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Book ChapterDOI

Crowdsourcing Based Fuzzy Information Enrichment of Tourist Spot Recommender Systems

TL;DR: F fuzzy inference is proposed to compute a new score/rank, with each recommended spot, for each spot to be recommender based on the recommender's rank and current context.
Abstract: Tourist Spot Recommender Systems TSRS help users to find the interesting locations/spots in vicinity based on their preferences. Enriching the list of recommended spots with contextual information such as right time to visit, weather conditions, traffic condition, right mode of transport, crowdedness, security alerts etc. may further add value to the systems. This paper proposes the concept of information enrichment for a tourist spot recommender system. Proposed system works in collaboration with a Tourist Spot Recommender System, takes the list of spots to be recommended to the current user and collects the current contextual information for those spots. A new score/rank is computed for each spot to be recommender based on the recommender's rank and current context and sent back to the user. Contextual information may be collected by several techniques such as sensors, collaborative tagging folksonomy, crowdsourcing etc. This paper proposes an approach for information enrichment using just in time location aware crowdsourcing. Location aware crowdsourcing is used to get current contextual information about a spot from the crowd currently available at that spot. Most of the contextual parameters such as traffic conditions, weather conditions, crowdedness etc. are fuzzy in nature and therefore, fuzzy inference is proposed to compute a new score/rank, with each recommended spot. The proposed system may be used with any spot recommender system, however, in this work a personalized tourist spot recommender system is considered as a case for study and evaluation. A prototype system has been implemented and is evaluated by 104 real users.
Citations
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Journal ArticleDOI
TL;DR: An analysis of the papers focused at boosting the current developments in fuzzy-based recommender systems, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas is developed.
Abstract: 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.

127 citations


Cites background from "Crowdsourcing Based Fuzzy Informati..."

  • ...Tiwari and Kaushik [124] Use a fuzzy inference system that manages dimensions such as traffic conditions, security, or suitable transportation No Gathered by the authors Tourist spots...

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  • ...Beyond these two key researches, other authors such as Chao et al. [27], Sobecki et al. [116], Jeon et al. [55], Nguyen and Duong [90] and Tiwari and Kaushik [124], have also developed more application-oriented recommendation approaches supported by fuzzy inference processes....

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  • ...[55], Nguyen and Duong [90] and Tiwari and Kaushik [124], have also developed more application-oriented recommendation approaches supported by fuzzy inference processes....

    [...]

Journal ArticleDOI
01 Jun 2020
TL;DR: Previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological service and are contributing towards a systemic and intelligent knowledge service to establish a better bridge among academic, industrial and individual community.

17 citations

Proceedings ArticleDOI
01 Nov 2018
TL;DR: This research creates a model which will recommend relevant learning content to a learner, from the huge available data, which gave recommendations that were commensurate with the assesment test marks and the time taken to complete the exams.
Abstract: There has been a huge growth of data in education due to integration of ICT in educational and the evolution of E-learning, to bring about technology enabled learning. This huge data in education has subjected learners to information overload in education, without a well-known mechanism to enable learners select their relevant learning content from the huge educational data. This has subjected learners, in most of the times, to take a lot of time accessing data which could not be relevant to their particular learning needs. This research therefore creates a model which will recommend relevant learning content to a learner, from the huge available data. In designing the model we employed context awareness recommender approach. The model will collect the learner’s context. It will then apply type-1 fuzzy logic data mining method to recommend relevant learning content to the learner; based on the learner’s assesment score and the time taken to complete the assesment test. The model was tested using a primary designed dataset stored in the database. The model gave recommendations that were commensurate with the assesment test marks and the time taken to complete the exams. Thus the higher the assessment score, the higher the crisp output; while the higher the time taken to complete the exams, the lower the crisp output.

11 citations


Cites methods from "Crowdsourcing Based Fuzzy Informati..."

  • ...As suggested by references [24] and [25], we preferred to use the triangular membership function (TriMf) of our fuzzy set; due to the linear nature of our antecedents and consequents, making it better situated membership function over the others....

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Book ChapterDOI
21 Jun 2017
TL;DR: This work, which supports the “dreaming stage”, proposes the automatic recommendation of personalised destinations based on textual reviews using a semantic content-based filter of crowd-sourced information.
Abstract: Nowadays tourists rely on technology for inspiration, research, booking, experiencing and sharing. Not only it provides access to endless sources of information, but has become an unbounded source of tourist-related data. In such crowd-sourced data-intensive scenario, we argue that new approaches are required to enrich current and new travelling experiences. This work, which supports the “dreaming stage”, proposes the automatic recommendation of personalised destinations based on textual reviews, i.e., a semantic content-based filter of crowd-sourced information. Our approach relies on Topic Modelling – to extract meaningful information from textual reviews – and Semantic Similarity – to identify relevant recommendations. Our main contribution is the processing of crowd-sourced tourism information employing data mining techniques in order to automatically discover untapped destinations on behalf of tourists.

