This document discusses an enhanced web usage mining system using fuzzy clustering and collaborative filtering recommendation algorithms. It aims to address challenges with existing recommender systems like producing low quality recommendations for large datasets. The system architecture uses fuzzy clustering to predict future user access based on browsing behavior. Collaborative filtering is then used to produce expected results by combining fuzzy clustering outputs with a web database. This approach aims to provide users with more relevant recommendations in a shorter time compared to other systems.
Analysis on Recommended System for Web Information Retrieval Using HMMIJERA Editor
Web is a rich domain of data and knowledge, which is spread over the world in unstructured manner. The
number of users is continuously access the information over the internet. Web mining is an application of data
mining where web related data is extracted and manipulated for extracting knowledge. The data mining is used
in the domain of web information mining is refers as web mining, that is further divided into three major
domains web uses mining, web content mining and web structure mining. The proposed work is intended to
work with web uses mining. The concept of web mining is to improve the user feedbacks and user navigation
pattern discovery for a CRM system. Finally a new algorithm HMM is used for finding the pattern in data,
which method promises to provide much accurate recommendation.
Contextual model of recommending resources on an academic networking portalcsandit
Artificial Intelligence techniques have been instrumental in helping users to handle the large
amount of information on the Internet. The idea of recommendation systems, custom search
engines, and intelligent software has been widely accepted among users who seek assistance in
searching, sorting, classifying, filtering and sharing this vast quantity of information. In this
paper, we present a contextual model of recommendation engine which keeping in mind the
context and activities of a user, recommends resources in an academic networking portal. The
proposed method uses the implicit method of feedback and the concepts relationship hierarchy
to determine the similarity between a user and the resources in the portal. The proposed
algorithm has been tested on an academic networking portal and the results are convincing.
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTALcscpconf
Artificial Intelligence techniques have been instrumental in helping users to handle the large amount of information on the Internet. The idea of recommendation systems, custom search engines, and intelligent software has been widely accepted among users who seek assistance insearching, sorting, classifying, filtering and sharing this vast quantity of information. In thispaper, we present a contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal. Theproposed method uses the implicit method of feedback and the concepts relationship hierarchy to determine the similarity between a user and the resources in the portal. The proposed algorithm has been tested on an academic networking portal and the results are convincing
Analysis on Recommended System for Web Information Retrieval Using HMMIJERA Editor
Web is a rich domain of data and knowledge, which is spread over the world in unstructured manner. The
number of users is continuously access the information over the internet. Web mining is an application of data
mining where web related data is extracted and manipulated for extracting knowledge. The data mining is used
in the domain of web information mining is refers as web mining, that is further divided into three major
domains web uses mining, web content mining and web structure mining. The proposed work is intended to
work with web uses mining. The concept of web mining is to improve the user feedbacks and user navigation
pattern discovery for a CRM system. Finally a new algorithm HMM is used for finding the pattern in data,
which method promises to provide much accurate recommendation.
Contextual model of recommending resources on an academic networking portalcsandit
Artificial Intelligence techniques have been instrumental in helping users to handle the large
amount of information on the Internet. The idea of recommendation systems, custom search
engines, and intelligent software has been widely accepted among users who seek assistance in
searching, sorting, classifying, filtering and sharing this vast quantity of information. In this
paper, we present a contextual model of recommendation engine which keeping in mind the
context and activities of a user, recommends resources in an academic networking portal. The
proposed method uses the implicit method of feedback and the concepts relationship hierarchy
to determine the similarity between a user and the resources in the portal. The proposed
algorithm has been tested on an academic networking portal and the results are convincing.
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTALcscpconf
Artificial Intelligence techniques have been instrumental in helping users to handle the large amount of information on the Internet. The idea of recommendation systems, custom search engines, and intelligent software has been widely accepted among users who seek assistance insearching, sorting, classifying, filtering and sharing this vast quantity of information. In thispaper, we present a contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal. Theproposed method uses the implicit method of feedback and the concepts relationship hierarchy to determine the similarity between a user and the resources in the portal. The proposed algorithm has been tested on an academic networking portal and the results are convincing
Annotation Approach for Document with Recommendation ijmpict
An enormous number of organizations generate and share textual descriptions of their products, facilities, and activities. Such collections of textual data comprise a significant amount of controlled information, which residues buried in the unstructured text. Whereas information extraction systems simplify the extraction of structured associations, they are frequently expensive and incorrect, particularly when working on top of text that does not comprise any examples of the targeted structured data. Projected an alternative methodology that simplifies the structured metadata generation by recognizing documents that are possible to contain information of awareness and this data will be beneficial for querying the database. Moreover, we intend algorithms to extract attribute-value pairs, and similarly devise new mechanisms to map such pairs to manually created schemes. We apply clustering technique to the item content information to complement the user rating information, which improves the correctness of collaborative similarity, and solves the cold start problem.
International conference On Computer Science And technologyanchalsinghdm
ICGCET 2019 | 5th International Conference on Green Computing and Engineering Technologies. The conference will be held on 7th September - 9th September 2019 in Morocco. International Conference On Engineering Technology
The conference aims to promote the work of researchers, scientists, engineers and students from across the world on advancement in electronic and computer systems.
This work describes a new system User Profile Relevant Results -
UProRevs which would filter the results given by a search engine based on the user’s profile.
