Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and trust

Birtolo, Cosimo; Ronca, Davide

doi:10.1016/j.eswa.2013.06.022

Cited by 86 publications

(39 citation statements)

References 28 publications

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“…Similarly, other approaches considering trust in memory-based collaborative filtering scenarios were developed at Birtolo and Ronca [19] and Bedi and Vashisth [15]. Table 4 presents further analysis of the referred research works, by additionally including other important aspects such as evaluation approaches, used datasets, and application areas.…”

Section: Sim(a B)mentioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

152

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Recommender systems are currently successful solutions for facilitating access for online users to the information that fits their preferences and needs in overloaded search spaces. In the last years several methodologies have been developed to improve their performance. This paper is focused on developing a review on the use of fuzzy tools in recommender systems, for detecting the more common research topics and also the research gaps, in order to suggest future research lines for boosting the current developments in fuzzy-based recommender systems. Specifically, it is developed an analysis of the papers focused at such aim, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas.

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Section: Sim(a B)mentioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

152

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“…This method is called TBRSK. The main purpose of this approach is to use the ant colony parallel with TRACCF in [8] to increase the coverage rate and predict the ratings that TRACCF is not capable of. It should be noted www.ijacsa.thesai.org that when TRACCF cannot calculate the prediction, the proposed approach can find trusted friends for the active user using the ant colony and predicts the desired ratings.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…After calculating the similarity and trust among users, the ratings are predicted through combining users' similarity and trust values, as in (3), also used in [8].…”

Section: Predicting Ratingsmentioning

confidence: 99%

A New Strategy in Trust-Based Recommender System using K-Means Clustering

Sani¹,

Tabriz²

2017

ijacsa

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Abstract-Recommender systems are among the most important parts of online systems, including online stores such as Amazon, Netflix that have become very popular in the recent years. These systems lead users to finding desired information and goods in electronic environments. Recommender systems are one of the main tools to overcome the problem of information overload. Collaborative filtering (CF) is one of the best approaches for recommender systems and are spreading as a dominant approach. However, they have the problem of coldstart and data sparsity. Trust-based approaches try to create a neighborhood and network of trusted users that demonstrate users' trust in each other's opinions. As such, these systems recommend items based on users' relationships. In the proposed method, we try to resolve the problems of low coverage rate and high RMSE rate in trust-based recommender systems using kmeans clustering and ant colony algorithm (TBRSK). For clustering data, the k-means method has been used on MovieLens and Epinion datasets and the rating matrix is calculated to have the least overlapping.

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“…It is a promising way to improve the scalability of collaborative filtering by reducing the search for the Neighborhoods in the preference space, and generates some recommendations for users without using entire dataset. Birtolo, D. Ronca [15] proposed Item-based Fuzzy Clustering recommender system which locating users in suitable classes and providing recommendations with best clusters of users. This method increases the coverage of predictions.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Collaborative Movie Recommender System Using Clustering and Bat Optimization

Vellaichamy¹,

Vivekanandan²

2017

IJIES

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Abstract:A Recommender system (RS) is an information filtering software that helps users with a personalized manner to recommend online products to Users and give suggestions about the products that he or she might like. In e-commerce, collaborative Movie recommender system assist users to select their favorite movies based on their similar neighbor's movie ratings. However due to data sparsity and scalability problems, neighborhood selection is more challenging with the rapid increasing number of users and movies. In this paper, a hybrid Collaborative Movie Recommender system is proposed that combines Fuzzy C Means clustering (FCM) with Bat optimization to reduce the scalability problem and enhance the clustering which improves recommendation quality. Fuzzy c means clustering is used to cluster the users into different groups. Bat Algorithm is used to obtain the initial position of clusters. Lastly, the proposed system creates movie recommendations for target users. The proposed system was evaluated over Movie Lens dataset. Experiment results obtained show that the proposed Algorithm can yield better recommendation results compared to other techniques in terms of Mean Absolute Error (MAE), precision and Recall.

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Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and trust

Cited by 86 publications

References 28 publications

Fuzzy Tools in Recommender Systems: A Survey

Fuzzy Tools in Recommender Systems: A Survey

A New Strategy in Trust-Based Recommender System using K-Means Clustering

Hybrid Collaborative Movie Recommender System Using Clustering and Bat Optimization

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