A hybrid recommendation approach for a tourism system

Lucas, Joel Pinho; Luz, Nuno; Moreno, María N.; Anacleto, Ricardo; Almeida, Ana; Martins, Constantino

doi:10.1016/j.eswa.2012.12.061

Cited by 154 publications

(69 citation statements)

References 52 publications

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“…A system that is capable to locate an individual can be explored to improve life quality since emergency teams (fire-fighters, military forces [5] and medics) can respond more precisely if the team members location is known, tourists can have better recommendations [8], the elderly can be better monitored [9] and parents can be more relaxed with their children in shopping malls [3].…”

Section: Introductionmentioning

confidence: 99%

Step Count and Classification Using Sensor Information Fusion

Anacleto

Figueiredo

Almeida

et al. 2015

Advances in Intelligent Systems and Computing

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In order to suppress the GNSS (Global Navigation Satellite System) limitation to track persons in indoor or in dense environments, a pedestrian inertial navigation system can be used. However, this type of systems have huge location estimation errors due to the Pedestrian Dead Reckoning (PDR) characteristics and the use of low-cost inertial sensors. To suppress some of these errors we propose a system that uses several sensors spread in person's body combined with information fusion techniques. Information fusion techniques provide lighter algorithms implementations, to count and classify the type of step, to run in mobile devices. Thus, improving pedestrian inertial navigation systems accuracy.

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Section: Introductionmentioning

confidence: 99%

Step Count and Classification Using Sensor Information Fusion

Anacleto

Figueiredo

Almeida

et al. 2015

Advances in Intelligent Systems and Computing

Self Cite

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“…Content-based recommendation algorithms often suffer from over-specialization, which refers to the problem that recommended items are too similar to one another, thus reducing the diversity of results and limiting user choices [38]. To overcome these shortcomings, several recommendation algorithms have been combined into hybrid solutions [21]. Hybrid approaches, similar to our approach, have been proposed in the domain of tourism recommendations [28].…”

Section: Related Workmentioning

confidence: 99%

Hybrid group recommendations for a travel service

Pessemier

Dhondt

Martens

2016

Multimed Tools Appl

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Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations.

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“…Furthermore, hybrid recommender systems have been proposed [7,10,13,18,21,36,38], which combine individual recommender systems to avoid certain limitations of individual recommender systems.…”

Section: Main Types Of Recommender Systemsmentioning

confidence: 99%

Novel centroid selection approaches for KMeans-clustering based recommender systems

Zahra

Ghazanfar

Khalid

et al. 2015

Information Sciences

199

110

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Recommender systems have the ability to filter unseen information for predicting whether a particular user would prefer a given item when making a choice. Over the years, this process has been dependent on robust applications of data mining and machine learning techniques, which are known to have scalability issues when being applied for recommender systems. In this paper, we propose a k-means clustering-based recommendation algorithm, which addresses the scalability issues associated with traditional recommender systems. An issue with traditional k-means clustering algorithms is that they choose the initial k centroid randomly, which leads to inaccurate recommendations and increased cost for offline training of clusters. The work in this paper highlights how centroid selection in k-means based recommender systems can improve performance as well as being cost saving. The proposed centroid selection method has the ability to exploit underlying data correlation structures, which has been proven to exhibit superior accuracy and performance in comparison to the traditional centroid selection strategies, which choose centroids randomly. The proposed approach has been validated with an extensive set of experiments based on five different datasets (from movies, books, and music domain). These experiments prove that the proposed approach provides a better quality cluster and converges quicker than existing approaches, which in turn improves accuracy of the recommendation provided.

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A hybrid recommendation approach for a tourism system

Cited by 154 publications

References 52 publications

Step Count and Classification Using Sensor Information Fusion

Step Count and Classification Using Sensor Information Fusion

Hybrid group recommendations for a travel service

Novel centroid selection approaches for KMeans-clustering based recommender systems

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