Joint Geographical and Temporal Modeling Based on Matrix Factorization for Point-of-Interest Recommendation

Rahmani, Hossein A.; Aliannejadi, Mohammad; Baratchi, Mitra; Crestani, Fábio

doi:10.1007/978-3-030-45439-5_14

Cited by 49 publications

(31 citation statements)

References 33 publications

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“…Moreover, users' behaviors show that in addition to being influenced by the opinions of their friends, they usually check in around several centers which shows their visited centers depend on the different temporal states. Inspired by Rahmani et al [35], we make use of two MF models named static and dynamic MF. In the static MF model, the same traditional model [10] is considered for modeling the static user behavior.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…This tries to examine the user's behavior from different aspects. Rahmani et al [35] proposed a joint approach based on the MF that considers both geographical and temporal information jointly and is called STACP. STACP models users' activity centers based on temporal information, and they show users' centers depend on the different temporal states.…”

Section: Related Workmentioning

confidence: 99%

“…We evaluate the performance and accuracy of our proposed model and the baseline methods using three common evaluation metrics, namely, Precision at N (Precision@N), Recall at N (Recall@N), and Normalized Discounted Cumulative Gain at N (nDCG@N) with N ∈ {10, 20} -as applied in previous literature [35,47,52]. The precision is a measure of recommendation relevancy, while recall is a measure of how many truly relevant recommendations are returned.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

Seyedhoseinzadeh¹,

Rahmani²,

Afsharchi³

et al. 2022

Preprint

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Recommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such as data sparsity, limiting their effectiveness. In this paper, we address this problem by incorporating social, geographical, and temporal information into the Matrix Factorization (MF) technique. To this end, we model social influence based on two factors: similarities between users in terms of common check-ins and the friendships between them. We introduce two levels of friendship based on explicit friendship networks and high check-in overlap between users. We base our friendship algorithm on users' geographical activity centers. The results show that our proposed model outperforms the state-of-the-art on two real-world datasets. More specifically, our ablation study shows that the social model improves the performance of our proposed POI recommendation system by 31% and 14% on the Gowalla and Yelp datasets in terms of Precision@10, respectively.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

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Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

Seyedhoseinzadeh¹,

Rahmani²,

Afsharchi³

et al. 2022

Preprint

Self Cite

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“…Their model considers two neural network components; one that recommends POI and another component judges if it is a true recommendation or not and helps in optimizing the proposed recommendation. The STACP (Spatio-Temporal Activity Center POI) novel recommendation model proposed by [42] considers the effect of spatial and temporal characteristics of a user jointly. This model trains the matrix factorization model in a static and temporal manner and forms spatio-temporal activity centers for users.…”

Section: Memory Based Collaborative Filteringmentioning

confidence: 99%

Locus recommendation using probabilistic matrix factorization techniques

Behl

Kashyap²

2021

ing. Solidar

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Introduction: The present paper is the outcome of the research “Locus Recommendation using Probabilistic Matrix Factorization Techniques” carried out in Manav Rachna International Institute of Research and Studies, India in the year 2019-20. Methodology: Matrix factorization is a model-based collaborative technique for recommending new items to the users. Results: Experimental results on two real-world LBSNs showed that PFM consistently outperforms PMF. This is because the technique is based on gamma distribution to the model user and item matrix. Using gamma distribution is reasonable for check-in frequencies which are all positive in real datasets. However, PMF is based on Gaussian distribution that can allow negative frequency values as well. Conclusion: The motive of the work is to identify the best technique for recommending locations with the highest accuracy and allow users to choose from a plethora of available locations; the best and interesting location based on the individual’s profile. Originality: A rigorous analysis of Probabilistic Matrix Factorization techniques has been performed on popular LBSNs and the best technique for location recommendation has been identified by comparing the accuracy viz RMSE, Precision@N, Recall@N, F1@N of different models. Limitations: User’s contextual information like demographics, social and geographical preferences have not been considered while evaluating the efficiency of probabilistic matrix factorization techniques for POI Recommendations.

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“…Markov Chain based stochastic models have been explored extensively in this regard [1,2,3,4,5,6,7,8,9]. Due to the success of Matrix Factorization (MF [10]) based methods for recommendation systems in other domains, MF methods [11,12,13,2,14,15,16,17] have also been studied for better POI recommendation modeling. To achieve better performance than vanilla MF methods, Bayesian Personalized Ranking (BPR [18]) methods have been employed [19,20,21,22,23,24,25,8].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

Islam¹,

Mohammad²,

Das³

et al. 2020

Preprint

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Location-based Social Networks (LBSNs) enable users to socialize with friends and acquaintances by sharing their check-ins, opinions, photos, and reviews. Huge volume of data generated from LBSNs opens up a new avenue of research that gives birth to a new sub-field of recommendation systems, known as Point-of-Interest (POI) recommendation. A POI recommendation technique essentially exploits users' historical check-ins and other multi-modal information such as POI attributes and friendship network, to recommend the next set of POIs suitable for a user. A plethora of earlier works focused on traditional machine learning techniques by using hand-crafted features from the dataset. With the recent surge of deep learning research, we have witnessed a large variety of POI recommendation works utilizing different deep learning paradigms. These techniques largely vary in problem formulations, proposed techniques, used datasets and features, etc. To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works. Our work categorizes and critically analyzes the recent POI recommendation works based on different deep learning paradigms and other relevant features. This review can be considered a cookbook for researchers or practitioners working in the area of POI recommendation.

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Joint Geographical and Temporal Modeling Based on Matrix Factorization for Point-of-Interest Recommendation

Cited by 49 publications

References 33 publications

Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

Locus recommendation using probabilistic matrix factorization techniques

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

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