A hybrid context aware system for tourist guidance based on collaborative filtering

Fenza, Giuseppe; Fischetti, Enrico; Furno, D.; Loia, Vincenzo

doi:10.1109/fuzzy.2011.6007604

Cited by 40 publications

(30 citation statements)

References 22 publications

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“…For example, in (Lakiotaki et al 2011;Mikeli et al 2013), a global k-means clustering process is done to calculate similar users in the system. Some other works also group related alternatives to provide recommendations (Fenza et al 2011;Moreno et al 2015). In Fenza et al (2011), a fuzzy clustering called Fuzzy C-Means (FCM) is developed for a tourist guidance recommender system.…”

Section: Recommmender Systems Considering Multiple Criteriamentioning

confidence: 96%

A hierarchical multi-criteria sorting approach for recommender systems

et al. 2015

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Classification problems refer to the assignment of alternatives to predefined categories. In this work we focus on ordered classification, called sorting, in which the predefined categories indicate several degrees of interest or suitability of alternatives for a certain user. The assignment of alternatives is based on multiple conflicting criteria. This multi-criteria sorting approach is specially interesting for recommender systems aimed at finding the most suitable alternatives for each user. First, we study the ELECTRE-TRI-B sorting method, which follows the outranking approach based on comparing the evaluations of alternatives with the profile limits separating the categories. The complexity of some recommenders systems requires the extension of the classical ELECTRE-TRI-B method to manage a taxonomical organization of the set of criteria. In this paper we consider a set of criteria in the form of a hierarchy. The intermediate criteria in such a hierarchy correspond to different aspects of the recommendation procedure, such as content, context or cost. At each of these criteria, a sorting problem must be solved. Therefore, we propose extending Luis Del Vasto-Terrientes J Intell Inf Syst ELECTRE-TRI-B to handle assignments of alternatives on several levels of the hierarchy. A hierarchical procedure for sorting is proposed, called ELECTRE-TRI-B-H. Secondly, the paper explains the integration of ELECTRE-TRI-B-H into a recommender system of touristic activities related to wine, called GoEno-Tur. This system is developed for the region of Tarragona, Catalonia (Spain), which is a well-recognized area of wine and cava production.

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Section: Recommmender Systems Considering Multiple Criteriamentioning

confidence: 96%

A hierarchical multi-criteria sorting approach for recommender systems

et al. 2015

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“…In the equations, ε is a stop threshold, m is a fuzziness exponent, and || * || is a norm expressing the similarity between any measured datum and the centroid. Regarding both ratings and additional items' information, several works have employed the fuzzy c-means clustering [11,38,40,66,73,87,101,103,128,129,131,141,142], and also similar approaches such as relational fuzzy subtractive clustering [121], co-clustering [45,49,114,133], picture fuzzy clustering [123], folksonomy-focused intuitionistic fuzzy agglomerative hierarchical clustering [43], fuzzy geographical clustering [119], linear fuzzy clustering [48], and other fuzzy clustering approaches [18,29,35,39,61,64,143].…”

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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“…Therefore, a new user would not be able to get accurate recommendations having very few ratings.This problem can be addressed by various techniques like hybrid approach of recommendations or strategies based upon item popularity, Item entropy,user personalization or combination of all. [4], [5]. 3) New item problem: Collaborative recommended systems completely rely on users' preferences to make recommendations.…”

confidence: 99%

Location Based Personalized Recommendation Systems for the Tourists in India

Sahoo¹

2017

IJRASET

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In the domain of tourism this study presents collaborative filtering in recommender system in tourism sector by categorizing users according to their choice of place, food, local item purchase etc. Two hundred (Male = 110, Female = 90) married individuals from Bhubaneswar, Odisha (India) participated in the study. By applying collaborative filtering algorithms improved the rating prediction by the users. The results revealed that collaborative filtering in recommender system having more reliable as per query user. Experimental results show performance in terms of precision and recall values.

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A hybrid context aware system for tourist guidance based on collaborative filtering

Cited by 40 publications

References 22 publications

A hierarchical multi-criteria sorting approach for recommender systems

A hierarchical multi-criteria sorting approach for recommender systems

Fuzzy Tools in Recommender Systems: A Survey

Location Based Personalized Recommendation Systems for the Tourists in India

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