“UProRevs - User Profile Relevant Results” has been published by the IEEE - Computer Society as the proceedings for the 10th International Conference on Information Technology.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
The development of the web in past few years has created a lot of challenge in this field. The new work in this field is the search of the data in a search tree pattern based on tree. Various sequential mining algorithms have been devoloped till date. Web usage mining is used to operate the web server logs, that contains the navigation history of the user. Recommendater system is explained properly with the explanation of whole procedure of the recommendater system. The search results of the data leads to the proper ad efficient search. But the problem was the time utilization and the search results generated from them. So, a new local search algorithm is proposed for country-wise search that makes the searching more efficient on local results basis. This approach has lead to an advancement in the search based methods and the results generated.
Extracting and Reducing the Semantic Information Content of Web Documents to ...ijsrd.com
Ranking and optimization of web service compositions represent challenging areas of research with significant implication for realization of the "Web of Services" vision. The semantic web, where the semantics information is indicated using machine-process able language such as the Web Ontology Language (OWL) "Semantic web service" use formal semantic description of web service functionality and enable automated reasoning over web service compositions. These semantic web services can then be automatically discovered, composed into more complex services, and executed. Automating web service composition through the use of semantic technologies calculating the semantic similarities between outputs and inputs of connected constituent services, and aggregate these values into a measure of semantics quality for the composition. It propose a novel and extensible model balancing the new dimensions of semantic quality ( as a functional quality metric) with QoS metric, and using them together as a ranking and optimization criteria. It also demonstrates the utility of Genetic Algorithms to allow optimization within the context of a large number of services foreseen by the "Web of Service" vision. To reduce the semantics of the web documents then to support semantic document retrieval by using Network Ontology Language (NOL) and to improve QoS as a ranking and optimization.
Multi Similarity Measure based Result Merging Strategies in Meta Search EngineIDES Editor
In Meta Search Engine result merging is the key
component. Meta Search Engines provide a uniform query
interface for Internet users to search for information.
Depending on users’ needs, they select relevant sources and
map user queries into the target search engines, subsequently
merging the results. The effectiveness of a Meta Search
Engine is closely related to the result merging algorithm it
employs. In this paper, we have proposed a Meta Search
Engine, which has two distinct steps (1) searching through
surface and deep search engine, and (2) Ranking the results
through the designed ranking algorithm. Initially, the query
given by the user is inputted to the deep and surface search
engine. The proposed method used two distinct algorithms
for ranking the search results, concept similarity based
method and cosine similarity based method. Once the results
from various search engines are ranked, the proposed Meta
Search Engine merges them into a single ranked list. Finally,
the experimentation will be done to prove the efficiency of
the proposed visible and invisible web-based Meta Search
Engine in merging the relevant pages. TSAP is used as the
evaluation criteria and the algorithms are evaluated based on
these criteria.
Comparable Analysis of Web Mining Categoriestheijes
Web Data Mining is the current field of analysis which is a combination of two research area known as Data Mining and World Wide Web. Web Data Mining research associates with various research diversities like Database, Artificial Intelligence and Information redeem. The mining techniques are categorized into various categories namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this work, analysis of mining techniques are done. From the analysis it has been concluded that Web Content Mining has unstructured or semi- structure view of data whereas Web Structure Mining have linked structure and Web Usage Mining mainly includes interaction.
Optimization of Search Results with Duplicate Page Elimination using Usage DataIDES Editor
The performance and scalability of search engines
are greatly affected by the presence of enormous amount of
duplicate data on the World Wide Web. The flooded search
results containing a large number of identical or near identical
web pages affect the search efficiency and seek time of the users
to find the desired information within the search results. When
navigating through the results, the only information left behind
by the users is the trace through the pages they accessed. This
data is recorded in the query log files and usually referred to
as Web Usage Data. In this paper, a novel technique for
optimizing search efficiency by removing duplicate data from
search results is being proposed, which utilizes the usage data
stored in the query logs. The duplicate data detection is
performed by the proposed Duplicate Data Detection (D3)
algorithm, which works offline on the basis of favored user
queries found by pre-mining the logs with query clustering.
The proposed result optimization technique is supposed to
enhance the search engine efficiency and effectiveness to a large
scale.
Structured data and metadata evaluation methodology for organizations looking...Emily Kolvitz
The current state of findability on the web for many organizations is incipient. Search Engine Optimization (SEO) techniques change frequently and remain much a mystery to many companies. The one variable in the equation of web findability that remains a staple is good quality metadata under the hood of the website.
