scholarly journals Recommendation as Collaboration in Web Search

AI Magazine ◽  
2011 ◽  
Vol 32 (3) ◽  
pp. 35-45 ◽  
Author(s):  
Barry Smyth ◽  
Jill Freyne ◽  
Maurice Coyle ◽  
Peter Briggs
Keyword(s):  
Recommender Systems ◽  
Search Engines ◽  
Web Search ◽  
Online Information ◽  
Information Discovery ◽  
Proactive Approach ◽  
Systems Research ◽  
Traditional Approaches ◽  
New Generation

Recommender systems now play an important role in online information discovery, complementing traditional approaches such as search and navigation, with a more proactive approach to discovery that is informed by the users interests and preferences. To date recommender systems have been deployed within a variety of e-commerce domains, covering a range of products such as books, music, movies, and have proven to be a successful way to convert browsers into buyers. Recommendation technologies have a potentially much greater role to play in information discovery however and in this article we consider recent research that takes a fresh look at web search as a fertile platform for recommender systems research as users demand a new generation of search engines that are less susceptible to manipulation and more responsive to searcher needs and preferences.

WEBCONTENT VISUALIZER: A VISUALIZATION SYSTEM FOR SEARCH ENGINES IN SEMATIC WEB

2011 ◽  
Vol 10 (05) ◽  
pp. 913-931 ◽  
Author(s):  
XIANYONG FANG ◽  
CHRISTIAN JACQUEMIN ◽  
FRÉDÉRIC VERNIER
Keyword(s):  
Semantic Web ◽  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Search Query ◽  
Visualization System ◽  
Xml Documents ◽  
Web Search Engines ◽  
New Generation

Since the results from Semantic Web search engines are highly structured XML documents, they cannot be efficiently visualized with traditional explorers. Therefore, the Semantic Web calls for a new generation of search query visualizers that can rely on document metadata. This paper introduces such a visualization system called WebContent Visualizer that is used to display and browse search engine results. The visualization is organized into three levels: (1) Carousels contain documents with the same ranking, (2) carousels are piled into stacks, one for each date, and (3) these stacks are organized along a meta-carousel to display the results for several dates. Carousel stacks are piles of local carousels with increasing radii to visualize the ranks of classes. For document comparison, colored links connect documents between neighboring classes on the basis of shared entities. Based on these techniques, the interface is made of three collaborative components: an inspector window, a visualization panel, and a detailed dialog component. With this architecture, the system is intended to offer an efficient way to explore the results returned by Semantic Web search engines.

scholarly journals Neural Networks in Big Data and Web Search

Data ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 7 ◽  
Author(s):  
Will Serrano
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Recommender Systems ◽  
User Experience ◽  
Search Engines ◽  
Web Search ◽  
Practical Applications ◽  
Survey Paper ◽  
Search Service ◽  
Web Search Engines

As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of crawling and indexing information within the enormous size and scale of the Internet, e-commerce customers and general Web users should not stay confident that the products suggested or results displayed are either complete or relevant to their search aspirations due to the commercial background of the search service. The economic priority of Web-related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers. On the other hand, web search engine and recommender system revenue is obtained from advertisements and pay-per-click. The essential user experience is the self-assurance that the results provided are relevant and exhaustive. This survey paper presents a review of neural networks in Big Data and web search that covers web search engines, ranking algorithms, citation analysis and recommender systems. The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications. Finally, the random neural network is presented with its practical applications to reasoning approaches for knowledge extraction.

Smart Internet Search with Random Neural Networks

2017 ◽  
Vol 25 (2) ◽  
pp. 260-272 ◽  
Author(s):  
Will Serrano
Keyword(s):  
Web Service ◽  
Search Engines ◽  
Web Search ◽  
User Needs ◽  
Online Information ◽  
Internet Search ◽  
Intelligent Search ◽  
Random Neural Network ◽  
The Web ◽  
Random Neural Networks

Web services that are free of charge to users typically offer access to online information based on some form of economic interest of the web service itself. Advertisers who put the information on the web will make a payment to the search services based on the clicks that their advertisements receive. Thus, end users cannot know that the results they obtain from web search engines are exhaustive, or that they actually respond to their needs. To fill the gap between user needs and the information presented to them on the web, Intelligent Search Assistants have been proposed to act at the interface between users and search engines to present data to users in a manner that reflects their actual needs or their observed or stated preferences. This paper presents an Intelligent Internet Search Assistant based on the Random Neural Network that tracks the user’s preferences and makes a selection on the output of one or more search engines using the preferences that it has learned. We also introduce a ‘relevance metric’ to compare the performance of our Intelligent Internet Search Assistant against a few search engines, showing that it provides better performance.

Classification of means and methods of the Web semantic retrieval

2017 ◽  
pp. 030-050
Author(s):  
J.V. Rogushina ◽  
Keyword(s):  
Search Engines ◽  
Domain Knowledge ◽  
Information Needs ◽  
Web Search ◽  
User Interaction ◽  
Query Languages ◽  
Semantic Search ◽  
Semantic Retrieval ◽  
The Web

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.

