Recommending biomedical resources: A fuzzy linguistic approach based on semantic web

Morales-del-Castillo, José M.; Peis, Eduardo; Ruiz, A. A.

doi:10.1002/int.20447

Cited by 18 publications

(8 citation statements)

References 24 publications

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“…The intention is to establish a mechanism that can measure the performance of the system in terms of precision (how many of the items recommended by the system are actually interesting for the user), recall (how many of the items that are relevant for the user are actually recommended) and F1 (the harmonic mean of precision and recall), as has been done in similar recommender systems ( [25,30,34]). Measuring precision is simpler, since we can assume that all recommendations of news that are finally read by the user are good recommendations.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Refs. [21,24,25,30,34] present recommender systems that deal with different linguistic domains.…”

Section: Related Workmentioning

confidence: 99%

“…A recommender system can be evaluated in terms of precision (how many of the items recommended are interesting for the user) and recall (how many of the items that are of interest for the user are actually recommended) (e.g., [25,30,34]). In our case, the nature of the system makes it difficult to perform such an evaluation, which requires a domain labeled a priori with the correct selections for a specified user and a corpus of examples to recommend.…”

Section: Evaluation Proceduresmentioning

confidence: 99%

“…The initial user profile can be obtained with various techniques: extracting information about user interests from a set of training data or questionnaires [25,30], classifying users into a particular group of users and assigning them a previously created stereotyped profile [1], or allowing a manual specification of the initial profile [23,30,34].…”

Section: Related Workmentioning

confidence: 99%

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On-line dynamic adaptation of fuzzy preferences

Marin

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Moreno

et al. 2013

Information Sciences

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Section: Discussionmentioning

confidence: 99%

“…For example, Refs. [21,24,25,30,34] present recommender systems that deal with different linguistic domains.…”

Section: Related Workmentioning

confidence: 99%

Section: Evaluation Proceduresmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

On-line dynamic adaptation of fuzzy preferences

Marin

Isern

Moreno

et al. 2013

Information Sciences

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“…11 Another study demonstrated the use of Semantic Web technologies and fuzzy linguistic modeling techniques to identify specific biomedical resources from large repositories. 12 …”

Section: Introductionmentioning

confidence: 99%

Using Google Blogs and Discussions to Recommend Biomedical Resources: A Case Study

Reed

Chattopadhyay

Iwema

2013

Medical Reference Services Quarterly

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This case study investigated whether data gathered from discussions within the social media provide a reliable basis for a biomedical resources recommendation system. Using a search query to mine text from Google Blogs and Discussions, a ranking of biomedical resources was determined based on those most frequently mentioned. To establish quality, these results were compared to rankings by subject experts. An overall agreement between the frequency of social media discussions and subject expert recommendations was observed when identifying key bioinformatics and consumer health resources. Testing the method in more than one biomedical area implies this procedure could be employed across different subjects.

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A linguistic multicriteria decision-making model applied to hotel service quality evaluation from web data sources

Carrasco

Villar

Hornos

2012

Int. J. Intell. Syst.

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Currently, there are a large number of hotel Web sites that develop their own seals of quality based on customer feedback. As a result, a hotel can be classified differently by various Web sites at the same time, creating confusion in the consumer perceptions about the quality of a given hotel. Moreover, there are attempts to standardize such service quality evaluation, such as the SERVQUAL instrument, which is a multiple-item scale for measuring service quality with several dimensions. In this context, we present a two-stage linguistic multicriteria decision-making model to integrate the hotel guests' opinions included in several Web sites, with two objectives: on the one hand, obtaining a SERVQUAL scale evaluation value of service quality with the integrated answers of the input opinions; on the other hand, getting a SERVQUAL overall evaluation value of service quality. This model is incorporated into an opinion aggregation architecture to integrate heterogeneous data (natural language included) from various tourism Web sites. As a particular case study, we show an application example using the high-end hotels located in Granada (Spain). Among the successive variants of the latter, the SERVQUAL instrument, 13−17 also called PZB (Parasuraman-Zeithaml-Berry) model, is the most commonly used. 12 SERVQUAL is a multiple-item scale for measuring service quality with five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. Several authors have adapted the SERVQUAL instrument to analyze the hotel guests' expectations and perceptions about the service quality of hotels they have visited. 18−21 Some authors (e.g., see Ref. 18) propose a modified SERVQUAL with a basic questionnaire which presents customers a collection of statements (questions) about the five above-mentioned dimensions to ask them if they are agree or disagree on a five-point Likert scale. 22 One method of evaluating hotel quality is the creation of a ranking based on specific criteria and on the assignment of a symbol that certifies a quality category. 1 The symbol and the scale used can vary from one country to another, but the most commonly used are the star and the diamond, with a scale from one to five. In Europe, hotels are usually ranked on a scale from one to five stars, with five stars being the highest rating possible. In Australia and Canada, a five-star scale is also used, sometimes using half star increments. In the United States, hotels are generally ranked on a scale from one to five stars by the Forbes Travel Guide, whereas the American Automobile Association still uses the diamond on the same scale (from one to five). Even countries that had a different scale have modified the structure of their programs over the last few years, making them more uniform. This method is also the more commonly used in tourism Web sites, 2−8 not only to show the overall rating of the hotel, but also to ask the value of the assessed attributes to customers.Regarding the input of this second MCDM process, the first option is to use the ...

show abstract

Recommending biomedical resources: A fuzzy linguistic approach based on semantic web

Cited by 18 publications

References 24 publications

On-line dynamic adaptation of fuzzy preferences

On-line dynamic adaptation of fuzzy preferences

Using Google Blogs and Discussions to Recommend Biomedical Resources: A Case Study

A linguistic multicriteria decision-making model applied to hotel service quality evaluation from web data sources

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