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A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence.

Alphy A, Prabakaran S - ScientificWorldJournal (2015)

Bottom Line: Our dynamic recommender system was compared against traditional collaborative filtering systems.The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems.Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, SRM University, Chennai 603203, India.

ABSTRACT
In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.

No MeSH data available.


Bluegill-BestPredictions algorithm.
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Related In: Results  -  Collection


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alg4: Bluegill-BestPredictions algorithm.

Mentions: In the proposed WebBluegillRecom-annealing algorithm (Algorithm 1), each user is mapped to an agent. All the agents are placed on the visualization panel randomly. To bring similar agents closer and dissimilar agents far apart, a cooling algorithm (Algorithm 2) is applied. Then the clusters of agents are formed using cluster-creation algorithm (Algorithm 3). It groups similar agents into the same cluster. That is, users having similar interests belong to the same cluster. This initial set of clusters can be used for further processing. These initial clusters are given as input to the Bluegill-BestPredictions algorithm (Algorithm 4). The Bluegill-BestPredictions algorithm can optimize these initial clusters by identifying a better neighborhood for agents in each cluster forming another hinterland. Moreover, it can assign new dynamic data representing a new dynamic behavior of user to the most similar cluster. It performs dynamic clustering of dynamic data and gives the users the finest recommendations by predicting the best I items preferred by the neighborhood agents. Bluegill-BestPredictions algorithm gives dynamic recommendations to users. Since the recommendations are dynamic, the WebBluegillRecom-annealing algorithm can satisfy the needs of old and new users. The following part explains each of these algorithms in detail.


A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence.

Alphy A, Prabakaran S - ScientificWorldJournal (2015)

Bluegill-BestPredictions algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4502334&req=5

alg4: Bluegill-BestPredictions algorithm.
Mentions: In the proposed WebBluegillRecom-annealing algorithm (Algorithm 1), each user is mapped to an agent. All the agents are placed on the visualization panel randomly. To bring similar agents closer and dissimilar agents far apart, a cooling algorithm (Algorithm 2) is applied. Then the clusters of agents are formed using cluster-creation algorithm (Algorithm 3). It groups similar agents into the same cluster. That is, users having similar interests belong to the same cluster. This initial set of clusters can be used for further processing. These initial clusters are given as input to the Bluegill-BestPredictions algorithm (Algorithm 4). The Bluegill-BestPredictions algorithm can optimize these initial clusters by identifying a better neighborhood for agents in each cluster forming another hinterland. Moreover, it can assign new dynamic data representing a new dynamic behavior of user to the most similar cluster. It performs dynamic clustering of dynamic data and gives the users the finest recommendations by predicting the best I items preferred by the neighborhood agents. Bluegill-BestPredictions algorithm gives dynamic recommendations to users. Since the recommendations are dynamic, the WebBluegillRecom-annealing algorithm can satisfy the needs of old and new users. The following part explains each of these algorithms in detail.

Bottom Line: Our dynamic recommender system was compared against traditional collaborative filtering systems.The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems.Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, SRM University, Chennai 603203, India.

ABSTRACT
In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.

No MeSH data available.