A Generative Model Approach for Geo-Social Group Recommendation

Zhu, Huihui; Liu, Yanchi; Zhou, Ziting; Li, Zhixu; Xu, Jiajie; Zhao, Lei; Sheng, Victor S.

doi:10.1007/s11390-018-1852-1

Cited by 19 publications

(7 citation statements)

References 27 publications

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“…Finally, the item scores were obtained by combining the results of both parts. Zhao et al (2018) modeled user preferences by considering three factors: geographic information; group theme and social influence; and obtained group preferences by aggregating member preferences to estimate the item’s score for the group. …”

Section: Related Workmentioning

confidence: 99%

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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%

“…Finally, the item scores were obtained by combining the results of both parts. Zhao et al (2018) modeled user preferences by considering three factors:…”

Section: General Group Recommendationmentioning

confidence: 99%

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

Xiong

Guo

et al. 2023

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“…Geographical information considerably affects the check-in behavior of users (Liu et al, 2020). For example, research shows that users are usually willing to visit surrounding places (Zhao et al, 2018), so we can understand users' behavior by finding patterns in users' check-ins, reviews, ratings, friendships, etc.…”

Section: Geographical Influence In Poi Recommendationsmentioning

confidence: 99%

Behavior‐based POI recommendation for small groups in location‐based social networks

Sojahrood

Taleai

2021

Transactions in GIS

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In point-of-interest (POI) group recommender systems, suggesting the most suitable visit places for a heterogeneous group in which members are far from each other would be difficult in terms of finding a consensus among group members. In this article, we argue that the geographical proximity of POIs to users' locations has a notable influence on group decisions to visit the POI and their check-in behavior. We develop a new geographical model based on the check-in behavior of the group in location-based social networks to improve group recommendations by introducing a new concept called users' spatial ratio in a group vectors and using a 2D kernel density estimation method. To evaluate the performance of

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“…2) Evaluating the performance of activity location prediction: For activity location prediction, we focus on the activity location prediction based on the activity groups obtained above. This paper utilizes two metrics to demonstrate the performance of proposed method and baselines: P recision@K [7] [34] and normalized discounted cumulative gain nDCG@K [30], where K is the number of recommended activity locations (POIs).…”

Section: Performance Metricsmentioning

confidence: 99%

Attentive Geo-Social Group Recommendation

Yu,

Fan,

Jiang

et al. 2019

Preprint

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Social activities play an important role in people's daily life since they interact. For recommendations based on social activities, it is vital to have not only the activity information but also individuals' social relations. Thanks to the geo-social networks and widespread use of location-aware mobile devices, massive geo-social data is now readily available for exploitation by the recommendation system.In this paper, a novel group recommendation method, called attentive geo-social group recommendation, is proposed to recommend the target user with both activity locations and a group of users that may join the activities. We present an attention mechanism to model the influence of the target user uT in candidate user groups that satisfy the social constraints. It helps to retrieve the optimal user group and activity topic candidates, as well as explains the group decision-making process. Once the user group and topics are retrieved, a novel efficient spatial query algorithm SPA-DF is employed to determine the activity location under the constraints of the given user group and activity topic candidates.The proposed method is evaluated in real-world datasets and the experimental results show that the proposed model significantly outperforms baseline methods.

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A Generative Model Approach for Geo-Social Group Recommendation

Cited by 19 publications

References 27 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

Behavior‐based POI recommendation for small groups in location‐based social networks

Attentive Geo-Social Group Recommendation

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