The Digital Innovations & Contemporary Research in Science & Engineering Journal, 2016
Various searching mechanisms and algorithms are used to query and rank information on the interne... more Various searching mechanisms and algorithms are used to query and rank information on the internet. QuickRank Algorithm (QRA) is a popular method for ranking entities based on various criteria selected by the user. However, this algorithm fails to handle evolving individualism and as a result ignored user biases and preferences. The aim of this work was to develop an enhanced recursive ranking model that could improve searching experience incorporating user biases. Information retrieval plays significant role in all aspects of life, especially with the tremendous increase in inaccessible data. The problem of finding relevant information by searchers is made more difficult by the complex nature and structure of accessible data. A simple web query can return millions of results, through all of which practically nobody has the time or patience to go through. Users expect the most relevant results to be shown first, before the less relevant. Moreover, different users may have different search intent, and as such expect different results. Search efficiency becomes increasingly important as the number of internet users continues to increase. Article Progress Time Stamps
Advances in Mathematical & Computational Sciences Journal, 2018
Our intention is to develop a recursive ranking algorithm with wide area of application. Fundamen... more Our intention is to develop a recursive ranking algorithm with wide area of application. Fundamental to the achievement of this goal is the formulation of a model that will adequately characterise objects in the problem domain. Such model enabled users find what they want based on individual bias or preferences. The model was translated into a recursive ranking algorithm that traversed the entire problem domain, and generated the most appropriate set of results.We developed an algorithm that allow users to specify search criteria in order of relative importance to their search for people, publications, records, etc. on the Web, social networks, citation databases, and so on. This was done by the systematic analysis of different models, algorithms and related works as covered in-depth in previous works. We then formulate our model – the Enhanced Recursive Ranking Algorithm (ERRA) and carried out normalization of the dataset that were used in our test results. Testing and presentation of results are presented
Computing, Information Systems & Development Informatics Journal, 2017
The information age is characterized by the huge amount of data available to people. The exact am... more The information age is characterized by the huge amount of data available to people. The exact amount of data in the globe was estimated at about 4.4 zettabytes as at 2013 and this is assumed to rise to about 44 zettabytes by 2020. Arguably, a zettabyte is equivalent to 44 trillion gigabytes. The Internet is regarded as the largest source of data, an enormous source of information with a great stock of various web pages & hyperlinks. The information on the internet can be queried with the aim of enabling users to find whatever they want. In the same vein, the number and diversity of internet users continue to increase. Millions of users all over the world search the Web on a daily basis with the aim of finding the right answer or solution to problems. The Web has revolutionized access to digitally available data; as such, web information search and retrieval have become key aspects of human interactions. Information retrieval is a complex process, the study of which has attracted a lot of research efforts. This paper carried out a systematic review of information retrieval, search, andranking algorithms. The objective is to provide a theoreicalk framework and identify research gaps that can serve as a basis for the development of an enhanced recursive model for individualized search
The Digital Innovations & Contemporary Research in Science & Engineering Journal, 2016
Various searching mechanisms and algorithms are used to query and rank information on the interne... more Various searching mechanisms and algorithms are used to query and rank information on the internet. QuickRank Algorithm (QRA) is a popular method for ranking entities based on various criteria selected by the user. However, this algorithm fails to handle evolving individualism and as a result ignored user biases and preferences. The aim of this work was to develop an enhanced recursive ranking model that could improve searching experience incorporating user biases. Information retrieval plays significant role in all aspects of life, especially with the tremendous increase in inaccessible data. The problem of finding relevant information by searchers is made more difficult by the complex nature and structure of accessible data. A simple web query can return millions of results, through all of which practically nobody has the time or patience to go through. Users expect the most relevant results to be shown first, before the less relevant. Moreover, different users may have different search intent, and as such expect different results. Search efficiency becomes increasingly important as the number of internet users continues to increase. Article Progress Time Stamps
Advances in Mathematical & Computational Sciences Journal, 2018
Our intention is to develop a recursive ranking algorithm with wide area of application. Fundamen... more Our intention is to develop a recursive ranking algorithm with wide area of application. Fundamental to the achievement of this goal is the formulation of a model that will adequately characterise objects in the problem domain. Such model enabled users find what they want based on individual bias or preferences. The model was translated into a recursive ranking algorithm that traversed the entire problem domain, and generated the most appropriate set of results.We developed an algorithm that allow users to specify search criteria in order of relative importance to their search for people, publications, records, etc. on the Web, social networks, citation databases, and so on. This was done by the systematic analysis of different models, algorithms and related works as covered in-depth in previous works. We then formulate our model – the Enhanced Recursive Ranking Algorithm (ERRA) and carried out normalization of the dataset that were used in our test results. Testing and presentation of results are presented