This research methodology will allow for :
An assessment of findability maturity on the web from an image-centric viewpoint
Help improve findability on the web by establishing a baseline for where your organization is at in terms of structured data content and visualize gaps or areas for improvement from a search engine neutral perspective
Structural Balance Theory Based Recommendation for Social Service PortalYogeshIJTSRD
There is enormous data present in our world. Therefore in order to access the most accurate information is becoming more difficult and complicated. As a result many relevant information gets missed which leads to much duplication of work and effort. Due to the huge search results, the user will generally have difficulty in identifying the relevant ones. To solve this problem, a recommendation system is used. A recommendation system is nothing but a filtering information system, which is used to predict the relevance of retrieved information according to the user’s needs for some criteria. Hence, it can provide the user with the results that best fit their needs. The services provided through the web normally provide huge records about any requested item or service. A proper recommendation system is used to separate this information result. A recommendation system can be improved further if supported with a level of trust information. That is, recommendations are prioritized according to their level of trust. Recommending appropriate needs social service to the target volunteers will become the key to ensure continuous success of social service. Today, many social service systems does not adopt any recommendation techniques. They provide advertisement or highlights request for a small commission. G. Banupriya | M. Anand "Structural Balance Theory-Based Recommendation for Social Service Portal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41216.pdf Paper URL: https://www.ijtsrd.comengineering/software-engineering/41216/structural-balance-theorybased-recommendation-for-social-service-portal/g-banupriya
An Improvised Fuzzy Preference Tree Of CRS For E-Services Using Incremental A...IJTET Journal
Abstract—Web mining is the amalgamation of information accumulated by traditional data mining methodologies and techniques with information collected over the World Wide Web. A Recommendation system is a profound application that comforts the user in a decision-making process, where they lack of personal experience to choose an item from the confound set of alternative products or services. The key challenge in the development of recommender system is to overcome the problems like single level recommendation and static recommendation, which are exists in the real world e-services. The goal is to achieve and enhance predicting algorithm to discover the frequent items, which are feasible to be purchasable. At this point, we examine the prior buying patterns of the customers and use the knowledge thus procured, to achieve an item set, which co-ordinates with the purchasing mentality of a particular set of customers. Potential recommendation is concerned as a link structure among the items within E-commerce website, which supports the new customers to find related products in a hurry. In Existing system, a fuzzy set consists of user preference and item features alone, so the recommendations to the customers are irrelevant and anonymous. In this paper, we suggest a recommendation technique, which practices the wild spreading and data sharing competency of a huge customer linkage and also this method follows a fuzzy tree- structured model, in which fuzzy set techniques are utilized to express user preferences and purchased items are in a clustered form to develop a user convenient recommendations. Here, an incremental association rule mining is employed to find interesting relation between variables in a large database.
Charoen Pokphand Foods Plc.’s Quest to become the Kitchen of the World: An Ov...inventionjournals
In this case study, the readers will be navigated through the successful journey of Charoen Pokphand Foods Plc. (CPF) as the company brilliantly raced to reach its pinnacle in business map as kitchen of the world. For the starters, the readers will be enlightened with the history and business model of CPF. The highlight of this paper is mainly how CPF reconnected and aligned its aim to their customer’sbuying dynamics so that they manage to market the right product to the right customer in the right wayin order to become the Kitchen of the World.
Transportation is defined as the movement of passengers and freight from one place to another. Passenger is an important part of the overall development problem of the nation and it affects mostly all the aspects of mobility. The Transportation problem is one of the sub classes of LPP in which the objective is to transport various amount of a single homogeneous commodity, that are initially stored at various origins, to different destinations in such a way that the total transportation cost is minimum. Although the name of the problem is derived from transport to which it was first applied, the problem can also be used for machine allocation, plant location, product mix problem, and many others, so that the problem is not confined to transportation or distribution only. Data Envelopment Analysis (DEA) is a very powerful service management and benchmarking technique originally developed by Charnes et al (1) to evaluate nonprofit and public sector organizations. Linear programming problem (LPP) is the underlying methodology that makes DEA particularly powerful compared with alternative productivity management tools. A Transportation Problem can be solved very easily by different methods (NWC RULE, LCM & VAM) by recognizing and formulating into LPP. The IBFS obtained in Transportation Algorithm can be tested also by MODIFIED DISTRIBUTION METHOD. After studying this paper, we will be able to achieve the following objectives of Transportation System - a major problem of the metropolitan cities. 1- Recognize and formulate the transportation problem as a linear programming problem. 2- Build a transportation table and describe its components. 3- Find an initial basic feasible solution of the transportation problem by using various methods. 4- Know in detail all the steps involved in solving a transportation problem by MODI problem. 5- Solve the unbalanced transportation problems by MODI method. 6- Identify the special situation in transportation problems; such as degeneracy and alternative optimal solution. 7- Resolve the special cases in transportation problems, where the objective may be of maximization or some transportation route may be prohibited.
Annotation Approach for Document with Recommendation ijmpict
An enormous number of organizations generate and share textual descriptions of their products, facilities, and activities. Such collections of textual data comprise a significant amount of controlled information, which residues buried in the unstructured text. Whereas information extraction systems simplify the extraction of structured associations, they are frequently expensive and incorrect, particularly when working on top of text that does not comprise any examples of the targeted structured data. Projected an alternative methodology that simplifies the structured metadata generation by recognizing documents that are possible to contain information of awareness and this data will be beneficial for querying the database. Moreover, we intend algorithms to extract attribute-value pairs, and similarly devise new mechanisms to map such pairs to manually created schemes. We apply clustering technique to the item content information to complement the user rating information, which improves the correctness of collaborative similarity, and solves the cold start problem.
International conference On Computer Science And technologyanchalsinghdm
ICGCET 2019 | 5th International Conference on Green Computing and Engineering Technologies. The conference will be held on 7th September - 9th September 2019 in Morocco. International Conference On Engineering Technology
The conference aims to promote the work of researchers, scientists, engineers and students from across the world on advancement in electronic and computer systems.
