Should I Stay or Should I Go? Improving Event Recommendation in the Social Web

Cena, Federica; Likavec, Silvia; Lombardi, Ilaria; Picardi, Claudia

doi:10.1093/iwc/iwu029

Cited by 14 publications

(3 citation statements)

References 30 publications

(36 reference statements)

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“…The authors of [158] suggested an approach to contextualize query internet-scale IoT data and illustrate a method for smart cities using recommendation applications of smart parking space. The authors of [159] suggested a smartphone application for event recommendation such as restaurants [160], cultural heritage [161,162], museums tours [22] monuments [163] route recommendations [164], concerts and events [165], and seminars [166] with the context-aware and tag-based recommendations. This initializes users' accounts with minimal user intervention and the features of the items provide recommendations using a tag-based context-aware recommender algorithm.…”

Section: Events and Activitiesmentioning

confidence: 99%

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

et al. 2021

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Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.

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Section: Events and Activitiesmentioning

confidence: 99%

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

et al. 2021

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“…This gap has been encountered in other areas with an appropriate response. For instance, in the field of developing recommender systems, which focus only on in-hand products/services, social interactions has been considered in various new models (Deng et al, 2014;Yigit et al, 2015;Cena et al, 2016;Feng et al, 2016). Taking inspiration from such fields could be useful.…”

Section: Related Workmentioning

confidence: 99%

Extracting new ideas from the behavior of social network users

Karimi-Majd¹,

Fathian²

2017

10.5267/j.dsl

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Online social networks (OSNs) provide services targeting multifarious types of users in order to attract and retain them. For this purpose, developing new services according to user preferences has recently been under focused by various researchers. Most of present studies focus only on extracting the behavioral patterns of users, and neglect users' interactions, which is the main part of the social activities in OSNs. To cope with this issue, this paper proposes a new methodology to bring both dimensions of data, the extracted behavioral patterns of users and their social interactions, in order to reach a better analysis. Moreover, the idea provides a basis for considering other dimensions efficiently. In order to evaluate the performance of the methodology, this paper performs a case study, and conducts a set of experiments on the computer-generated datasets. The results indicates the great performance of the methodology.Growing Science Ltd. All rights reserved. 7

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“…As the number of OSN users rapidly increases, they can acquire a large amount of content through numerous paths. With such a large amount of content being created, there is an increasing requirement for the users to select from all this information and identify the content that is appropriate for them . Mostly, research has been performed on content recommendation schemes for selectively providing users with only the content they prefer .…”

Section: Introductionmentioning

confidence: 99%

Personalized content recommendation scheme based on trust in online social networks

Bok

Geonsik

Lim

et al. 2019

Concurrency and Computation

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Summary As various social network services have developed, users are creating and sharing a large amount of content. Concurrently, there have been many studies on recommendation schemes for providing users with content that matches their preferences. In this paper, we propose a trust‐based personalized content recommendation scheme using collaborative filtering in online social network services. The user trust is calculated by analyzing social activities, content usages, and social relationships. In addition, the content trust is calculated by analyzing user expertise and reputations. Collaborative filtering is performed on users who are filtered through the user trust, and recommendation priorities are determined according to the content trust. The proposed scheme can improve the performance of collaborative filtering by eliminating untrustworthy users using user trust. It also improves the accuracy of recommendations since it provides recommendations based on content trust. Therefore, the proposed scheme can improve the performance of recommendation services using collaborative filtering in online social network services that share multimedia content. Performance evaluation is performed in terms of MAE and RMSE, which assesses errors in recommended results to demonstrate the superiority of the proposed scheme. Performance evaluations have shown that errors in the proposed scheme are reduced compared to the existing schemes, improving the accuracy of recommendations.

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Should I Stay or Should I Go? Improving Event Recommendation in the Social Web

Cited by 14 publications

References 30 publications

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

Extracting new ideas from the behavior of social network users

Personalized content recommendation scheme based on trust in online social networks

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