s):  
Improved Method ◽  
User Interest ◽  
Search Results

A Re-Ranking Method of Search Results Based on Keyword and User Interest

2010 ◽  
pp. 108-121
Author(s):  
Ming Xu ◽  
Hong-Rong Yang ◽  
Ning Zheng
Keyword(s):  
Experimental Results ◽  
Search Process ◽  
Improved Method ◽  
User Interest ◽  
High Recall ◽  
String Match ◽  
Electronic Evidence ◽  
Search Results ◽  
Text String ◽  
Digital Forensic

It is a pivotal task for a forensic investigator to search a hard disk to find interesting evidences. Currently, most search tools in digital forensic field, which utilize text string match and index technology, produce high recall (100%) and low precision. Therefore, the investigators often waste vast time on huge irrelevant search hits. In this chapter, an improved method for ranking of search results was proposed to reduce human efforts on locating interesting hits. The K-UIH (the keyword and user interest hierarchies) was constructed by both investigator-defined keywords and user interest learnt from electronic evidence adaptive, and then the K-UIH was used to re-rank the search results. The experimental results indicated that the proposed method is feasible and valuable in digital forensic search process.

A Webpage Ranking Algorithm Based on Collaborative Recommendation

2013 ◽  
Vol 765-767 ◽  
pp. 998-1002
Author(s):  
Shao Xuan Zhang ◽  
Tian Liu
Keyword(s):  
Sorting Algorithm ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Search Results ◽  
Collaborative Recommendation ◽  
Interest Model ◽  
Sorting Efficiency ◽  
User Interest Model ◽  
Relevant Calculation ◽  
Personalized Ranking

In view of the present personalized ranking of search results user interest model construction difficult, relevant calculation imprecise problems, proposes a combination of user interest model and collaborative recommendation algorithm for personalized ranking method. The method from the user search history, including the submit query, click the relevant webpage information to train users interest model, then using collaborative recommendation algorithm to obtain with common interests and neighbor users, on the basis of these neighbors on the webpage and webpage recommendation level associated with the users to sort the search results. Experimental results show that: the algorithm the average minimum precision than general sorting algorithm was increased by about 0.1, with an increase in the number of neighbors of the user, minimum accuracy increased. Compared with other ranking algorithms, using collaborative recommendation algorithm is helpful for improving webpage with the user interest relevance precision, thereby improving the sorting efficiency, help to improve the search experience of the user.

scholarly journals Personalised Information Recommender Using Framework for Ontology Alignment Among Digital Libraries

2016 ◽  
Vol 36 (4) ◽  
Author(s):  
Anirban Chakrabarty ◽  
Sudipta Roy
Keyword(s):  
Digital Libraries ◽  
User Profile ◽  
Context Aware ◽  
Human Intervention ◽  
Ontology Alignment ◽  
User Interest ◽  
Search Results ◽  
Efficient Information ◽  
Alignment Model ◽  
Multiple Ontologies

In the digital erantology is considered as one of the powerful tools for knowledge representation and efficient information retrieval. Ontology alignment is a process that discovers mapping between source and target ontologies, where each mapping is a relationship based on some similarity measure. This paper, has presented a new context aware alignment approach that needs little human intervention and it can map multiple ontologies to generate user interest dynamically. The objective is to design and develop an ontology alignment model that provides more benefits to its stakeholders in sharing resources and searching across digital libraries based on priorities of users. The experimental results evidently indicate significant improvement in search results when user profile and navigational pattern ontologies are aligned with digital library ontology.

Clustering Chinese Web Search Results Based on Association Calculation

2011 ◽  
Vol 55-57 ◽  
pp. 1418-1423
Author(s):  
Ying Zhao ◽  
Ya Jun Du ◽  
Qiang Qiang Peng
Keyword(s):  
Associate Degree ◽  
Suffix Tree ◽  
Web Search ◽  
Semantic Information ◽  
Web Pages ◽  
Improved Method ◽  
Search Results ◽  
Sentence Segmentation ◽  
Novel Method ◽  
Clustering Search

Clustering web search results is a kind of solution which help user to find the interested topic by grouping the search results. This paper presents an improved method for clustering search results focused on Chinese web pages. The main contributions of this paper are the following: First, in this paper, a method which identifies the complete semantic information phrase by comparing the attributes of base clusters in the suffix tree document model and the overlap of their document sets is presented. Second, by analyzing the content and structure of title and snippet of Chinese web search results, one way of sentence segmentation is designed and implemented to constructing suffix tree. Third, In order to better respond to the associate degree of terms, a novel method is proposed which compute the distance in sentence-grain of terms' co-occurrences. Finally, the experiment illustrates that the new clustering method provides an efficient and effective way for user browsing and locating sought information.

