Sequential group recommendations based on satisfaction and disagreement scores

Stratigi, Maria; Pitoura, Evaggelia; Nummenmaa, Jyrki; Stefanidis, Kostas

doi:10.1007/s10844-021-00652-x

Cited by 15 publications

(3 citation statements)

References 37 publications

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“…Jeong and Kim (2019) proposed a group recommendation method that considers the cohesiveness of group members, divides the group into strong and weak cohesive groups by the number of bystanders in the group and optimizes the preference aggregation strategy by differentially considering the weight of group members in different cohesive groups. Stratigi et al (2022) pointed out that historical recommendation results should be used to optimize the needs of group members, i.e. user feedback needs to be exploited.…”

Section: Related Workmentioning

confidence: 99%

A group recommender system for books based on fine-grained classification of comments

Xiong

Guo

et al. 2023

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Purpose The purpose of this study is to investigate how book group recommendations can be used as a meaningful way to suggest suitable books to users, given the increasing number of individuals engaging in sharing and discussing books on the web. Design/methodology/approach The authors propose reviews fine-grained classification (CFGC) and its related models such as CFGC1 for book group recommendation. These models can categorize reviews successively by function and role. Constructing the BERT-BiLSTM model to classify the reviews by function. The frequency characteristics of the reviews are mined by word frequency analysis, and the relationship between reviews and total book score is mined by correlation analysis. Then, the reviews are classified into three roles: celebrity, general and passerby. Finally, the authors can form user groups, mine group features and combine group features with book fine-grained ratings to make book group recommendations. Findings Overall, the best recommendations are made by Synopsis comments, with the accuracy, recall, F-value and Hellinger distance of 52.9%, 60.0%, 56.3% and 0.163, respectively. The F1 index of the recommendations based on the author and the writing comments is improved by 2.5% and 0.4%, respectively, compared to the Synopsis comments. Originality/value Previous studies on book recommendation often recommend relevant books for users by mining the similarity between books, so the set of book recommendations recommended to users, especially to groups, always focuses on the few types. The proposed method can effectively ensure the diversity of recommendations by mining users’ tendency to different review attributes of books and recommending books for the groups. In addition, this study also investigates which types of reviews should be used to make book recommendations when targeting groups with specific tendencies.

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Section: Related Workmentioning

confidence: 99%

A group recommender system for books based on fine-grained classification of comments

Xiong

Guo

et al. 2023

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“…This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/deed.uk) Information systems and technology ISSN 2617-4316 (Print) ISSN 2663-7723 (Online) recommend products or services suitable for group purchases or activities, promote collaborative learning in educational settings, aid in event planning and entertainment recommendations, and contribute to diversity and inclusion efforts by considering the diverse preferences, backgrounds, and perspectives within a group [4].…”

Section: Introduction Formulation Of the Problemmentioning

confidence: 99%

Methods of preference aggregation in group recommender systems

Gorbatenko,

Hodovychenko

2024

AAIT

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The rapid growth of data volumes has led to information overload, which impedes informed decision-making. To solve this problem, recommender systems have emerged that analyze user preferences and offer relevant products on their own. One type of recommender system is group recommender systems, which are designed to facilitate collaborative decision-making, increase user engagement, and promote diversity and inclusion. However, these systems face challenges such as accommodating diverse group preferences and maintaining transparency in recommendation processes. In this study, we propose a method for aggregating preferences in group recommendation systems to retain as much information as possible from group members and improve the accuracy of recommendations. The proposed method provides recommendations to groups of users by avoiding the aggregation process in the first steps of recommendation, which preserves information throughout the group recommendation process and delays the aggregation step to provide accurate and diverse recommendations. When the object of a collaborative filtering-based recommender system is not a single user but a group of users, the strategy for calculating similarity between individual users to find similarity should be adapted to avoid aggregating the preferences of group members in the first step. In the proposed model, the nearest neighbors of a group of users are searched, so the method of finding neighbors is adapted to compare individual users with the group profile. An experimental study has shown that the proposed method achieves a satisfactory balance between accuracy and diversity. This makes it well suited for providing recommendations to large groups in situations where accuracy is more or less important compared to diversity. These results support the assumption that retaining all information from group members without using aggregation techniques can improve the performance of group recommender systems, taking into account various features.

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“…Постановка завдання. В останні роки широке використання сенсорних технологій, технологій зберігання даних, комп'ютерних технологій та мережевих технологій призвело до значного зростання обсягів даних [1]. Однак, оскільки кількість даних продовжує зростати, люди стикаються з труднощами в роботі з великим обсягом інформації, що обмежує їхню здатність приймати добре поінформовані та відповідні рішення.…”

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Підходи До Зниження Природнього Шуму В Групових Рекомендаційних Системах

Горбатенко

2024

НТС

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This paper addresses the issue of the impact of noise on the effectiveness of group recommender systems (GRS). Noise in this context is defined as irrelevant or false information that can distort the recommendations displayed by the system. The article analyzes in detail the sources of noise in GRSs, including incorrectly entered data by users, data processing errors, and fluctuations in user preferences. Significant attention is paid to filtering strategies and algorithms that can be used to reduce the impact of noise, thus providing more accurate and useful recommendations for user groups.The paper proposes four approaches to reducing natural noise based on the assumption that there is local data related to the preferences of group members and global data related to the preferences of all users of the recommender system. One of the main aspects of the article is the discussion of different methodologies for aggregating group preferences, which allow to optimize information processing and increase the relevance of recommendations for all group members. The authors point out the importance of choosing an adequate aggregation strategy, as it affects the system's ability to minimize the impact of noise and take into account the dynamics and diversity of preferences within the group. The article also presents an experimental study that illustrates the application of the discussed methods in practice. Through the analysis of real data and modeling of GDS usage scenarios, the effectiveness of different techniques in the context of different types of groups and interaction scenarios is investigated. The final part of the paper emphasizes potential areas for further research in the field of optimizing group recommender systems, in particular, the development of more flexible and adaptive systems that can effectively cope with noise-related challenges. The authors call for greater attention to machine learning and artificial intelligence aspects that can help further develop and improve GRSs, increasing their accuracy and usefulness in real-world settings.

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Sequential group recommendations based on satisfaction and disagreement scores

Cited by 15 publications

References 37 publications

A group recommender system for books based on fine-grained classification of comments

A group recommender system for books based on fine-grained classification of comments

Methods of preference aggregation in group recommender systems

Підходи До Зниження Природнього Шуму В Групових Рекомендаційних Системах

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