3,253 research outputs found
Leverage web analytics for real time website browsing recommendations
Trabalho apresentado no 5th World Conference on Information Systems and Technologies (WorldCISTâ17), 11-13 de abril 2017, Porto Santo, Madeira PortugalAs a websitesâ structure grow it is paramount to accommodate the
alignment of user needs and experience with the overall websitesâ purposes.
Toward this requirement, the proposed website navigation recommendation
system suggests to users, pages that might be of her interest based on past
successful navigation patterns of overall siteâs usage. Most of existing
recommendation systems adopts traditionally one of the web mining branches.
We take a different stance, on web mining usage, and alternatively considered
the real time enactment of web analytic tools supported analysis given their
current maturity and affordances. On this basis we provide a model, its
implementation and evaluation for navigation based recommendations
generation and delivery. The developed prototype adopted a SaaS orientation
to promote the underlying functionalities integration within any website.
Preliminary evaluationâs results seem to favor the validation of the present
contribution rational.N/
An efficient technique to provide webpage recommendation based on domain knowledge and web usage knowledge
Now a dayâs use of world wide web is going on increasing to get various kind of related information. By considering this fact, there is a need to provide Web page Recommendation to get a relevant result to the user search. There are different kinds of web recommendations are made like images, video, audio, query and web pages. This paper focus on providing web page recommendation to the web page in website based on domain knowledge and web usage. So it proposes models for web page recommendations. The first model is an Ontological Model for finding domain terms. The second model is semantic network analysis model to find out the relationship between domain terms and WebPages. The third model is Conceptual Prediction Model to find out web usage knowledge from web pages .On this basis, web page recommendation is provided to the web page that gives a more relevant result to user search than any other web pages present in that particular website
User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration
Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks.
Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion.
Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data
A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS
Personalization has proved to achieve better learning outcomes by adapting to specific learnersâ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learnersâ interests by continuously extrapolating topical navigation graphs from learnersâ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learnersâ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in usersâ feedback. Second, web analytics data is analyzed to get an insight into usersâ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in userâs interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts
Retrospective Higher-Order Markov Processes for User Trails
Users form information trails as they browse the web, checkin with a
geolocation, rate items, or consume media. A common problem is to predict what
a user might do next for the purposes of guidance, recommendation, or
prefetching. First-order and higher-order Markov chains have been widely used
methods to study such sequences of data. First-order Markov chains are easy to
estimate, but lack accuracy when history matters. Higher-order Markov chains,
in contrast, have too many parameters and suffer from overfitting the training
data. Fitting these parameters with regularization and smoothing only offers
mild improvements. In this paper we propose the retrospective higher-order
Markov process (RHOMP) as a low-parameter model for such sequences. This model
is a special case of a higher-order Markov chain where the transitions depend
retrospectively on a single history state instead of an arbitrary combination
of history states. There are two immediate computational advantages: the number
of parameters is linear in the order of the Markov chain and the model can be
fit to large state spaces. Furthermore, by providing a specific structure to
the higher-order chain, RHOMPs improve the model accuracy by efficiently
utilizing history states without risks of overfitting the data. We demonstrate
how to estimate a RHOMP from data and we demonstrate the effectiveness of our
method on various real application datasets spanning geolocation data, review
sequences, and business locations. The RHOMP model uniformly outperforms
higher-order Markov chains, Kneser-Ney regularization, and tensor
factorizations in terms of prediction accuracy
Design intelligence of web application for internet direct consumer-to-consumer trading
An online web application called Student-Trade has been developed. It is a state-of-the-art platform for direct consumer-to-consumer trading in the Internet. The platform is targeted for direct consumer-to-consumer trading among university students. The items for trading include books, household items, electronics, housing rental, sports equipment and tutoring services. This paper is on the design intelligence of the Student-Trade web application. One objective is to help the user to decide on the selling price of his item when the item is being posted in the web application. The system integrates a hybrid neighborhood search algorithm for determining the price of sale item when it is placed for trading in the Internet. Data mining techniques are explored for efficient processing of a vast amount of information in the database tables. In addition, the trading system would also have the intelligence of recommending items or products to a potential buyer given the previous purchase patterns. The aim is to provide a pleasant trading experience for the user. © 2015 IEEE.published_or_final_versio
Application of the Markov Chain Method in a Health Portal Recommendation System
This study produced a recommendation system that can effectively recommend items on a health portal. Toward this aim, a transaction log that records usersâ traversal activities on the Medical College of Wisconsinâs HealthLink, a health portal with a subject directory, was utilized and investigated. This study proposed a mixed-method that included the transaction log analysis method, the Markov chain analysis method, and the inferential analysis method. The transaction log analysis method was applied to extract usersâ traversal activities from the log. The Markov chain analysis method was adopted to model usersâ traversal activities and then generate recommendation lists for topics, articles, and Q&A items on the health portal. The inferential analysis method was applied to test whether there are any correlations between recommendation lists generated by the proposed recommendation system and recommendation lists ranked by experts. The topics selected for this study are Infections, the Heart, and Cancer. These three topics were the three most viewed topics in the portal. The findings of this study revealed the consistency between the recommendation lists generated from the proposed system and the lists ranked by experts. At the topic level, two topic recommendation lists generated from the proposed system were consistent with the lists ranked by experts, while one topic recommendation list was highly consistent with the list ranked by experts. At the article level, one article recommendation list generated from the proposed system was consistent with the list ranked by experts, while 14 article recommendation lists were highly consistent with the lists ranked by experts. At the Q&A item level, three Q&A item recommendation lists generated from the proposed system were consistent with the lists ranked by experts, while 12 Q&A item recommendation lists were highly consistent with the lists ranked by experts. The findings demonstrated the significance of usersâ traversal data extracted from the transaction log. The methodology applied in this study proposed a systematic approach to generating the recommendation systems for other similar portals. The outcomes of this study can facilitate usersâ navigation, and provide a new method for building a recommendation system that recommends items at three levels: the topic level, the article level, and the Q&A item level
A Business Intelligence Framework to Provide Performance Management through a Holistic Data Mining View
Traditional views of business intelligence have mainly focused on the physical and human aspects of the organization. This paper tries to show that a new information view of business activities can make a platform for developing business intelligence and support performance management. To do that, the paper proposes a new framework that can be used to provide high level of business intelligence for performance management usage. The framework introduces a hierarchy of performance influencers and a new methodology for managing them. The new methodology introduces a holistic view towards data mining concepts. The framework can be served as a blueprint for the companies which use any of ecommerce business models
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