This work describes a new system User Profile Relevant Results -
UProRevs which would filter the results given by a search engine based on the user’s profile.
“UProRevs - User Profile Relevant Results” has been published by the IEEE - Computer Society as the proceedings for the 10th International Conference on Information Technology.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
The development of the web in past few years has created a lot of challenge in this field. The new work in this field is the search of the data in a search tree pattern based on tree. Various sequential mining algorithms have been devoloped till date. Web usage mining is used to operate the web server logs, that contains the navigation history of the user. Recommendater system is explained properly with the explanation of whole procedure of the recommendater system. The search results of the data leads to the proper ad efficient search. But the problem was the time utilization and the search results generated from them. So, a new local search algorithm is proposed for country-wise search that makes the searching more efficient on local results basis. This approach has lead to an advancement in the search based methods and the results generated.
Extracting and Reducing the Semantic Information Content of Web Documents to ...ijsrd.com
Ranking and optimization of web service compositions represent challenging areas of research with significant implication for realization of the "Web of Services" vision. The semantic web, where the semantics information is indicated using machine-process able language such as the Web Ontology Language (OWL) "Semantic web service" use formal semantic description of web service functionality and enable automated reasoning over web service compositions. These semantic web services can then be automatically discovered, composed into more complex services, and executed. Automating web service composition through the use of semantic technologies calculating the semantic similarities between outputs and inputs of connected constituent services, and aggregate these values into a measure of semantics quality for the composition. It propose a novel and extensible model balancing the new dimensions of semantic quality ( as a functional quality metric) with QoS metric, and using them together as a ranking and optimization criteria. It also demonstrates the utility of Genetic Algorithms to allow optimization within the context of a large number of services foreseen by the "Web of Service" vision. To reduce the semantics of the web documents then to support semantic document retrieval by using Network Ontology Language (NOL) and to improve QoS as a ranking and optimization.
Multi Similarity Measure based Result Merging Strategies in Meta Search EngineIDES Editor
In Meta Search Engine result merging is the key
component. Meta Search Engines provide a uniform query
interface for Internet users to search for information.
Depending on users’ needs, they select relevant sources and
map user queries into the target search engines, subsequently
merging the results. The effectiveness of a Meta Search
Engine is closely related to the result merging algorithm it
employs. In this paper, we have proposed a Meta Search
Engine, which has two distinct steps (1) searching through
surface and deep search engine, and (2) Ranking the results
through the designed ranking algorithm. Initially, the query
given by the user is inputted to the deep and surface search
engine. The proposed method used two distinct algorithms
for ranking the search results, concept similarity based
method and cosine similarity based method. Once the results
from various search engines are ranked, the proposed Meta
Search Engine merges them into a single ranked list. Finally,
the experimentation will be done to prove the efficiency of
the proposed visible and invisible web-based Meta Search
Engine in merging the relevant pages. TSAP is used as the
evaluation criteria and the algorithms are evaluated based on
these criteria.
Comparable Analysis of Web Mining Categoriestheijes
Web Data Mining is the current field of analysis which is a combination of two research area known as Data Mining and World Wide Web. Web Data Mining research associates with various research diversities like Database, Artificial Intelligence and Information redeem. The mining techniques are categorized into various categories namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this work, analysis of mining techniques are done. From the analysis it has been concluded that Web Content Mining has unstructured or semi- structure view of data whereas Web Structure Mining have linked structure and Web Usage Mining mainly includes interaction.
Optimization of Search Results with Duplicate Page Elimination using Usage DataIDES Editor
The performance and scalability of search engines
are greatly affected by the presence of enormous amount of
duplicate data on the World Wide Web. The flooded search
results containing a large number of identical or near identical
web pages affect the search efficiency and seek time of the users
to find the desired information within the search results. When
navigating through the results, the only information left behind
by the users is the trace through the pages they accessed. This
data is recorded in the query log files and usually referred to
as Web Usage Data. In this paper, a novel technique for
optimizing search efficiency by removing duplicate data from
search results is being proposed, which utilizes the usage data
stored in the query logs. The duplicate data detection is
performed by the proposed Duplicate Data Detection (D3)
algorithm, which works offline on the basis of favored user
queries found by pre-mining the logs with query clustering.
The proposed result optimization technique is supposed to
enhance the search engine efficiency and effectiveness to a large
scale.
Structured data and metadata evaluation methodology for organizations looking...Emily Kolvitz
The current state of findability on the web for many organizations is incipient. Search Engine Optimization (SEO) techniques change frequently and remain much a mystery to many companies. The one variable in the equation of web findability that remains a staple is good quality metadata under the hood of the website.
