A Fuzzy Associative Classification Approach for Recommender Systems

Lucas, Joel Pinho; Laurent, Anne; Moreno, María N.; Teisseire, Maguelonne

doi:10.1142/s0218488512500274

Cited by 30 publications

(22 citation statements)

References 39 publications

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“…In a different direction, it was also detected a group of works focused on the use of fuzzy association rule mining for supporting recommendation (Table 8). Such group is composed of the researches developed by Chen and Tai [28], Pinho Lucas et al [98] [90] Model-based collaborative filtering using a fuzzy neural network to learn user's behaviours for video recommendation MAE, RMSE Netflix Video recommendation Leung et al [70], and Teng et al [122]. Specifically, Leung et al [70] introduce a collaborative filtering approach based on fuzzy association rules and multiple-level similarity.…”

Section: Maementioning

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: Maementioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

152

View full text Add to dashboard Cite

show abstract

“…This type of methods has been developed more recently in order to avoid the sparsity problem (Lucas, Laurent, Moreno, & Teisseire, 2012b;Sasikala & Vidhya, 2012) that affects mainly to memory based methods, but model based ones are also affected by it. Methods within this category usually apply data mining algorithms in order to build the recommendation models, which are used to predict user preferences.…”

Section: Related Workmentioning

confidence: 99%

“…Fuzzy logic has also been successfully applied in this field in combination with associative classification (Lucas et al, 2012b). For that reason, the recommender method used in the tourism system presented in this paper is mainly based on fuzzy logic and associative classification.…”

Section: Associative Classification and Fuzzy Logicmentioning

confidence: 99%

“…The proposal take into account the main drawbacks of recommender system, described in subsection 2.3, and offers the appropriate mechanisms to minimize their effects. This framework is an adaptation for a tourism system of a previous generic approach, which has been empirically validated with data from MovieLens and BookCrossing databases (Lucas et al, 2012b).…”

Section: Recommendation Frameworkmentioning

confidence: 99%

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A hybrid recommendation approach for a tourism system

Lucas

Luz

Moreno

et al. 2013

Expert Systems with Applications

Self Cite

157

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A B S T R A C TMany current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulner-able to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.K e y w o r d s : Recommender systems, Associative classification, Fuzzy logic

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“…Using the cosine coefficient, convenient offers were provided to the user based on the matching degrees and the degree of pages belonging to clusters (IPACT). Lucas et al (2012) suggested a new recommender system based on fuzzy logic and associative classification. In this paper, a CBA fuzzy algorithm was used to classify users and to apply association rules.…”

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

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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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A Fuzzy Associative Classification Approach for Recommender Systems

Cited by 30 publications

References 39 publications

Fuzzy Tools in Recommender Systems: A Survey

Fuzzy Tools in Recommender Systems: A Survey

A hybrid recommendation approach for a tourism system

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

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