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User profile Ontology for the Personalization approach

User profile Ontology for the Personalization approach

Whatever the domain of technology, personalization of information can be exploited according to two modes of management: by recommendation or by interrogation. Recommender systems [9] exploit user profiles or user communities for disseminate offers targeted on the interests centers and preferences of the latter. This procedure is also called "push mode". User feedback is very important to refine their profiles and increase the efficiency of the system. Personalization by interrogation [7] is to adapt the evaluation of a query in relation to the characteristics and preferences of the user who sent it. In this context, the system reacts to a specific request of the user in enriching his request so as to make it more accurate [7], in choosing data sources based on the quality requirements of the user [10] or personalizing the display of results [11]. This procedure is also called "pull mode". The work described in this paper fits into this context. The implementation of personalization systems is getting mainly in two phases:
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Innovative Personalized Architecture In Case Of Web Search Users

Innovative Personalized Architecture In Case Of Web Search Users

Web search engines provide users with a Large number of results for a submitted query. However, not all return results are relevant to the uses needs. In this paper, we proposed a new web search personalization approach that captures the user's interest and references in the form of concepts by mining search results and they click through. In this paper an effective mixture personalized re-ranking search approach is proposed by modeling user's search wellbeing in a conceptual user profile and then exploiting this profile in the re-ranking process. In this each concept in the user profile consist of two types of documents: categorization document and viewed document Taxonomy is used to represent the user general interest as it contains information from web pages originally associated with open dictionary project category. Viewed documents are used to represent the user's specific interest as it contains information from the web pages clicked by the users. Finally the system create a semantic profile of the user's by monitor and analyze the user's search history. The search results generated will utilize and incorporation of various techniques including clustering, re-ranking and semantic user profile to enhance the performance of the web search engine.
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Using A Concept-based User Context For Search Personalization

Using A Concept-based User Context For Search Personalization

learning process consists of mapping each visited Web page into five taxonomy concepts with the highest similarities; the user profile consists then of a list of concepts for which the weights are accumulated based on user’s browsing behaviors. This user profile is used to re-rank the search results by com- bining the original rank of the document and the conceptual rank computed using a similarity between the document and the user profile. An interest-based personalized search in [17] consists of mapping a set of known user interests onto a group of categories in the concepts taxonomy and therefore categorize and personalize search results according to the mapped categories associated to these user interests. Comparatively to these previous works, our approach consists of representing the user interests, each one as a set of semantically related concepts of reference ontology, while all possible user interests are represented in [9] over all the concepts in the ontology. Another distinctive aspect for our approach is that instead of mapping the web pages browsed by the user as in [9], we map a keyword user context derived automatically from the user feedback onto the ontology. While user interests are mapped in [17] on the ontology as keyword vectors, we note that their representation cannot be derived automatically in such a way that they are far from real world applications.
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Ontology for mobile device utilization: towards knowledge personalization in mobile learning

Ontology for mobile device utilization: towards knowledge personalization in mobile learning

In addition, different researchers have different focus on delivering the personalized content in m-learning. Zhang (2003) proposed a generic framework for delivering personalized content to mobile users based on user profile. Sá and Carriço (2009) however presented a framework which takes advantage of mobile devices‟ features to supports end-users in content personalisation. While, Tan et al. (2011) presented a framework for location-based m-learning system which means the focus is location. These different focuses in m-learning domain can confuse the application developer. There are different elements that can be considered in m-learning to offer knowledge personalization such as user profile, mobile devices‟ features, location, and others. In order to have a comprehensive understanding or conceptualization on the elements that can be included in m-learning domain, a general framework for knowledge personalization in m-learning is needed.
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Ontology Generation from Session Data for Web Personalization

Ontology Generation from Session Data for Web Personalization

of occurrence of each item which was collected from the web logs , for each item of the user the model will find out the time difference between the entry and exit in each item, the operations which was done in that item and finally the model stores all these information in their corresponding c-file. Based upon the count, time and activity it assign weights for that page and store it in the w -file. It ranking the weights in decreasing order and stor ed it in the w-file itself, for each and every user their will be a separate c-file and a common w-file where the w-file contains the user-id, item and the corresponding weight. Based upon the ranking it will measure the user’s interested web pages for that user and the same procedure will be used to generate the User Interested Page Ontology (UIPO) [2] for all users. The important concept of our model is that the U IPO will be updates dynamically based upon the access of the web pages by the users. It will not be suited for new users because for them there will be no previous access pattern so there is no entry in w-file and there is no c-file for that user, for those cases it will generate a UIPO based upon the user interest which will be collected from the user’s profile. Suppose if the user’s profile information is not relevant to generate the U IPO than for those users it will generate the overall website ontology .
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Hermes: an Ontology Based News Personalization Portal

