Uncertainty Analysis for the Keyword System of Web Events

© 2015 IEEE. Webpage recommendations for hot Web events can assist people to easily follow the evolution of these Web events. At the same time, there are different levels of semantic uncertainty underlying the amount of Webpages for a Web event, such as recapitulative information and detailed information. Apparently, the grasp of the semantic uncertainty of Web events could improve the satisfactoriness of Webpage recommendations. However, traditional hit-rate-based or clustering-based Webpage recommendation methods have overlooked these different levels of semantic uncertainty. In this paper, we propose a framework to identify the different underlying levels of semantic uncertainty in terms of Web events, and then utilize these for Webpage recommendations. Our idea is to consider a Web event as a system composed of different keywords, and the uncertainty of this keyword system is related to the uncertainty of the particular Web event. Based on keyword association linked network Web event representation and Shannon entropy, we identify the different levels of semantic uncertainty, and construct a semantic pyramid (SP) to express the uncertainty hierarchy of a Web event. Finally, an SP-based Webpage recommendation system is developed. Experiments show that the proposed algorithm can significantly capture the different levels of the semantic uncertainties of Web events and it can be applied to Webpage recommendations

SEMANTIC WEB CONTENT MINING FOR CONTENT-BASED RECOMMENDER SYSTEMS

The fast-growing presence of data is crucial to all sectors and domain as it is being harnessed to solve various real-time problems, such as product recommendation. Web content mining, which is referred to a data mining for web textual content can be used to retrieve, refine and analyze data to solve these problems. It is therefore important that the web content mining process is optimized to improve preprocessing of web textual data for efficient recommendation. Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems. The methodology is based on two major phases. The first phase is the semantic preprocessing of data. This phase uses both a developed ontology and an existing ontology together with the typical text preprocessing steps such as filtration stemming and so on. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also the developed system is able to provide a platform for content based recommendation which provides an edge over the existing recommender approach because it is able to analyze the textual contents of users feedback on a product

Semantic-enhanced web-page recommender systems

University of Technology, Sydney. Faculty of Engineering and Information Technology.This thesis presents a new framework for a semantic-enhanced Web-page recommender (WPR) system, and a suite of enabling techniques which include semantic network models of domain knowledge and Web usage knowledge, querying techniques, and Web-page recommendation strategies. The framework enables the system to automatically discover and construct the domain and Web usage knowledge bases, and to generate effective Webpage recommendations. The main contributions of the framework are fourfold: (1) it effectively changes the fact that knowledge base construction must rely on human experts; (2) it enriches the pool of candidate Web-pages for effective Web-page recommendations by using semantic knowledge of both Web-pages and Web usage; (3) it thoroughly resolves the inconsistency problem facing contemporary WPR systems which heavily employ heterogeneous representations of knowledge bases. Knowledge bases in the system are consistently represented in a formal Web ontology language, namely OWL; and (4) it can generate effective Web-page recommendations based on a set of thoughtfully-designed recommendation strategies. A prototype of the semantic-enhanced WPR system is developed and presented, and the experimental comparisons with existing WPR approaches convincingly prove the significantly improved performance of WPR systems based on the framework and its enabling techniques

Web Site Recommendation Modelling Assisted by Ontologies Networks

Web site recommendation systems help to get high quality information. The modeling of recommendation system involves the combination of many features:metrics of quality, quality criteria, recommendation criteria, user profile, specific domain, among others. At the moment of the specification of a recommendation system it must be guaranteed a right interrelation of all of this features. In this paper we propose a ontology network based process for web site recommendation modeling. This ontology network conceptualizes the different domains (web site domain, quality assurance domain, user context domain, recommendation criteria domain, specific domain) in a set of interrelated ontologies. Basically, this work introduces the semantic relationships that were used to construct this ontology network. Moreover, it shows the usefulness of this ontology network for the detection of possible inconsistencies when specifying recommendation criteria. Particularly, this approach is illustrated for the health domain

Word sense ranking based on semantic similarity and graph entropy

In this paper we propose a system for the recommendation of tagged pictures obtained from the Web. The system, driven by user feedback, executes an abductive reasoning (based on WordNet synset semantic relations) that is able to iteratively lead to new concepts which progressively represent the cognitive creative user state. Furthermore we design a selection mechanism to pick the most relevant abductive inferences by mixing a topological graph analysis together with a semantic similitude measure.Preprin

Recommender Systems for the Semantic Web

This paper presents a semantic approach to Recommender Systems (RS), to exploit available contextual information about both the items to be recommended and the recommendation process, in an attempt to overcome some of the shortcomings of traditional RS implementations. An ontology is used as a backbone to the system in the proposed architecture to represent the problem domain, while multiple web services are orchestrated to compose a suitable recommendation model, matching the current recommendation context at run-time. In order to allow for such dynamic behaviour, the proposed system tackles the recommendation problem by applying existing RS techniques on three different levels: the selection of appropriate sets of features, recommendation model and recommendable items

SemAware: An Ontology-Based Web Recommendation System

Web Recommendation Systems (WRS\u27s) are used to recommend items and future page views to world wide web users. Web usage mining lays the platform for WRS\u27s, as results of mining user browsing patterns are used for recommendation and prediction. Existing WRS\u27s are still limited by several problems, some of which are the problem of recommending items to a new user whose browsing history is not available (Cold Start), sparse data structures (Sparsity), and no diversity in the set of recommended items (Content Overspecialization). Existing WRS\u27s also fail to make full use of the semantic information about items and the relations (e.g., is-a, has-a, part-of) among them. A domain ontology, advocated by the Semantic Web, provides a formal representation of domain knowledge with relations, concepts and axioms.This thesis proposes SemAware system, which integrates domain ontology into web usage mining and web recommendation, and increases the effectiveness and efficiency of the system by solving problems of cold start, sparsity, content overspecialization and complexity-accuracy tradeoffs. SemAware technique includes enriching the web log with semantic information through a proposed semantic distance measure based on Jaccard coefficient. A matrix of semantic distances is then used in Semantics-aware Sequential Pattern Mining (SPM) of the web log, and is also integrated with the transition probability matrix of Markov models built from the web log. In the recommendation phase, the proposed SPM and Markov models are used to add interpretability. The proposed recommendation engine uses vector-space model to build anitem-concept correlation matrix in combination with user-provided tags to generate top-n recommendation.Experimental studies show that SemAware outperforms popular recommendation algorithms, and that its proposed components are effective and efficient for solving the contradicting predictions problem, the scalability and sparsity of SPM and top-n recommendations, and content overspecialization problems

Personalized And Situation-Aware Recommendations For Runners

The project uService investigates the transformation of a mobile user into a service super prosumer, i.e., a producer, provider and consumer of services at the same time. The goal is to develop a platform which enables a user to create, discover and consume mobile services anywhere and at any time on the mobile device. uRun is an application scenario of the project in the field of mobile health and fitness. The uRun framework provides a mobile assistance system particularly for runners, which combines Web 2.0 and Web 3.0 technologies and personalized and situation-aware recommendation mechanisms. The ability to create individual and mobile health and fitness services as well as a personalized and situation-aware assistance system based on a semantic knowledge base are considered to provide an edge over existing consumer-centric health care systems. In this article, we describe the recommendation mechanism and the incorporation of semantic knowledge for the uService platform and the uRun framework