Web-Page Recommendation Based on Web Usage and Domain Knowledge

© 1989-2012 IEEE. Web-page recommendation plays an important role in intelligent Web systems. Useful knowledge discovery from Web usage data and satisfactory knowledge representation for effective Web-page recommendations are crucial and challenging. This paper proposes a novel method to efficiently provide better Web-page recommendation through semantic-enhancement by integrating the domain and Web usage knowledge of a website. Two new models are proposed to represent the domain knowledge. The first model uses an ontology to represent the domain knowledge. The second model uses one automatically generated semantic network to represent domain terms, Web-pages, and the relations between them. Another new model, the conceptual prediction model, is proposed to automatically generate a semantic network of the semantic Web usage knowledge, which is the integration of domain knowledge and Web usage knowledge. A number of effective queries have been developed to query about these knowledge bases. Based on these queries, a set of recommendation strategies have been proposed to generate Web-page candidates. The recommendation results have been compared with the results obtained from an advanced existing Web Usage Mining (WUM) method. The experimental results demonstrate that the proposed method produces significantly higher performance than the WUM method

Web Page Recommendation Using Domain Knowledge and Improved Frequent Sequential Pattern Mining Algorithm

Web page recommendation is the technique of web site customization to fulfil the needs of every particular user or group of users. The web has become largest world of knowledge. So it is more crucial task of the webmasters to manage the contents of the particular websites to gather the requirements of the web users. The web page recommendation systems most part based on the exploitation of the patterns of the site's visitors. Domain ontology’s provide shared and regular understanding of a particular domain. Existing system uses pre-order linked WAP-tree mining (PLWAP Mine) algorithm that helps web recommendation system to recommend the interested pages but it has some drawbacks, it require more execution time and memory. To overcome the drawbacks of existing system paper utilizes PREWAP algorithm. The PREWAP algorithm recommends the interested results to web user within less time and with less memory and improves the efficiency of web page recommendation system. In work, various models are presented; the first model is Web Usage Mining which uses the web logs. The second model also utilizes web logs to represent the domain knowledge, here the domain ontology is used to solve the new page problem. Likewise the prediction model, which is a network of domain terms, which is based on the frequently viewed web-pages and represents the integrated web usage. The recommendation results have been successfully verified based on the results which are acquired from a proposed and existing web usage mining (WUM) technique

Personalized Web Page Recommendation Using Ontology

In this network era, Web Page Recommendation and web page Recommendation systems can take advantage of semantic network reasoning-capabilities to overcome common limitations of current systems and improve the recommendations’ quality. This paper presents a personalized-web-recommendation system, a system that makes use of representations of items and user-profiles based on ontology in order to provide semantic applications with personalized services. The recommender uses domain ontology to enhance the personalization: on the other hand, user’s interests are modeled in a more effective and accurate way by applying a domain-based inference method; on the other hand, the stemmer algorithm used by our content-based filtering approach, which provides a measure of the affinity between an item and a user, is enhanced by applying a semantic similarity method. Web Usage Mining plays an important role in web page recommender systems and web personalization system. In this paper, we propose an effective personalized web recommendation system based on ontology and Web Usage Mining. The proposed approach integrates semantic knowledge into Web Usage Mining and personalization processes. DOI: 10.17762/ijritcc2321-8169.15071

Web Mining for Web Personalization

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented

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

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

The onset of e-marketplace, virtual communities and social networking has appreciated the influential capability of online consumer reviews (OCR) and therefore necessitate conglomeration of the body of knowledge. This article attempts to conceptually cluster academic literature in both management and technical domain. The study follows a framework which broadly clusters management research under two heads: OCR Assessment and OCR Implication (business implication). Parallel technical literature has been reviewed to reconcile methodologies adopted in the analysis of text content on the web, majorly reviews. Text mining through automated tools, algorithmic contribution (dominant majorly in technical stream literature) and manual assessment (derived from the stream of content analysis) has been studied in this review article. Literature survey of both the domains is analyzed to propose possible area for further research. Usage of text analysis methods along with statistical and data mining techniques to analyze review text and utilize the knowledge creation for solving managerial issues can possibly constitute further work. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/4