Applying Fuzzy Logic to Recommend Consumer Electronics

Soft Computing for Business Intelligence

López

2014

Abstract. CRM (Customer Relationship Management) is one important area of Business Intelligence (BI) where information is strategically used for maximizing the value of each customer in a company. Recommender systems constitute a suitable context to apply CRM strategies. This kind of systems are becoming indispensable in the e-commerce environment since they represent a way of increasing customer satisfaction and taking positions in the competitive market of the electronic business activities. They are used in many application domains to predict consumer preferences and assist web users in the search of products or services. There are a wide variety of methods for making recommendations; however, in spite of the advances in the methodologies, recommender systems still present some important drawbacks that prevent from satisfying entirely their users. This chapter presents one of the most promising approaches consisting of combining data mining and fuzzy logic.

Section: Fuzzy Methodsmentioning

confidence: 99%

Fuzzy Data-Mining Hybrid Methods for Recommender Systems

Soft Computing for Business Intelligence

López

2014

“…Cao et al 44 have implemented a recommender system based on fuzzy concepts in order to recommend to users products not usually consumed. Moreover, it can always have situations in which there may not be enough information about the customer's past purchases and the customer may have his specific requirements in each single purchase.…”

Section: Fuzzy Associative Classification In Recommender Systemsmentioning

confidence: 99%

“…Moreover, it can always have situations in which there may not be enough information about the customer's past purchases and the customer may have his specific requirements in each single purchase. 44 In this context, authors use fuzzy set's operations in order to define relationships between user requirements and products' features.…”

Section: Fuzzy Associative Classification In Recommender Systemsmentioning

confidence: 99%

A Fuzzy Associative Classification Approach for Recommender Systems

Laurent

Int. J. Unc. Fuzz. Knowl. Based Syst.

et al. 2012

Despite the existence of different methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by means of an alternative data mining technique called classification based on association, which uses association rules in a prediction perspective. In this work we propose a hybrid methodology for recommender systems, which uses collaborative filtering and content-based approaches in a joint method taking advantage from the strengths of both approaches. Moreover, we also employ fuzzy logic to enhance recommendations' quality and effectiveness. In order to analyze the behavior of the techniques used in our methodology, we accomplished a case study using real data gathered from two recommender systems. Results revealed that such techniques can be applied effectively in recommender systems, minimizing the effects typical drawbacks they present.

“…There are more specific works, such as (Yager, 2003), where fuzzy set methods are used to describe information in a content-based recommender system, or (Cao, Li, & Liao, 2005) where fuzzy concepts are used in order to recommend to users products not usually consumed. This work also addresses other situations in which there may not be enough information about the customer's past purchases and the customer may give his specific requirements in each single purchase.…”

Section: Associative Classification and Fuzzy Logicmentioning

confidence: 99%

A hybrid recommendation approach for a tourism system

Luz

Expert Systems with Applications

et al. 2013

157

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