2012
DOI: 10.1007/s11390-012-1244-x
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Exponential Fuzzy C-Means for Collaborative Filtering

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Cited by 24 publications
(15 citation statements)
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“…A relevant approach is the inclusion of the time dimension to the original input data of collaborative filtering for finding the fuzzy cluster at different time frames, proposing a dynamic membership degree and determining the neighbourhood for a given user based on the dynamic fuzzy cluster [86]. On the other hand, Treerattanapitak and Jaruskulchai [125] propose a new exponential fuzzy clustering (XFCM) algorithm by reformulating the clustering objective function with an exponential equation in order to improve the method in relation to membership calculation. This transformation allows a more aggressive exclusion of irrelevant data from the clusters, improving in this way other fuzzy c-means alternatives.…”
Section: Maementioning
confidence: 99%
“…A relevant approach is the inclusion of the time dimension to the original input data of collaborative filtering for finding the fuzzy cluster at different time frames, proposing a dynamic membership degree and determining the neighbourhood for a given user based on the dynamic fuzzy cluster [86]. On the other hand, Treerattanapitak and Jaruskulchai [125] propose a new exponential fuzzy clustering (XFCM) algorithm by reformulating the clustering objective function with an exponential equation in order to improve the method in relation to membership calculation. This transformation allows a more aggressive exclusion of irrelevant data from the clusters, improving in this way other fuzzy c-means alternatives.…”
Section: Maementioning
confidence: 99%
“…This method increases the coverage of predictions. Treerattanapitak, Jaruskulchai [16] developed exponential fuzzy C means clustering based recommendation by changing the clustering's objective function with an exponential function so that it will enhance the membership assignment and perform well better than other Fuzzy C Means. Koohi, K.Kiani [17] employ a User Based Collaborative Filtering using Fuzzy C Means and its performance is evaluated against different clustering methods such as K-means, Self-Organizing Map (SOM).…”
Section: Related Workmentioning
confidence: 99%
“…Koohi, K.Kiani [17] employ a User Based Collaborative Filtering using Fuzzy C Means and its performance is evaluated against different clustering methods such as K-means, Self-Organizing Map (SOM). The limitation of the above methods [15][16][17] is, FCM is more sensitive to initialization and get stuck in local optima. This affects the clustering and also reduces the recommendation quality.…”
Section: Related Workmentioning
confidence: 99%
“…If you want to predict user u 2 's rating for item i 5 , Sim(u 1 ,u 2 )=0.9548 and Figure 1 that score interval between u 1 and u 2 is [1][2][3][4][5][6], and score interval between u 2 and u 3 is [1][2][3][4]. Therefore, similarity between u 1 and u 2 is smaller than u 2 and u 3 actually.…”
Section: Figure 1 Scoring Matrix Of Users and Itemsmentioning
confidence: 99%
“…According to user's interest in hobbies and historical data, collaborative filtering could find neighbors that have some similarities in these aspects, and make recommendation according to neighbor's action and evaluation data. Different with content based recommendation systems, collaborative filtering algorithm is only looking for neighbors that have the similarity evaluation based on user evaluation of a project, ignores data details, and does not extract the project's text feature vectors [2][3][4]. Collaborative filtering recommendation algorithm is generally divided into two categories: memory based collaborative filtering and model based collaborative filtering.…”
Section: Introductionmentioning
confidence: 99%