IPACT: Improved Web Page Recommendation System Using Profile Aggregation Based On Clustering of Transactions

Almurtadha, Yahya; Sulaiman, Md. Nasir; Mustapha, Norwati; Udzir, Nur Izura

doi:10.3844/ajassp.2011.277.283

Cited by 21 publications

(12 citation statements)

References 18 publications

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“…5) show the average precision and recalling for the suggestions presented to several users using the proposed method. These figures show the result of the comparison between proposed method despite using date in user profile and the proposed method (without inclusion of date), the IPACT system proposed in (AlMurtadha et al, 2011) and user natural behavior. …”

Section: Assessment Of the Proposed Methodsmentioning

confidence: 99%

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Constructing a web recommender system using web usage mining and user’s profiles

Mombeini¹,

Harounabadi²,

Sheshdeh³

2014

10.5267/j.msl

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The World Wide Web is a great source of information, which is nowadays being widely used due to the availability of useful information changing, dynamically. However, the large number of webpages often confuses many users and it is hard for them to find information on their interests. Therefore, it is necessary to provide a system capable of guiding users towards their desired choices and services. Recommender systems search among a large collection of user interests and recommend those, which are likely to be favored the most by the user. Web usage mining was designed to function on web server records, which are included in user search results. Therefore, recommender servers use the web usage mining technique to predict users' browsing patterns and recommend those patterns in the form of a suggestion list. In this article, a recommender system based on web usage mining phases (online and offline) was proposed. In the offline phase, the first step is to analyze user access records to identify user sessions. Next, user profiles are built using data from server records based on the frequency of access to pages, the time spent by the user on each page and the date of page view. Date is of importance since it is more possible for users to request new pages more than old ones and old pages are less probable to be viewed, as users mostly look for new information. Following the creation of user profiles, users are categorized in clusters using the Fuzzy C-means clustering algorithm and S(c) criterion based on their similarities. In the online phase, a neural network is offered to identify the suggested model while online suggestions are generated using the suggestion module for the active user. Search engines analyze suggestion lists based on rate of user interest in pages and page rank and finally suggest appropriate pages to the active user. Experiments show that the proposed method of predicting user recent requested pages has more accuracy and cover than other methods.Growing Science Ltd. All rights reserved. 4

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Section: Assessment Of the Proposed Methodsmentioning

confidence: 99%

“…Afterwards, suggestions were provided to users through user review records and the extracted knowledge. Almurtadha et al (2011) introduced a recommender system to explore user priorities and suggested pages for future reviews. This system includes two phases.…”

Section: Related Workmentioning

confidence: 99%

Constructing a web recommender system using web usage mining and user’s profiles

Mombeini¹,

Harounabadi²,

Sheshdeh³

2014

10.5267/j.msl

View full text Add to dashboard Cite

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“…This is so due to the following characteristics of the Web: (1) the amount of data/information on the Web is huge and still growing rapidly. (AlMurtadha et al, 2011;Al Shalabi, 2009)Web data is also easily accessible, (2) the coverage of Web information is wide and diverse. One can find information about almost anything on the Web, (3) Information on the Web is heterogeneous, (4) Much of the Web information is semi-structured due to the nested structure of HTML code and the need of Web page designers to present information in a simple and regular fashion to facilitate human viewing and browsing, (5) Much of the Web information is linked.…”

Section: Introductionmentioning

confidence: 99%

Sketching-Din Elimination of Web Page

Sivakumar¹

2011

Journal of Computer Science

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Problem statement:The web content mining used to access lot of web pages, mining of web contents aims to extort positive information or awareness. Approach: There are several type of Web contents which can suggest valuable information to users are accessible in the Web, for instance graphical data, Extensible Markup Language documents, Hyper Text Markup Language documents and simple text. Here, only element of the information is useful for a testing purpose and the remaining information are noises. Results: In this research study, we propose an approach for removing the noises from a given web page which will get better the presentation of web content mining. At first, the web page information is divided into various blocks. Conclusion: From which, the duplicate blocks are removed using sketching. The performance of the proposed approach and results ensure the effectiveness of the proposed approach in classify the main blocks.

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“…User interest may change over time so that profiles are maintained and updated. An improved recommendation system using Profile Aggregation based on Clustering of Transactions (iPACT) shows better prediction accuracy than the previous methods PACT and Hypergraph (Almurtadha et al, 2011). To maintain the interest scores of the user profile, spreading activation algorithm is proposed to analyse ongoing browsing activities (Sieg et al, 2007a).…”

Section: Introductionmentioning

confidence: 99%

Ontological Approach for Effective Generation of Concept Based User Profiles to Personalize Search Results

Tim¹

2012

Journal of Computer Science

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Problem statement: Ontological user profile generation was a semantic approach to derive richer concept based user profiles. It depends on the semantic relationship of concepts. This study focuses on ontology to derive concept oriented user profile based on user search queries and clicked documents.This study proposes concept based on topic ontology which derives the concept based user profiles more independently. It was possible to improve the search engine processes more efficiently. Approach: This process consists of individual user's interests, topical categories of user interests and identifies the relationship among the concepts. The proposed approach was based on topic ontology for concept based user profile generation from search engine logs. Spreading activation algorithm was used to optimize the relevance of search engine results. Topic ontology was constructed to identify the user interest by assigning activation values and explore the topics similarity of user preferences. Results: To update and maintain the interest scores, spreading activation algorithm was proposed. User interest may change over the period of time which was reflected to user profiles. According to profile changes, search engine was personalized by assigning interest scores and weight to the topics. Conclusion: Experiments illustrate the efficacy of proposed approach and with the help of topic ontology user preferences can be identified correctly. It improves the quality of the search engine personalization by identifying the user's precise needs.

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IPACT: Improved Web Page Recommendation System Using Profile Aggregation Based On Clustering of Transactions

Cited by 21 publications

References 18 publications

Constructing a web recommender system using web usage mining and user’s profiles

Constructing a web recommender system using web usage mining and user’s profiles

Sketching-Din Elimination of Web Page

Ontological Approach for Effective Generation of Concept Based User Profiles to Personalize Search Results

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