Personalized web page recommendation using case-based clustering and weighted association rule mining

Bhavithra, J.; Saradha, A.

doi:10.1007/s10586-018-2053-y

Cited by 27 publications

(14 citation statements)

References 20 publications

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“…As shown in information recommendation module is the most crucial functional module of the system. Since our system is designed specifically with information recommendation in mind, this article will focus on the information recommendation module, which mainly includes three sub-modules: data preprocessing [10], association rule mining [11] and information recommendation. The data preprocessing sub-module extracts relevant information about books and borrowers from the book-borrowing database and collects data for cleaning, conversion, and integration processing; The association rule mining sub-module uses an improved Apriori data mining algorithm and takes the processed data as item set to discover strong association rules with satisfied support degree (greater than minimum support threshold) and satisfied confidence coefficient (greater than minimum confidence coefficient threshold) based on item sets coming from the processed data.…”

Section: Functional Modulesmentioning

confidence: 99%

Design and Implementation of Book Recommendation Management System Based on Improved Apriori Algorithm

Zhou¹

2020

IIM

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The traditional Apriori applied in books management system causes slow system operation due to frequent scanning of database and excessive quantity of candidate item-sets, so an information recommendation book management system based on improved Apriori data mining algorithm is designed, in which the C/S (client/server) architecture and B/S (browser/server) architecture are integrated, so as to open the book information to library staff and borrowers. The related information data of the borrowers and books can be extracted from books lending database by the data preprocessing sub-module in the system function module. After the data is cleaned, converted and integrated, the association rule mining sub-module is used to mine the strong association rules with support degree greater than minimum support degree threshold and confidence coefficient greater than minimum confidence coefficient threshold according to the processed data and by means of the improved Apriori data mining algorithm to generate association rule database. The association matching is performed by the personalized recommendation sub-module according to the borrower and his selected books in the association rule database. The book information associated with the books read by borrower is recommended to him to realize personalized recommendation of the book information. The experimental results show that the system can effectively recommend book related information, and its CPU occupation rate is only 6.47% under the condition that 50 clients are running it at the same time. Anyway, it has good performance.

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Section: Functional Modulesmentioning

confidence: 99%

Design and Implementation of Book Recommendation Management System Based on Improved Apriori Algorithm

Zhou¹

2020

IIM

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“…Several studies have considered interaction attributes as implicit feedback of users" preferences. Bhavithra and Saradha [22] proposed a case-based reasoning strategy to recommend web pages based on the searching history of a particular user. They considered several interaction factors to be added in a user profile such as time on page, time on site, exit rate, and others.…”

Section: B Personalizationmentioning

confidence: 99%

“…They considered several interaction factors to be added in a user profile such as time on page, time on site, exit rate, and others. Their main aim was to benefit from these attributes to recognize patterns and apply collaborative filtering [22]. Moreover, Stai et al [21] developed a mechanism to effectively personalize the enriched multimedia content based on users" interests and needs.…”

Section: B Personalizationmentioning

confidence: 99%

SentiFilter: A Personalized Filtering Model for Arabic Semi-Spam Content based on Sentimental and Behavioral Analysis

Alsulami¹,

Al-Aama²

2020

IJACSA

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Unwanted content in online social network services is a substantial issue that is continuously growing and negatively affecting the user-browsing experience. Current practices do not provide personalized solutions that meet each individual's needs and preferences. Therefore, there is a potential demand to provide each user with a personalized level of protection against what he/she perceives as unwanted content. Thus, this paper proposes a personalized filtering model, which we named SentiFilter. It is a hybrid model that combines both sentimental and behavioral factors to detect unwanted content for each user towards pre-defined topics. An experiment involving 80,098 Twitter messages from 32 users was conducted to evaluate the effectiveness of the SentiFilter model. The effectiveness was measured in terms of the consistency between the implicit feedback derived from the SentiFilter model towards five selected topics and the explicit feedback collected explicitly from participants towards the same topics. Results reveal that commenting behavior is more effective than liking behavior to detect unwanted content because of its high consistency with users' explicit feedback. Findings also indicate that sentiment of users' comments does not reflect users' perception of unwanted content. The results of implicit feedback derived from the SentiFilter model accurately agree with users' explicit feedback by the indication of the low statistical significance difference between the two sets. The proposed model is expected to provide an effective automated solution for filtering semi-spam content in favor of personalized preferences.

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“…Khusumanegara et al [12] applied hierarchical agglomerative clustering. Bhavithra and Saradha [4] proposed a new clustering method based on the k-NN approach as preprocessing for a recommendation. Recently, some research approached this problem using neural networks to predict each user's movement, such as [29].…”

Section: Web Usage Miningmentioning

confidence: 99%

New Performance Index “Attractiveness Factor” for Evaluating Websites via Obtaining Transition of Users’ Interests

Yoshida

Higurashi²,

Maruishi

et al. 2019

Data Sci. Eng.

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The studies of browsing behavior have gained increasing attention in web analysis for providing better service. Most of the conventional approaches focus on simple indices such as average dwell time and conversion rate. These indices make similar evaluations to websites even if their features are significantly different. Moreover, such statistical indices are not sensitive to the dynamics of users' interests. In this paper, we propose a new framework for measuring a website's attractiveness that takes into account both the distribution and dynamics of users' interests. Within the framework, we define a new index for the website, called Attractiveness Factor, which evaluates the degree of users' attention. It consists of three procedures: First, we capture the transition of users' interests during browsing by solving a nonnegative matrix factorization and constrained network flow problems. To accommodate multiple types of interests of a user, we applied a soft clustering as opposed to a hard clustering to model attributes of users and websites. Second, for each website, the feature of each cluster is obtained by fitting the dwell time distribution with Weibull distribution. Finally, we calculate Attractiveness Factor of a website by applying the results of clustering and fitting. Attractiveness Factor depends on the distribution of the dwell time of users interested in the website, which reflects the change of interest of users. Numerical experiments with real web access data of Yahoo Japan News are conducted by solving extremely large-scale optimization problems. They show that Attractiveness Factor captures more exceptional information about browsing behavior more effectively than well-used indices. Attractive factors give low ratings to category pages; however, it can assign high ratings to websites that attract many people, such as hot topic news about the 2018 FIFA World Cup, Japan's new imperial era' REIWA,' and North Korea-the United States Hanoi Summit. Moreover, we demonstrate that Attractiveness Factor can detect the tendency of users' attention to each website at a given time interval of the day.

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Personalized web page recommendation using case-based clustering and weighted association rule mining

Cited by 27 publications

References 20 publications

Design and Implementation of Book Recommendation Management System Based on Improved Apriori Algorithm

Design and Implementation of Book Recommendation Management System Based on Improved Apriori Algorithm

SentiFilter: A Personalized Filtering Model for Arabic Semi-Spam Content based on Sentimental and Behavioral Analysis

New Performance Index “Attractiveness Factor” for Evaluating Websites via Obtaining Transition of Users’ Interests

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