GuideMe – A Tourist Guide with a Recommender System and Social Interaction

Umanets, Artem; Ferreira, Artur J.; Leite, Nuno

doi:10.1016/j.protcy.2014.10.248

Cited by 51 publications

(23 citation statements)

References 5 publications

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“…The third category is dedicated to the various applications of Recommender Systems. This include travel package recommendations (Patil & Kolhe, 2014), tourist guide (Umanets, Ferreira, & Leite, 2014), radio station hosting (Ignatov, Nikolenko, Abaev, & Konstantinova, 2014) and the fourth category cover the evaluation of the various Recommender Systems techniques (Hornung et al, 2013;Krohn-Grimberghe, Nanopoulos, & Schmidt-Thieme, 2010;Shinde & Potey, 2015).…”

Section: Related Workmentioning

confidence: 99%

Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

Abdi¹,

Okeyo²,

Mwangi³

2018

CIS

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Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems.

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Section: Related Workmentioning

confidence: 99%

Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

Abdi¹,

Okeyo²,

Mwangi³

2018

CIS

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“…This problem can be overcome through clustering of items and users. 71,73  Scalability: It is seen when the number of users or items increases markedly in the system. However, dimensionality reduction techniques can be used to address this problem.…”

Section: 72mentioning

confidence: 99%

Recommender system introduction for requests of cancer world

Aalipour

Ghazisaeedi

2017

Int J Community Med Public Health

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Awareness of cancer patients and their families, health care providers, and specialized cancer centers is achieved through access to up-to-date information about various items. Today, intelligent information technology systems have an important role in the awareness of people. Therefore, a type of technology is required that is capable of learning people’s needs, interests and suggesting appropriate information accordingly. The emergence of information technology systems, like recommender systems, is a step towards selecting appropriate information. With modeling the preferences, interests, needs, requests, and behaviors of the users, recommender systems seek to predict the future preferences, needs, and behaviors of the users to recommend appropriate and helpful services accordingly. Recommender systems can be a suitable tool for the information management of cancer-related screenings, diagnoses, treatments, operations, and rehabilitation programs. Access to treatment and health recommendations from valid sources is an important component of the natural processes of human decision making. The aim of this collection is to introduce recommender systems to use in cancer-related issues.

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“…But the 'Plan together' feature is more like a shared calendar with very little tourism information. Other niche tourism recommender systems available [3] are mainly providing information for individuals rather than groups, such as GUIDEME system [8], STS system [9], and EnoSigTur [10].…”

Section: Role Of Technologymentioning

confidence: 99%

Designing a Trip Planner Application for Groups

Zhang

Sun

2016

Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems

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Due to the consistent growing number of tourists around the world and the increasing use of online services to plan trips, this paper investigates the group tourists' trip planning requirements and how technology might support this group planning process. We investigated 12 groups' real trip planning behaviour and requirements through a diary study and a follow-up interview. The experiment was designed and analysed based on Activity Theory, a theoretical construct commonly applied in human computer interaction (HCI) and computer supported cooperative work (CSCW). From our preliminary findings, we report group's trip planning requirements in three aspects: division of labour & information search, intra-group communication, and culture difference (i.e., Chinese users' characteristics). To facilitate group tourists' planning experience, this paper concludes HCI design implications from three perspectives: tourism information presentation, facilitating intra-group communication, and requirements from Chinese users.

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GuideMe – A Tourist Guide with a Recommender System and Social Interaction

Cited by 51 publications

References 5 publications

Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

Recommender system introduction for requests of cancer world

Designing a Trip Planner Application for Groups

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