Computing, Information Systems & Development Informatics Journal, 2017
The information age is characterized by the huge amount of data available to people. The exact am... more The information age is characterized by the huge amount of data available to people. The exact amount of data in the globe was estimated at about 4.4 zettabytes as at 2013 and this is assumed to rise to about 44 zettabytes by 2020. Arguably, a zettabyte is equivalent to 44 trillion gigabytes. The Internet is regarded as the largest source of data, an enormous source of information with a great stock of various web pages & hyperlinks. The information on the internet can be queried with the aim of enabling users to find whatever they want. In the same vein, the number and diversity of internet users continue to increase. Millions of users all over the world search the Web on a daily basis with the aim of finding the right answer or solution to problems. The Web has revolutionized access to digitally available data; as such, web information search and retrieval have become key aspects of human interactions. Information retrieval is a complex process, the study of which has attracted a lot of research efforts. This paper carried out a systematic review of information retrieval, search, andranking algorithms. The objective is to provide a theoreicalk framework and identify research gaps that can serve as a basis for the development of an enhanced recursive model for individualized search
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Papers by Johnson Yaya
achievement of this goal is the formulation of a model that will adequately characterise objects in the problem
domain. Such model enabled users find what they want based on individual bias or preferences. The model
was translated into a recursive ranking algorithm that traversed the entire problem domain, and generated the
most appropriate set of results.We developed an algorithm that allow users to specify search criteria in order
of relative importance to their search for people, publications, records, etc. on the Web, social networks,
citation databases, and so on. This was done by the systematic analysis of different models, algorithms and
related works as covered in-depth in previous works. We then formulate our model – the Enhanced
Recursive Ranking Algorithm (ERRA) and carried out normalization of the dataset that were used in our test
results. Testing and presentation of results are presented
data in the globe was estimated at about 4.4 zettabytes as at 2013 and this is assumed to rise to about 44
zettabytes by 2020. Arguably, a zettabyte is equivalent to 44 trillion gigabytes. The Internet is regarded
as the largest source of data, an enormous source of information with a great stock of various web pages &
hyperlinks. The information on the internet can be queried with the aim of enabling users to find
whatever they want. In the same vein, the number and diversity of internet users continue to increase.
Millions of users all over the world search the Web on a daily basis with the aim of finding the right
answer or solution to problems. The Web has revolutionized access to digitally available data; as such,
web information search and retrieval have become key aspects of human interactions. Information
retrieval is a complex process, the study of which has attracted a lot of research efforts. This paper carried
out a systematic review of information retrieval, search, andranking algorithms. The objective is to
provide a theoreicalk framework and identify research gaps that can serve as a basis for the development
of an enhanced recursive model for individualized search
achievement of this goal is the formulation of a model that will adequately characterise objects in the problem
domain. Such model enabled users find what they want based on individual bias or preferences. The model
was translated into a recursive ranking algorithm that traversed the entire problem domain, and generated the
most appropriate set of results.We developed an algorithm that allow users to specify search criteria in order
of relative importance to their search for people, publications, records, etc. on the Web, social networks,
citation databases, and so on. This was done by the systematic analysis of different models, algorithms and
related works as covered in-depth in previous works. We then formulate our model – the Enhanced
Recursive Ranking Algorithm (ERRA) and carried out normalization of the dataset that were used in our test
results. Testing and presentation of results are presented
data in the globe was estimated at about 4.4 zettabytes as at 2013 and this is assumed to rise to about 44
zettabytes by 2020. Arguably, a zettabyte is equivalent to 44 trillion gigabytes. The Internet is regarded
as the largest source of data, an enormous source of information with a great stock of various web pages &
hyperlinks. The information on the internet can be queried with the aim of enabling users to find
whatever they want. In the same vein, the number and diversity of internet users continue to increase.
Millions of users all over the world search the Web on a daily basis with the aim of finding the right
answer or solution to problems. The Web has revolutionized access to digitally available data; as such,
web information search and retrieval have become key aspects of human interactions. Information
retrieval is a complex process, the study of which has attracted a lot of research efforts. This paper carried
out a systematic review of information retrieval, search, andranking algorithms. The objective is to
provide a theoreicalk framework and identify research gaps that can serve as a basis for the development
of an enhanced recursive model for individualized search