This research methodology will allow for :
An assessment of findability maturity on the web from an image-centric viewpoint
Help improve findability on the web by establishing a baseline for where your organization is at in terms of structured data content and visualize gaps or areas for improvement from a search engine neutral perspective
Structural Balance Theory Based Recommendation for Social Service PortalYogeshIJTSRD
There is enormous data present in our world. Therefore in order to access the most accurate information is becoming more difficult and complicated. As a result many relevant information gets missed which leads to much duplication of work and effort. Due to the huge search results, the user will generally have difficulty in identifying the relevant ones. To solve this problem, a recommendation system is used. A recommendation system is nothing but a filtering information system, which is used to predict the relevance of retrieved information according to the user’s needs for some criteria. Hence, it can provide the user with the results that best fit their needs. The services provided through the web normally provide huge records about any requested item or service. A proper recommendation system is used to separate this information result. A recommendation system can be improved further if supported with a level of trust information. That is, recommendations are prioritized according to their level of trust. Recommending appropriate needs social service to the target volunteers will become the key to ensure continuous success of social service. Today, many social service systems does not adopt any recommendation techniques. They provide advertisement or highlights request for a small commission. G. Banupriya | M. Anand "Structural Balance Theory-Based Recommendation for Social Service Portal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41216.pdf Paper URL: https://www.ijtsrd.comengineering/software-engineering/41216/structural-balance-theorybased-recommendation-for-social-service-portal/g-banupriya
An Improvised Fuzzy Preference Tree Of CRS For E-Services Using Incremental A...IJTET Journal
Abstract—Web mining is the amalgamation of information accumulated by traditional data mining methodologies and techniques with information collected over the World Wide Web. A Recommendation system is a profound application that comforts the user in a decision-making process, where they lack of personal experience to choose an item from the confound set of alternative products or services. The key challenge in the development of recommender system is to overcome the problems like single level recommendation and static recommendation, which are exists in the real world e-services. The goal is to achieve and enhance predicting algorithm to discover the frequent items, which are feasible to be purchasable. At this point, we examine the prior buying patterns of the customers and use the knowledge thus procured, to achieve an item set, which co-ordinates with the purchasing mentality of a particular set of customers. Potential recommendation is concerned as a link structure among the items within E-commerce website, which supports the new customers to find related products in a hurry. In Existing system, a fuzzy set consists of user preference and item features alone, so the recommendations to the customers are irrelevant and anonymous. In this paper, we suggest a recommendation technique, which practices the wild spreading and data sharing competency of a huge customer linkage and also this method follows a fuzzy tree- structured model, in which fuzzy set techniques are utilized to express user preferences and purchased items are in a clustered form to develop a user convenient recommendations. Here, an incremental association rule mining is employed to find interesting relation between variables in a large database.
Charoen Pokphand Foods Plc.’s Quest to become the Kitchen of the World: An Ov...inventionjournals
In this case study, the readers will be navigated through the successful journey of Charoen Pokphand Foods Plc. (CPF) as the company brilliantly raced to reach its pinnacle in business map as kitchen of the world. For the starters, the readers will be enlightened with the history and business model of CPF. The highlight of this paper is mainly how CPF reconnected and aligned its aim to their customer’sbuying dynamics so that they manage to market the right product to the right customer in the right wayin order to become the Kitchen of the World.
Transportation is defined as the movement of passengers and freight from one place to another. Passenger is an important part of the overall development problem of the nation and it affects mostly all the aspects of mobility. The Transportation problem is one of the sub classes of LPP in which the objective is to transport various amount of a single homogeneous commodity, that are initially stored at various origins, to different destinations in such a way that the total transportation cost is minimum. Although the name of the problem is derived from transport to which it was first applied, the problem can also be used for machine allocation, plant location, product mix problem, and many others, so that the problem is not confined to transportation or distribution only. Data Envelopment Analysis (DEA) is a very powerful service management and benchmarking technique originally developed by Charnes et al (1) to evaluate nonprofit and public sector organizations. Linear programming problem (LPP) is the underlying methodology that makes DEA particularly powerful compared with alternative productivity management tools. A Transportation Problem can be solved very easily by different methods (NWC RULE, LCM & VAM) by recognizing and formulating into LPP. The IBFS obtained in Transportation Algorithm can be tested also by MODIFIED DISTRIBUTION METHOD. After studying this paper, we will be able to achieve the following objectives of Transportation System - a major problem of the metropolitan cities. 1- Recognize and formulate the transportation problem as a linear programming problem. 2- Build a transportation table and describe its components. 3- Find an initial basic feasible solution of the transportation problem by using various methods. 4- Know in detail all the steps involved in solving a transportation problem by MODI problem. 5- Solve the unbalanced transportation problems by MODI method. 6- Identify the special situation in transportation problems; such as degeneracy and alternative optimal solution. 7- Resolve the special cases in transportation problems, where the objective may be of maximization or some transportation route may be prohibited.