9 citations

Proceedings Article
Fátima Leal1
30 Aug 2018
TL;DR: This PhD dissertation focuses on the problem of the personalisation of tourism recommendations based on crowdsourced information, and designed multiple recommendation approaches to suggest tourism resources, supporting the travel cycle.
Abstract: In the last decades, travelling has changed dramatically due to the evolution and popularisation of ICT as well as mobile devices, namely, smartphones. Concretely, ICT have revolutionised the tourism industry as well as the tourist behaviour by allowing permanent access to Web-based platforms holding large amounts of crowdsourced information. Crowdsourcing has become an essential source of information for tourists and the tourism industry. Increasingly, tourists search on and contribute to tourism crowdsourcing platforms. Every day, large volumes of tourism-related knowledge accumulates as tourists leave their digital footprints in the form of searches, posts, shares, reviews or ratings in these platforms. This crowdsourced information classifies prior tourist experiences and influences the planning and decision making behaviour of future tourists. Although tourism crowdsourced information influences decision making, typically, a tourist cannot monitor or control his/her own crowdsourced footprint to enhance his/her options, due to the complexity of the diverse platforms and resources. The problem of selecting tourism information, from large and heterogeneous datasets, based on the tourist profile is complex and requires specific tools. Using recommendation systems it is possible to suggest tourism resources according to the tourist digital footprint, i.e., the tourist profile, using artificial intelligence methodologies to mine the crowdsourced information. In this PhD dissertation, we focus on the problem of the personalisation of tourism recommendations based on crowdsourced information. In this context, we have designed multiple recommendation approaches to suggest tourism resources, supporting the travel cycle. Concretely, our contributions address: (i) the impact of ICT in the tourist experience; (ii) the profiling of tourists and resources based on crowdsourced ratings, reviews and views; and (iii) the personalised recommendation of tourism resources, using off-line and on-line content-based and collaborative algorithms as well as post-filters.

5 citations


Cites background from "Crowdsourcing Based Fuzzy Informati..."

  • ...Tiwari & Kaushik (2014, 2015) present a mobile location-aware tourism recommendation system enriched by Crowdsourcing....

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References
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Journal ArticleDOI
01 May 1993
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Abstract: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >

15,085 citations

Journal ArticleDOI
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Abstract: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

9,873 citations

Journal ArticleDOI
TL;DR: An introduction to crowdsourcing is provided, both its theoretical grounding and exemplar cases, taking care to distinguish crowdsourcing from open source production.
Abstract: Crowdsourcing is an online, distributed problem-solving and production model that has emerged in recent years. Notable examples of the model include Threadless, iStockphoto, InnoCentive, the Goldcorp Challenge, and user-generated advertising contests. This article provides an introduction to crowdsourcing, both its theoretical grounding and exemplar cases, taking care to distinguish crowdsourcing from open source production. This article also explores the possibilities for the model, its potential to exploit a crowd of innovators, and its potential for use beyond forprofit sectors. Finally, this article proposes an agenda for research into crowdsourcing.

2,019 citations

Book ChapterDOI
23 Aug 2004
TL;DR: This paper describes the context-aware mobile tourist application COMPASS that adapts its services to the user’s needs based on both the user's interests and his current context and describes how this integration has been accomplished.
Abstract: This paper describes the context-aware mobile tourist application COMPASS that adapts its services to the user’s needs based on both the user’s interests and his current context. In order to provide context-aware recommendations, a recommender system has been integrated with a context-aware application platform. We describe how this integration has been accomplished and how users feel about such an adaptive tourist application.

420 citations

Proceedings ArticleDOI
Xin Lu1, Changhu Wang2, Jiangming Yang2, Yanwei Pang1, Lei Zhang2 
25 Oct 2010
TL;DR: This paper proposes to leverage existing travel clues recovered from 20 million geo-tagged photos collected from www.panoramio.com to suggest customized travel route plans according to users' preferences, and can provide a customized trip plan for a tourist.
Abstract: Travel route planning is an important step for a tourist to prepare his/her trip. As a common scenario, a tourist usually asks the following questions when he/she is planning his/her trip in an unfamiliar place: 1) Are there any travel route suggestions for a one-day or three-day trip in Beijing? 2) What is the most popular travel path within the Forbidden City? To facilitate a tourist's trip planning, in this paper, we target at solving the problem of automatic travel route planning. We propose to leverage existing travel clues recovered from 20 million geo-tagged photos collected from www.panoramio.com to suggest customized travel route plans according to users' preferences. As the footprints of tourists at memorable destinations, the geo-tagged photos could be naturally used to discover the travel paths within a destination (attractions/landmarks) and travel routes between destinations. Based on the information discovered from geo-tagged photos, we can provide a customized trip plan for a tourist, i.e., the popular destinations to visit, the visiting order of destinations, the time arrangement in each destination, and the typical travel path within each destination. Users are also enabled to specify personal preference such as visiting location, visiting time/season, travel duration, and destination style in an interactive manner to guide the system. Owning to 20 million geo-tagged photos and 200,000 travelogues, an online system has been developed to help users plan travel routes for over 30,000 attractions/landmarks in more than 100 countries and territories. Experimental results show the intelligence and effectiveness of the proposed framework.

300 citations