A personalized search using a semantic distance measure in a graph-based ranking model

2011 ◽  
Vol 37 (6) ◽  
pp. 614-636 ◽  
Author(s):  
Mariam Daoud ◽  
Lynda Tamine ◽  
Mohand Boughanem
Keyword(s):  
Distance Measure ◽  
Similarity Measures ◽  
User Profile ◽  
Semantic Distance ◽  
User Interest ◽  
Document Ranking ◽  
Search Results ◽  
Ranking Models ◽  
Ranking Model ◽  
Extended Graph

The goal of search personalization is to tailor search results to individual users by taking into account their profiles, which include their particular interests and preferences. As these latter are multiple and change over time, personalization becomes effective when the search process takes into account the current user interest. This article presents a search personalization approach that models a semantic user profile and focuses on a personalized document ranking model based on an extended graph-based distance measure. Documents and user profiles are both represented by graphs of concepts issued from predefined web ontology, namely, the Open Directory Project directory (ODP). Personalization is then based on reordering the search results of related queries according to a graph-based document ranking model. This former is based on using a graph-based distance measure combining the minimum common supergraph and the maximum common subgraph between the document and the user profile graphs. We extend this measure in order to take into account a semantic recovery at exact and approximate concept-level matching. Experimental results show the effectiveness of our personalized graph-based ranking model compared with Yahoo and different personalized ranking models performed using classical graph-based measures or vector-space similarity measures.

scholarly journals A Web Search Personalization Based on Probability of Semantic Similarity between User Log and Query with Web Page

2018 ◽  
Vol 7 (4.24) ◽  
pp. 59
Author(s):  
Y. Raju ◽  
Dr D. Suresh Babu ◽  
Dr K. Anuradha
Keyword(s):  
Data Collection ◽  
Semantic Similarity ◽  
Web Search ◽  
Web Page ◽  
User Interest ◽  
Information Requirements ◽  
Log Data ◽  
Search Results ◽  
Search Result ◽  
User Query

Web search personalization is recognized as a competent solution to address the problem of query-relevant search as per the user interest, while it able to present dissimilar search results based upon the preferences and information requirements of users. The popular search engines provide their search results interpreting the user query only, which mostly have unrelated results due to the keywords ambiguity problem. In order to have satisfied and user interesting result, it is important to personalize the results according to their relevancies. In this paper, we propose a Web search Personalization based on a Probability of Semantic Similarity (WP-PSS) between user log and query with search result webpage. It performs a probability of semantic similarities computation between the user query and search result webpage snippet, and compute the frequency of link associated with the log data. Based on these two computed factors a probability of similarities association is computed to group and re-rank the search results for the personalization. Experiment evaluation over a set of multi-domain web searched data collection shows an accuracy improvisation.

An Improved Method for the Preparation and TEM Examination of Sharp Pointed Tips used in APFIM and STM

1990 ◽  
Vol 48 (2) ◽  
pp. 102-103
Author(s):  
E.A. Fischione ◽  
P.E. Fischione ◽  
J.J. Haugh ◽  
M.G. Burke
Keyword(s):  
Atom Probe ◽  
Preparation Process ◽  
Improved Method ◽  
Ion Milling ◽  
Polishing Process ◽  
Probe Field ◽  
Scanning Tunnelling ◽  
Stm Tips ◽  
Tunnelling Microscopy ◽  
Ion Microscopy

A common requirement for both Atom Probe Field-Ion Microscopy (APFIM) and Scanning Tunnelling Microscopy (STM) is a sharp pointed tip for use as either the specimen (APFIM) or the probe (STM). Traditionally, tips have been prepared by either chemical or electropolishing techniques. Recently, ion-milling has been successfully employed in the production of APFIM tips [1]. Conventional electropolishing techniques are applicable to a wide variety of metals, but generally require careful manual adjustments during the polishing process and may also be time-consuming. In order to reduce the time and effort involved in the preparation process, a compact, self-contained polishing unit has been developed. This system is based upon the conventional two-stage electropolishing technique in which the specimen/tip blank is first locally thinned or “necked”, and subsequently electropolished until separation occurs.[2,3] The result of this process is the production of two APFIM or STM tips. A mechanized polishing unit that provides these functions while automatically maintaining alignment has been designed and developed.

Immunoelectron microscopic localization of elastic tissue in archival material using an improved method

1990 ◽  
Vol 48 (3) ◽  
pp. 794-795
Author(s):  
J. C. Fanning ◽  
J. F. White ◽  
R. Polewski ◽  
E. G. Cleary
Keyword(s):  
Normal Animal ◽  
Animal Tissue ◽  
Elastic Tissue ◽  
Human Tissues ◽  
Improved Method ◽  
Tissue Samples ◽  
Archival Material ◽  
Immuno Electron Microscopy ◽  
Arteries And Veins ◽  
Biochemical Examination

Elastic tissue is an important component of the walls of arteries and veins, of skin, of the lungs and in lesser amounts, of many other tissues. It is responsible for the rubber-like properties of the arteries and for the normal texture of young skin. It undergoes changes in a number of important diseases such as atherosclerosis and emphysema and on exposure of skin to sunlight.We have recently described methods for the localizationof elastic tissue components in normal animal and human tissues. In the study of developing and diseased tissues it is often not possible to obtain samples which have been optimally prepared for immuno-electron microscopy. Sometimes there is also a need to examine retrospectively samples collected some years previously. We have therefore developed modifications to our published methods to allow examination of human and animal tissue samples obtained at surgery or during post mortem which have subsequently been: 1. stored frozen at -35° or -70°C for biochemical examination; 2.

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