Hermes: an Ontology Based News Personalization Portal

In contrast to work that has been done earlier, this approach focuses both on news classification and visualizing ontologies. By combining these two research fields, it is possible to create an understandable news portal, in which the user can choose his own interests and in which the results are displayed in a way that the user can understand. The project also clearly demonstrates the usefulness of the Semantic Web and its techniques: it enables computers to ‘understand’ the news items on the Internet. This provides the user with the ability to specify searches with respect to the context of the news items. Furthermore, it contributes to the amount of content that is available online in Semantic Web format.
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Time based Web User Personalization and Search

Time based Web User Personalization and Search

The information on the World Wide Web is growing without bound. Users may have very diversified preferences in the pages they target through a search engine. It is therefore a challenging task to adapt a search engine to suit the needs of a particular user. In mobile search, the interaction between users and mobile devices are constrained by the small form factors of the mobile devices. To reduce the amount of user‟s interactions with the search interface, an important requirement for mobile search engine is to be able to understand the users‟ needs and preferences on that instant and deliver highly relevant information to the users. To effectively aid this task, we propose an efficient approach for web user personalization and search. In our approach, user‟s interests and preferences according to time are extracted by mining time of access, search results and their clickthroughs. User profile will be created and updated using RSVM training. Experimental result shows that, personalization according to time preference improve the effectiveness rate of personalization and search.
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Personalized Business to Business E-services using Tree-based Recommender System Rishi Kumar N, Nanda Kumaru, Pandikumar K

Personalized Business to Business E-services using Tree-based Recommender System Rishi Kumar N, Nanda Kumaru, Pandikumar K

Described as a single value or a vector in current research, and tree-structured items or user profiles have not been considered to date. The fuzzy preferences models mentioned previously, which are represented as vectors, are not suitable to dealing with the tree- structured data in a Web-based B2B environment. To solve these challenges namely, tree-structured items (products/services), tree-structured user preferences, vague values of user preferences, and personalization of recommendations in B2B e-service recommendation problems, this study proposes a method for modeling fuzzy tree-structured user preferences, presents a tree matching method, and, based on the previous methods, develops an innovative fuzzy preference tree-based recommendation approach. The developed new approach has been implemented and applied in a business partner recommender system. This paper has three main contributions. From the theoretical aspect, a tree matching method, which comprehensively considers tree structures, node attributes, and weights, is
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A Survey on Semantic Web based E learning

A Survey on Semantic Web based E learning

A personalize information retrieval approach in an e-learning environment has proposed by Zhuhadar et al. [8]. In the proposed architecture, domain ontology is encoded into tree structure with the help of OWL (web ontology language). This tree structure is called semantic domain. After that semantic learner profile is extracted from the semantic domain according to the visited nodes (concepts) by the user with the help of bottom up pruning. Time interval during which, user visit node is a controlling parameter for memory span of the user profile. After that documents are clusters into groups by the one of various hierarchical clustering algorithms. Similarity between user profile and cluster find and most similar cluster is used as a recommended cluster for the user. Now when user searches a query, the default search results come. These default search results are re rank based on whether the search output documents are in user profile or recommended cluster. User profile documents have highest priority than recommended cluster documents and rest of the documents. Experimental results show that the personalized semantic search improve the precision of search.
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An Enriched Privacy Protection in Personalized Web Search

An Enriched Privacy Protection in Personalized Web Search

Personalized search could be promising thanks to improve search quality. This approach needs users to grant the server full access to non-public data on web that violates users’ privacy. During this paper, investigated the practicableness of achieving a balance between users’ privacy and search quality. First, associate algorithmic rule is provided to the user for collection, summarizing, and organizing their personal data into a hierarchal user profile, wherever general terms area unit stratified to higher levels than specific terms. Through this profile, user’s management what portion of their non-public data is exposed to the server by adjusting the min Detail threshold. an extra privacy live, exp Ratio, is projected to estimate the quantity of privacy is exposed with the desired min Detail price. This work targets at bridging the conflict desires of personalization and privacy protection, and provides an answer wherever users decide their own privacy settings supported a structured user profile. This edges the user within the following ways that. Offers a ascendable thanks to mechanically build a hierarchal user profile on the shopper facet. It’s not realistic to want that each user to specify their personal interests expressly and clearly. Thus, associate algorithmic rule is enforced to mechanically collect personal data that indicates associate implicit goal or intent. The user profile is made hierarchically so the higher-level interests area unit a lot of general, and therefore the lower-level interest’s area unit a lot of specific. During this approach, an expensive pool of profile sources is explored as well as browsing histories, emails and private documents.
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User profile Personalization Based on Link-Click Concept