Police-Public Relations as a Potent Tool for Combating Crime, Insecurity, and...inventionjournals
For quite long now, some commentators have oftentimes been lured into confusing the concept and practice of police-public relations with that of police-community relations, community policing, policemedia relations or local vigilantism. Against this backdrop, therefore, this paper examined the state of police– public relations in Nigeria with special focus on how it could be deployed as a potent tool for fighting crime and preventing social disorder in the country. The paper adopted a research methodology of review of extant related literature on the fields of security, public, and policing backed by judgmental content analysis technique. The paper made a number of interesting findings. Prominent among this findings included the fact that Police authorities in Nigeria since 1930, the force was established had made efforts to forge a strong relationship with the public it serves including establishment of such structures and mechanisms like Police Public Complaints Bureau, Police Community Relations Committees and various organs of community-policing projects scattered all over the country. In spite of these efforts, police–public relations in the country had remained sordidly sour, conflictual, and unredeeming. Again, the paper also identified a number of structural, institutional, and attitudinal factors that are responsible for the deteriorating gap in the relationship between the police in Nigeria and members of the public they are hired to serve. These factors include systemic corruption, the very nature of policing in a once colonized state like Nigeria, inappropriate use of lethal force, extrajudicial killings, and intimidation. Others are extortion, illegal arrest and detention, poor funding and lack of adequate training or education, among other ills. Finally, the study made a number of recommendations, as a way forward which included greater emphasis on awareness campaigns to sanitize the police force to be appreciated by members of the public, that NPPRD should be made autonomous and public relations professionals recruited into its fold; rigorous background check of recruits in order to weed out persons of questionable character at the point of entry; and design of curriculum that would inculcate modern policing ideals and democratic values in police rank and file, among others
CONTENT AND USER CLICK BASED PAGE RANKING FOR IMPROVED WEB INFORMATION RETRIEVALijcsa
Search engines today are retrieving more than a few thousand web pages for a single query, most of which
are irrelevant. Listing results according to user needs is, therefore, a very real necessity. The challenge lies
in ordering retrieved pages and presenting them to users in line with their interests. Search engines,
therefore, utilize page rank algorithms to analyze and re-rank search results according to the relevance of
the user’s query by estimating (over the web) the importance of a web page. The proposed work
investigates web page ranking methods and recently-developed improvements in web page ranking.
Further, a new content-based web page rank technique is also proposed for implementation. The proposed
technique finds out how important a particular web page is by evaluating the data a user has clicked on, as
well as the contents available on these web pages. The results demonstrate the effectiveness of the proposed
page ranking technique and its efficiency.
Quest Trail: An Effective Approach for Construction of Personalized Search En...Editor IJCATR
Personalized search refers to search experiences that are tailored specifically to an individual's interests by incorporating information about the individual beyond specific query provided. Especially people working in a software development organization (analysts, developers, testers, maintenance team members), find it increasingly difficult to get relevant results to their searches. We propose methods to personalize searches by resolving the ambiguity of query terms, and increase the relevance of search results in order to match the user’s interests. Difficulty in web searches has given rise to the need for development of personalized search engines. Personalized search engines create user profiles to capture the users’ personal preferences and as such identify the actual goal of the input query. Since users are usually reluctant to explicitly provide their preferences due to the extra manual effort involved, the search engine faces the entire burden of predicting the user’s preferences and intentions behind a query in order to yield more relevant search results. In this paper we define a QUEST to be the objective of user’s search; here we combine quest level analysis of user’s search logs and semantic analysis of the user’s query in order to personalize user’s search results. Most personalization methods focus on the creation of one single profile for a user and apply the same profile to all of the user’s queries. Hence we propose a personalized search for a software development organization by creating QUEST or domain based profile rather than individual user based profile.
`A Survey on approaches of Web Mining in Varied Areasinventionjournals
There has been lot of research in recent years for efficient web searching. Several papers have proposed algorithm for user feedback sessions, to evaluate the performance of inferring user search goals. When the information is retrieved, user clicks on a particular URL. Based on the click rate, ranking will be done automatically, clustering the feedback sessions. Web search engines have made enormous contributions to the web and society. They make finding information on the web quick and easy. However, they are far from optimal. A major deficiency of generic search engines is that they follow the ‘‘one size fits all’’ model and are not adaptable to individual users.
The Internet, which brought the most innovative
improvement on information society, web recommendation
systems based on web usage mining try to mine user’s behavior
patters from web access logs, and recommend pages or
suggestions to the user by matching the user’s browsing behavior
with the mined historical behavior patterns. In this paper we
propose a recommendation framework that considers different
application status and various contexts of each user. We
successfully implemented the proposed framework and show how
this system can improve the overall quality of web
recommendations.
I
Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
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recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
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IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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Recommendation generation by integrating sequential pattern mining and semanticseSAT Journals
Abstract As the Internet usage keeps increasing, the number of web sites and hence the number of web pages also keeps increasing. A recommendation system can be used to provide personalized web service by suggesting the pages that are likely to be accessed in future. Most of the recommendation systems are based on association rule mining or based on keywords. Using the association rule mining the prediction rate is less as it doesn’t take into account the order of access of the web pages by the users. The recommendation systems that are key-word based provides lesser relevant results. This paper proposes a recommendation system that uses the advantages of sequential pattern mining and semantics over the association rule mining and keyword based systems respectively. Keywords: Sequential Pattern Mining, Taxonomy, Apriori-All, CS-Mine, Semantic, Clustering
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IRJET-A Survey on Web Personalization of Web Usage MiningIRJET Journal
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Abstract
Now a day, World Wide Web (www) is a rich and most powerful source of information. Day by day it is becoming more complex and expanding in size to get maximum information details online. However, it is becoming more complex and critical task to retrieve exact information expected by its users. To deal with this problem one more powerful concept is personalization which is becoming more powerful now days. Personalization is a subclass of information filtering system that seek to predict the 'ratings' or 'preferences' that a user would give to an items, they had not yet considered, using a model built from the characteristics of an item (content-based approaches or collaborative filtering approaches). Web mining is an emerging field of data mining used to provide personalization on the web. It consist three major categories i.e. Web Content Mining, Web Usage Mining, and Web Structure Mining. This paper focuses on web usage mining and algorithms used for providing personalization on the web.