Quality Assessment of Health Information about Scaphoid Fracture in Chinese Websites (Preprint)

2019 ◽  
Author(s):  
Yaobin Yin ◽  
Jianguang Ji ◽  
Peng Lu ◽  
Wenyao Zhong ◽  
Liying Sun ◽  
...  
Keyword(s):  
Social Networks ◽  
Health Information ◽  
Search Engines ◽  
Medical Information ◽  
Scaphoid Fracture ◽  
Family Members ◽  
Online Information ◽  
Specific Content ◽  
The Mean

BACKGROUND With online health information becoming increasingly popular among patients and their family members, concerns have been raised about the accuracy from the websites. OBJECTIVE We aimed to evaluate the overall quality of the online information about scaphoid fracture obtained from Chinese websites using the local search engines. METHODS We conducted an online search using the keyword “scaphoid fracture” from the top 5 search engines in China, i.e. Baidu, Shenma, Haosou, Sougou and Bing, and gathered the top ranked websites, which included a total of 120 websites. Among them, 81 websites were kept for further analyses by removing duplicated and unrelated one as well as websites requiring payment. These websites were classified into four categories, including forum/social networks, commercials, academics and physician’s personals. Health information evaluation tool DISCERN and Scaphoid Fracture Specific Content Score (SFSCS) were used to assess the quality of the websites. RESULTS Among the 81 Chinese websites that we studied, commercial websites were the most common one accounting more than half of all websites. The mean DISCERN score of the 81 websites was 25.56 and no website had a score A (ranging from 64 to 80).The mean SFSCS score was 10.04 and no website had a score A (range between 24 and 30). In addition, DISCERN and SFSCS scores from academic and physician’s websites were significantly higher than those from the forum/social networks and commercials. CONCLUSIONS The overall quality of health information obtained from Chinese websites about scaphoid fracture was very low, suggesting that patients and their family members should be aware such deficiency and pay special attentions for the medical information obtained by using the current search engines in China.

scholarly journals The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines

2021 ◽  
pp. 089443932110068
Author(s):  
Aleksandra Urman ◽  
Mykola Makhortykh ◽  
Roberto Ulloa
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Large Scale ◽  
Web Search ◽  
Primary Elections ◽  
Virtual Agents ◽  
Search Results ◽  
Presidential Primary ◽  
Large Scale Analysis ◽  
Algorithmic Information

We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research.

A branding model for web search engines

2012 ◽  
Vol 7 (3) ◽  
pp. 195 ◽  
Author(s):  
Lu Zhang ◽  
Bernard J. Jansen ◽  
Anna S. Mattila
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Web Search Engines

scholarly journals Query Recommendation Using Hybrid Query Relevance

2018 ◽  
Vol 10 (11) ◽  
pp. 112
Author(s):  
Jialu Xu ◽  
Feiyue Ye
Keyword(s):  
Information Retrieval ◽  
Information Search ◽  
Search Engines ◽  
Web Search ◽  
Superior Performance ◽  
Recommendation Algorithm ◽  
Web Information ◽  
Query Recommendation

With the explosion of web information, search engines have become main tools in information retrieval. However, most queries submitted in web search are ambiguous and multifaceted. Understanding the queries and mining query intention is critical for search engines. In this paper, we present a novel query recommendation algorithm by combining query information and URL information which can get wide and accurate query relevance. The calculation of query relevance is based on query information by query co-concurrence and query embedding vector. Adding the ranking to query-URL pairs can calculate the strength between query and URL more precisely. Empirical experiments are performed based on AOL log. The results demonstrate the effectiveness of our proposed query recommendation algorithm, which achieves superior performance compared to other algorithms.

scholarly journals HISTORY OF INDIAN TRADITIONAL MEDICINE: A MEDICAL INHERITANCE.

2018 ◽  
Vol 11 (1) ◽  
pp. 421 ◽  
Author(s):  
Partha Pradip Adhikari ◽  
Satya Bhusan Paul
Keyword(s):  
Traditional Medicine ◽  
Web Search ◽  
Care Service ◽  
Health Care Service ◽  
Modern Medicine ◽  
Traditional Medicines ◽  
Regional Effects ◽  
Practice Of Medicine ◽  
History Of ◽  
Traditional Approaches

 Objective: Indian Traditional Medicine, the foundation of age-old practice of medicine in the world, has played an essential role in human health care service and welfare from its inception. Likewise, all traditional medicines are of its own regional effects and dominant in the West Asian nations; India, Pakistan, Tibet, and so forth, East Asian nations; China, Korea, Japan, Vietnam, and so forth, Africa, South and Central America. This article is an attempt to illuminate Indian traditional medical service and its importance, based on recent methodical reviews.Methods: Web search engines for example; Google, Science Direct and Google Scholar were employed for reviews as well as for meta-analysis.Results: There is a long running debate between individuals, who utilize Indian Traditional Medicines for different ailments and disorders, and the individuals who depend on the present day; modern medicine for cure. The civil argument between modern medicine and traditional medicines comes down to a basic truth; each person, regardless of education or sickness, ought to be educated about the actualities concerning their illness and the associated side effects of medicines. Therapeutic knowledge of Indian traditional medicine has propelled various traditional approaches with similar or different theories and methodologies, which are of regional significance.Conclusion: To extend research exercises on Indian Traditional Medicine, in near future, and to explore the phytochemicals; the current review will help the investigators involved in traditional medicinal pursuit.

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