User profile Personalization Based on Link-Click Concept

Click through data is the search result clicked by the user and the ranking function is employed to present the search results in some proper order according to the user’s preferences. The first step in this method is preference mining, which discovers user’s preferences of search results from click through data. The second step is the ranking function optimization, which optimizes the ranking according to the users preferences [1].Existing click through can be categorized into document based and concept based approaches. In both the methods, the user clicks can be used to infer the users Interest. Document based profiling methods try to estimate users. Document preferences.[5],[6],[7],[8]. Most document based methods focus on analyzing users. Clicking and browsing behaviors recorded in the users. Click through data [9].The proposed system first, extracts the concepts from the web snippets. The maximum number of keywords can be limited to six or seven. Secondly, it uses the Naive Bayesian classifier. Since the search engines can’t understand the real intention, query expansion is very important and the system records the title, keyword, and description as the text contents of the page and it maintains an index file for each user. Since the click through data is spread overall the returned pages, it is essential to extract the user related pages from the all returned pages .This task can be accomplished with the help of the Naïve Bayes algorithm.
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Incorporating elements from image recommender systems within a personalized virtual tour framework

Incorporating elements from image recommender systems within a personalized virtual tour framework

A major vehicle that makes personalization possible is the recommender system that offers solution to overcome today’s world of information overflow. It provides specific suggestions based on user requirements, profiles or similar cases that have been previously handled by the system. This paper intends to illustrate the key elements summarized from previous image recommender systems, to be embedded within the suggested personalized virtual tour framework conducted in this research. This discussion will start by reviewing recommendation strategies that implement different ways of providing recommendations. As the focus of this paper is on image recommender systems, five key elements for better recommendations have been compiled based on a literature survey. Subsequently, these key elements were incorporated within a preliminary hybrid personalized virtual tour framework known as the ‘See What You Want, Feel What You See’ (SeeWYW, FeelWYS) model. Basically, the objective of this research on image recommendation is to implement a hybrid recommendation approach, which includes the socio-demographic and context-aware recommender engines. The socio- demographic recommender will deliver a suitable virtual route based on user demographic profiles. Following the suggested virtual route, a context-aware recommender will present a sequence of panorama that can be adapted based on user’s emotion as contextual information. It is hoped that these initial findings will provide insight on how to produce improved personalized and adaptive recommender system with good usability and good user feedback as well.
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AN EXTENDING RECOMMENDATION SYSTEM FOR WEB INFORMATION RETRIEVAL

AN EXTENDING RECOMMENDATION SYSTEM FOR WEB INFORMATION RETRIEVAL

Web is a diverse source of information where different kinds of knowledge and data are available. Some of the information is direct recoverable form web pages and some information is found in hidden formats such as web access log and web link organizations . According to the nature of data availability the web mining techniques are classified as web usage mining, content mining and structure mining. Data mining algorithms are implemented to find the knowledgeable pattern in such type of data. In this proposed work the web accessed log is analysed for knowledge discovery. Web access log analysis is also termed as web usage mining. Basically the web servers contains more than one websites and for keep track the traffic information a log file is managed. This web access log files contains the entire information for each user request and their response. Such log file is known as web access log where all the users’ access data or web usage information is available. In this proposed work a new recommendation system is investigated and designed. The recommendation system are the data analysis technique by which users previous or historical navigational patterns are analysed and based on their navigational behaviour future trends are predicted. For that purpose recommendation system consist of predictive algorithms and clustering techniques. The predicted future trends of user navigational patterns are help to understand which kind of data a user is looking and searching. Thus these systems are much helpful for making heuristics in e- commerce web sites, social networking web sites and others.
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User Profile Modelling in Online Communities

User Profile Modelling in Online Communities

Multiple efforts have emerged from the Semantic Web (SW) community to target this problem. Vocabularies in standard representation formats, such as RDF and OWL, have been developed, to model users and their social context. Examples of these vocabularies include FOAF – Friend of a Friend [6] and extensions like the Relationship Vocabulary [17], SIOC [2;9], OPO – Online Presence Ontology [15], or MOAT – Meaning of a Tag [7]. While these ontologies do indeed capture user inter- actions within online communities, they do not model more dynamic user aspects such as behavioural evolution within the community. The aforementioned vocabularies represent the raw data, but actionable knowledge comes from filtering the vocabularies, selecting useful features, and mining the profile data to uncover the most salient preferences, behaviours and needs of the users.
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Multidimensional User Data Model for Web Personalization