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Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering Recommendation Algorithms
1. International Journal of Mathematics and Statistics Invention (IJMSI)
E-ISSN: 2321 – 4767 P-ISSN: 2321 - 4759
www.ijmsi.org Volume 4 Issue 10 || December. 2016 || PP-09-13
www.ijmsi.org 9 | Page
Enhanced Web Usage Mining Using Fuzzy Clustering and
Collaborative Filtering Recommendation Algorithms
A.Sangeetha1
, C.Nalini2
1
Ph.D Scholor, Department of Computer Science & Engg. ,Bharat University
2
Professor, Department of Computer Science & Engg. ,Bharat University
ABSTRACT: Information is overloaded in the Internet due to the unstable growth of information and it makes
information search as complicate process. Recommendation System (RS) is the tool and largely used nowadays
in many areas to generate interest items to users. With the development of e-commerce and information access,
recommender systems have become a popular technique to prune large information spaces so that users are
directed toward those items that best meet their needs and preferences. As the exponential explosion of various
contents generated on the Web, Recommendation techniques have become increasingly indispensable. Web
recommendation systems assist the users to get the exact information and facilitate the information search
easier. Web recommendation is one of the techniques of web personalization, which recommends web pages or
items to the user based on the previous browsing history. But the tremendous growth in the amount of the
available information and the number of visitors to web sites in recent years places some key challenges for
recommender system. The recent recommender systems stuck with producing high quality recommendation with
large information, resulting unwanted item instead of targeted item or product, and performing many
recommendations per second for millions of user and items. To avoid these challenges a new recommender
system technologies are needed that can quickly produce high quality recommendation, even for a very large
scale problems. To address these issues we use two recommender system process using fuzzy clustering and
collaborative filtering algorithms. Fuzzy clustering is used to predict the items or product that will be accessed
in the future based on the previous action of user browsers behavior. Collaborative filtering recommendation
process is used to produce the user expects result from the result of fuzzy clustering and collection of Web
Database data items. Using this new recommendation system, it results the user expected product or item with
minimum time. This system reduces the result of unrelated and unwanted item to user and provides the results
with user interested domain.
KEYWORDS: fuzzy clustering, collaborative filtering, recommender
I. INTRODUCTION
A web search engine is a software system that is planned to seek for information on the World Wide
Web (WWW). The search consequences are commonly presented in a stroke of results regularly called to
as search engine results pages (SERPs). The information may be a blend of web page, pictures, and other types
of files. Some engines dig for data available in databases or open directory. Nothing like web directories, which
are maintain only by human editors, search engines also maintain real-time information by executing
an algorithm on a network crawler.
A search engine maintains the following processes in near real time:
1. Network crawling
2. Indexing
3. Searching
Web search engines obtain their information by network crawling from one site to other site. The
"spider" also called crawler checks for the customary filename robots.txt, addressed to it, before sending that
information back to be indexed depends on many factors, such as the titles, JavaScript ,page content, Cascading
Style Sheets (CSS), headings, as proof by the standard HTML markup of the informational content, or its
metadata in HTML Meta tags.
Indexing means associating words and other definable tokens initiate on web pages to their domain
names and HTML-based fields. The associations are done in a public database, made available for web search
queries. A query from a user can be a solitary word or set of words. The index helps find information linking to
the query as rapidly as possible. Some of the methods for indexing and caching are secrets for the trade, while
network crawling is a basic process of visiting all sites on an orderly basis.
Naturally when a user enters an inquiry into a search engine it is a few words. The index previously has
the names of the websites containing the words, and these are directly obtained from the index. The actual
processing load is in generating the web pages that are the search consequences list: Every page in the entire list
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must be prejudiced according to information in the indexes. Then the peak search result item requires the
lookup, reconstruction, and markup of the snippets showing the situation of the words matched. These are
simply part of the processing each search results requires, and further pages (next to the top) require extra of
this post processing.
Fig.1 Search Engine Process
In a search engine, the user choices will be determined based on the previous histories, which is helpful
to build a recommendation system. Recommendation system is a subdivision of information filtering system that
search to forecast the ranking or fondness that a user would give to an item. These systems have become
tremendously frequent in latest years, and are applied in a diversity of applications. The most admired ones are
movies, social tags, news, books, research articles, search queries, music, and products in general. Other than
this, there are also recommender systems for experts, collaborators, jokes, hotels, financial services, life
insurance, people (online dating), and Twitter followers. This system follows two approaches:
Collaborative Filtering
Content based Filtering
Collaborative filtering approaches structuring a model from a consumer’s precedent behavior (stuff
previously purchased or chosen and/or numerical ratings given to those items) as well as alike decisions made
by other users. This model is used to forecast items that the consumer may have a curiosity in. Content-based
filtering approaches make use of a sequence of discrete features of an item in order to suggest added items with
alike properties. These two approaches are joined to form a Hybrid Recommender Systems.
A. Organization
The enduring of the paper is explained as follows: In Part II, related work is clearly explained .In Part
III, system architecture is clearly explained with the techniques used in the work. The techniques explained are
fuzzy clustering and collaborative filtering. In Part IV, conclusion is provided.
