HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation

Lu, Ziyu; Li, Hui; Mamoulis, Nikos; Cheung, David W.

doi:10.1137/1.9781611974973.42

Cited by 30 publications

(12 citation statements)

References 21 publications

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“…• HBGG [32] is developed to recommend locations for a group of users. HBGG is a topic model which considers group geographical topic, group mobility regions and social information.…”

Section: B Comparison Methodsmentioning

confidence: 99%

Hybrid Deep Framework for Group Event Recommendation

Zhu

et al. 2020

IEEE Access

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Group recommender systems suggesting items for a group of users have received many attentions recently. Some aggregation-based and model-based group recommendation methods have been proposed. However, the cold-start problem in group recommendation has not been well studied, which limits the application of group recommendation in many important domains, such as recommending offline events for a group of users. In this paper, we propose a new hybrid deep framework to solve cold-start problem of group event recommendation. Our framework incorporates multiple Restricted Boltzmann Machines (RBM) and conditional RBM. The former extracts high latent group preference from user feedback and group feedback. The latter obtains latent event features based on contextual information, such as location and organizer of events. Thus, the hybrid deep framework can utilize user feedback and contextual information of events to overcome cold-start problem. We conduct exhaustive experiments on two real-world datasets and the results show that our proposed framework outperforms the baseline group recommendation methods and alleviates the cold-start problem of group event recommendation effectively. INDEX TERMS Group recommendation, hybrid recommendation, cold-start problem, restricted Boltzmann machines, deep belief networks.

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“…• HBGG [32] is developed to recommend locations for a group of users. HBGG is a topic model which considers group geographical topic, group mobility regions and social information.…”

Section: B Comparison Methodsmentioning

confidence: 99%

Hybrid Deep Framework for Group Event Recommendation

Zhu

et al. 2020

IEEE Access

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“…For POI group recommendation, we adopt a method commonly used in academic simulation proposed by Lu et al [27] to generate preferred POIs for a group. The specific methods are as follows: If user u 1 , u 2 , u 3 in less than an hour time to visit POI l, and the three users are friends, then the three-person group u 1 , u 2 ,u 3 preferred to the location l.…”

Section: Data Setmentioning

confidence: 99%

A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021

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The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

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“…Another strength of MF, making it widely used in recommender systems, is that side information other than existing ratings can easily be integrated into the model to further increase its accuracy. Such information includes social network data Lagun and Agichtein, 2015;Zhao et al, 2016;Xiao et al, 2017), locations of users and items (Lu et al, 2017) and visual appearance (He and McAuley, 2016; proposed Probabilistic Matrix Factorization (PMF) which extends MF to a probabilistic linear model with Gaussian noise. Following PMF, there are many extensions Chen et al, 2013;Zheng et al, 2016;He et al, 2016bHe et al, , 2017 aiming to improve its accuracy.…”

Section: Related Workmentioning

confidence: 99%

LRMM: Learning to Recommend with Missing Modalities

Wang¹,

Niepert²,

Li³

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.

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HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation

Cited by 30 publications

References 21 publications

Hybrid Deep Framework for Group Event Recommendation

Hybrid Deep Framework for Group Event Recommendation

A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

LRMM: Learning to Recommend with Missing Modalities

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