Harnessing user’s social influence and IoT data for personalized event recommendation in event-based social networks

Mahajan, Pratibha; Kaur, Pankaj Deep

doi:10.1007/s13278-021-00722-6

Cited by 8 publications

(1 citation statement)

References 38 publications

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“…The algorithm recommends an event to the user with the help of a prediction score for IoT-based factors and CF based on social influencers. Hence, the event with the highest prediction score is recommended to the user [24]. A travel recommendation technique based on a machine learning classifier that processing user activities in social networking is presented [25].…”

Section: Recent Studies In Social Networking Recommendationmentioning

confidence: 99%

Cosine similarity-based algorithm for social networking recommendation

Al-Otaibi

Altwoijry

Alqahtani

et al. 2022

IJECE

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Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences. Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities.

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Section: Recent Studies In Social Networking Recommendationmentioning

confidence: 99%

Cosine similarity-based algorithm for social networking recommendation

Al-Otaibi

Altwoijry

Alqahtani

et al. 2022

IJECE

View full text Add to dashboard Cite

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3T-IEC*: a context-aware recommender system architecture for smart social networks (EBSN and SBSN)

Mahajan

Kaur²

2022

J Intell Inf Syst

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DFGR: Diversity and Fairness Awareness of Group Recommendation in an Event-based Social Network

Liang

2023

Neural Process Lett

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An event-based social network is a new type of social network that combines online and offline networks, and one of its important problems is recommending suitable activities to users. However, the current research seldom considers balancing the accuracy, diversity and fairness of group activity recommendations. To solve this problem, we propose a group activity recommendation approach that considers fairness and diversity perception. Firstly, we calculate activity similarity based on the context and construct an activity similarity graph. We define the weighted coverage on the similarity graph as a submodular function and transform the problem of fair and diverse group activity recommendation into maximizing the weighted coverage on the similarity graph while considering accuracy, fairness, and diversity. Secondly, we employ a greedy algorithm to find an approximate solution that maximizes the weighted coverage with an approximation ratio. Finally, we conducted experiments on two real datasets and demonstrate the superiority of our method compared to existing approaches. Specifically, in the domain of diversity-based recommendation algorithms, our method achieves a remarkable 0.02% increase in recall rate. Furthermore, in the domain of fairness-based recommendation algorithms, our proposed method outperforms the latest approach by 0.05% in terms of overall metrics. These results highlight the effectiveness of our method in achieving a better balance among accuracy, fairness, and diversity.

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Harnessing user’s social influence and IoT data for personalized event recommendation in event-based social networks

Cited by 8 publications

References 38 publications

Cosine similarity-based algorithm for social networking recommendation

Cosine similarity-based algorithm for social networking recommendation

3T-IEC*: a context-aware recommender system architecture for smart social networks (EBSN and SBSN)

DFGR: Diversity and Fairness Awareness of Group Recommendation in an Event-based Social Network

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