II. RELATED WORK
In recent years, there have been numerous works based on the recommendation. Few of the works are
to be discussed as follows: In Improving efficiency of personalized web search [1], the user search is analyzed
using content and keyword extraction technique. The main aim of that work is to progress the search engine
quality. Depend on the user query, the search results are obtained. The content and keywords of results are
analyzed. The query is preprocessed and root words are found out. Based on the words, the dictionary is
constructed. The query is compared with the dictionary and the words are weighted and ranked. This work was
implemented in client-side. In survey on web search engines [2], the search engine basic working is explained.
This work explains the working and ranking concept of familiar search engines. Each search engine has its own
searching methodology. Some of the search engines which are explained in that work are as follows: Archie,
Gopher, Google, Bing, Yahoo and Ask. Archie uses File Transfer protocol concept to list all the search files.
Gopher user gopher protocol. It is an internet protocol to carry out search in the internet. Google is the
popular search engine and it uses the page ranking algorithm to rank the web pages. Whereas Bing uses the
number of back links to rank the results. Ask is an answering engine and it is not widely used now. Yahoo’s
ranking algorithm is based more on heading of the websites.
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In Winsome [3], a search engine is designed based on the concept of Entity ranking and time cache
results. This search engine provides the relevant results using the process of entity ranking. Entity ranking is
applied to filter the irrelevant entities for the result and groups the relevant entities to provide the relevant
results. But this engine does not concentrate on the user activities so the user query is not analyzed for providing
recommendation scheme.
III. ARCHITECTURE
Fig.2 System Architecture
The system architecture modules are given as follows:
Data Preprocessing
Recommendation process with user data
Recommendation process with user data and Resulted data
Automation Process
Data preprocessing is a key part in the process of data mining. The saying "garbage in, garbage out" is
mainly pertinent to data mining and learning projects. Data-gathering methods are regularly loosely controlled,
follow-on in out-of-range values (e.g., Weight: −120), impossible data combinations (e.g., Education: Degree,
Illiterate: Yes), missing values, etc. Analyzing data that has not been cautiously screened for such problems can
create deceptive results. Thus, the illustration and quality of data is primary and leading ahead of running an
analysis.
Data preprocessing is sub divided into following:
Data Cleaning
Data Integration
Data Transformation
Data Reduction
The system architecture of this recommendation system is shown in the above figure 2.This tool uses two main
methodologies to provide an efficient recommendation system. The methodologies are:
Fuzzy Clustering
Collaborative Filtering
Fuzzy clustering (soft clustering) is the process of clustering in which every data point can belong to
more than one cluster or partition. It was developed by J.C. Dunn in 1973, and enhanced by J.C. Bezdek in
1981. Clustering is the process of assigning data points to clusters or same classes. These are identified through
similarity contains intensity, distance and connectivity. Distinct similarity may be selected depending on the
application or data. The fuzzy algorithm is called Fuzzy C-means (FCM) algorithm. It is similar to k-means
algorithm. This algorithm is used in Bioinformatics, Marketing and Image Analysis.
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The FCM algorithm attempts to divide a finite collection of elements into a
compilation of c fuzzy clusters according to some given condition.
Given a limited set of data, the FCM returns a list of cluster centres and a partition
matrix
, where each element, , tells the extent to
which element, , belongs to cluster .
The FCM aims to minimize an objective function:
where:
Collaborative filtering (CF) is a method used by several recommender systems. This filtering has two
senses, a narrow one and a more general one. In general, the process of filtering for information or patterns
using methods involving collaboration among multiple agents, viewpoints, data sources, etc. It involves very
large data sets applications. It has been applied to many diverse kinds of data such as: sensing and monitoring
data, such as in mineral exploration, environmental sensing over huge areas or multiple sensors; financial data,
such as financial service institutions that integrate many financial sources; or in e-commerce and web
applications where the focus is on user data, etc. In the narrower one, collaborative filtering is a method of
building automatic predictions (filtering) about the interests of a user by collecting preferences
or tang information from several users (collaborating).
The collaborative filtering system process is given as follows:
1. A consumer expresses his or her opinions by ranking items (e.g. images, movies or CDs) of the system.
These rankings can be seen as a rough illustration of the consumer's interest in the consequent domain.
2. The system matches this consumer’s ratings against other consumers’ and finds the people with most
"related" tastes.
3. With related consumers, the system recommends stuff that the alike consumers have ranked highly but not
yet being ranked by this consumer (most probably the nonexistence of ranking is often referred as the
unfamiliarity of an item).
The architecture is explained as follows: From the group of users, the user’s interest is stored in web
log. The web log is used to extract the features based on that the recommendation process can be provided. The
user’s query request is stored in the web database. Then the recommendation process is carried out .it includes
fuzzy clustering, trained model, recommendation engine, filter user’s interest. Fuzzy clustering is applied to get
a trained model. The trained model data is used for the recommendation scheme. Upon that, list of
recommendation can be obtained. The list is filtered based on the user’s interest to get the suitable
recommendations. These recommendations are applied collaborative filtering, domain classification,
similarity based recommendation. Collaborative filtering is normally applied to get the best recommendation
approach. From this approach top most domain and recommended query is suggested to user .This process is
fully automated. From the recommended the user may select a particular query based on their choice so that the
time to search new query get reduced.
IV. CONCLUSION
This recommendation tool is designed with two key concepts fuzzy clustering and collaborative
filtering .This work is to provide an efficient recommendation scheme for the user. This tool provides excellent
recommendations for the user due to the usage of two techniques.