Multidimensional User Data Model for Web Personalization

user. For data retrieval, the user submits a search query to the search engine and manually picks the relevant links from list provided by the search engine. Usually search results are not tailored to the need of the particular user, but ordered based on many other factors that may not be relevant to the particular user. As a result, the user will have to browse through many pages to locate the relevant contents, even if it is present in the search results. Much research is going on to reduce the burden of the user by refining the search results according to the user needs. These systems are however not very efficient as they make a user data model based on the information obtained from how the users use their system. Currently personalization is used in many systems to a great extent. But the data model is separate and divided for each system. So Facebook profile of the user will be concentrating on the friendship details, Linked-in profile will be ba datased on the professional interests and so on. Mobasher et. al. has presented a personalization model integrating user transactions and page views [5].Our aim is to build a complete integrated and united profile portraying the diverse interests of the user which can be used in all variety of applications.
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Personalization as a means of achieving person-environment congruence in Malaysian housing

Personalization as a means of achieving person-environment congruence in Malaysian housing

Also, studies on personalization should encompass the low and medium cost houses. Much of the previous researches on renovation and extension practice in this country put more emphasis on low-cost housing problems leaving the medium and high cost housing almost untouched. Works by Nurizan (1999) on Squatters’ Resettlement program in Kuala Lumpur, and by Azizah Salim (1998) on housing extension in low-cost housing schemes near Kuala Lumpur are of the low-cost types. Renovation in the Malaysian context as Tipple (2000) indicates is a remedy for the inadequately and poorly designed low-cost houses. On the other hand, medium-cost houses are regarded as posing lesser problems due to owners’ financial standing and being the primary target group of developers to gain profit in housing projects (Nurizan Yahaya, 1989). Therefore studies on personalization in house design in general are lacking.
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An Interactive Approach of Ontology-based Requirement Elicitation  for Software Customization

An Interactive Approach of Ontology-based Requirement Elicitation for Software Customization

First, the ontology model is instantiated with requirements of the book locating module. Basically, book locating module provides two functionalities: get reference to a book and get detailed information about a book. Details of a book may include the publication information, the contents, sample chapters and so on. Here publication information and contents are used as examples. In addition, in order to get the reference of a book, the most common method is search. Search will return a list of relevant books, so users need to point out the very book from the list. In addition, to facilitate users in finding the book among a list of books, sorting could be applied. Thus getting a list of books may contain two sub-processes: search a book and sort the search results. Furthermore, there are two ways of book searching. One is to match user inputs with the predefined keywords of the books. The other is advanced composite search. Users may provide detailed information such as authors and publication to narrow down the search domain. There are two levels of keywords matching: broad match and exact match. Exact match tries to return the results that are most relevant to the inputs, while broad match allows returning something appearing similar to the inputs but not exactly related to the inputs. On the other hand, broad match may return something unexpected but interesting. Thus they are two different levels of search quality constraints, and mutually exclusive.
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An investigation into the determinants of user acceptance of personalization in online banking

An investigation into the determinants of user acceptance of personalization in online banking

In this research we approach trust from a holistic point of view rather than a ‘narrow’ web-centred point of view. This is firstly because we want to capture the underlying effect of trust in its entirety as it influences personalization. While we recognize that there are a myriad of factors influencing trust in this medium, we also recognize that banking has certain peculiar characteristics which may not hold for other online industries. Banking is based primarily on trust and integrity, which has to be real and not just perceived, because the customer and regulatory authorities demand it. There are other issues such as website design, level of feedback, consistency, level of down-time etc. These things influence to some degree the level of confidence and integrity we have toward the service and ultimately towards the provider. However our focus here is directed to Trust in relation to the organization and not as it relates to the communication medium.
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Cross System User Modeling and Personalization on the Social Web

Cross System User Modeling and Personalization on the Social Web

© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1388 Advantage: The Friend Relationship-Based User Identification (FRUI) algorithm is affirmed. FRUI calculates a matching of all candidate User Matched Pairs (UMPs), and only UMPs with high ranks will measure as authenticable users. We conjointly developed 2 propositions to improve the potency of the algorithmic rule. Results of extensive experiments demonstrate that FRUI performs far better than current network structure-based algorithms.

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A Strategic Approach to Metrics for User Experience Designers

A Strategic Approach to Metrics for User Experience Designers

User experience (UX) designers asked to justify return-on- investment (ROI) for UX activities often rely on published ROI studies and UX metrics that do not address decision makers’ concerns. With a little knowledge of business strategy and metrics and an understanding of their own value to an organization, UX practitioners can (a) identify the financial and non-financial metrics and goals that drive change in their organizations, (b) draw a clear picture for decision makers of the connection between their value and the company’s goals, and (c) demonstrate a positive return on investment in UX activities.
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