Modeling Spatio-Temporal App Usage for a Large User Population

Wang, Huandong; Li, Yong; Zeng, Sihan; Wang, Gang; Zhang, Pengyu; Hui, Pan; Jin, Depeng

doi:10.1145/3314414

Cited by 27 publications

(10 citation statements)

References 61 publications

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“…However, utilizing smartphone usage for sequential POI recommendations is not addressed in the past literature. The papers most similar to our work are by [47] and [44]. Wang et al [47] utilizes a Dirichlet process to determine the next user location, but it completely disregards the user's privacy, i.e., requires precise geo-coordinates, and Tu et al [44] is limited to the cold-start recommendation.…”

Section: Introductionmentioning

confidence: 90%

“…Other approaches [16,34] include the continuous-time contexts for modeling the time-evolving preferences of a user. However, prior research has shown that users exhibit revisitation patterns on their web activities [2,25] and these revisitation patterns resonate with the mobility preferences of a user [6,47]. As per the permissions given by a user to an app, leading corporations, such as Foursquare, utilize smartphone activities to better understand the likes and dislikes of a user to give better POI recommendations [9].…”

Section: Related Workmentioning

confidence: 99%

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Modeling Spatial Trajectories Using Coarse-Grained Smartphone Logs

Gupta

Bedathur

2023

IEEE Trans. Big Data

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Current approaches for points-of-interest (POI) recommendation learn the preferences of a user via the standard spatial features such as the POI coordinates, the social network, etc. These models ignore a crucial aspect of spatial mobility -every user carries their smartphones wherever they go. In addition, with growing privacy concerns, users refrain from sharing their exact geographical coordinates and their social media activity. In this paper, we present REVAMP, a sequential POI recommendation approach that utilizes the user activity on smartphone applications (or apps) to identify their mobility preferences. This work aligns with the recent psychological studies of online urban users, which show that their spatial mobility behavior is largely influenced by the activity of their smartphone apps. In addition, our proposal of coarse-grained smartphone data refers to data logs collected in a privacy-conscious manner, i.e., consisting only of (a) category of the smartphone app and (b) category of check-in location. Thus, REVAMP is not privy to precise geo-coordinates, social networks, or the specific application being accessed. Buoyed by the efficacy of self-attention models, we learn the POI preferences of a user using two forms of positional encodings -absolute and relative -with each extracted from the inter-check-in dynamics in the check-in sequence of a user. Extensive experiments across two large-scale datasets from China show the predictive prowess of REVAMP and its ability to predict app-and POI categories.

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Section: Introductionmentioning

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Modeling Spatial Trajectories Using Coarse-Grained Smartphone Logs

Gupta

Bedathur

2023

IEEE Trans. Big Data

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“…Recent research utilizes Spatio-temporal information such as location, time, and session data to predict the next app that an individual user will use next [3]. Another study utilized a Bayesian network on the Spatio-temporal app usage data for predicting the next app [10,20]. At the same time, some works aimed to use similar information to predict app usage in different scenarios (e.g., in different places [7,23] or apps used to complete various search tasks [1]).…”

Section: Introductionmentioning

confidence: 99%

CoSEM: Contextual and Semantic Embedding for App Usage Prediction

Khaokaew

Rahaman

White

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

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App usage prediction is important for smartphone system optimization to enhance user experience. Existing modeling approaches utilize historical app usage logs along with a wide range of semantic information to predict the app usage; however, they are only effective in certain scenarios and cannot be generalized across different situations. This paper address this problem by developing a model called Contextual and Semantic Embedding model for App Usage Prediction (CoSEM) for app usage prediction that leverages integration of 1) semantic information embedding and 2) contextual information embedding based on historical app usage of individuals. Extensive experiments show that the combination of semantic information and history app usage information enables our model to outperform the baselines on three real-world datasets, achieving an MRR score over 0.55,0.57,0.86 and Hit rate scores of more than 0.71, 0.75, and 0.95, respectively. CCS CONCEPTS• Information systems → Decision support systems; • Humancentered computing → Ubiquitous and mobile computing.

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“…To improve the accuracy of recommendation services on mobile devices, temporal recommendation techniques [20,38,40] have been proposed by leveraging temporal user behavior data generated on mobile devices [7,29,41], such as item clicks, dwell time, and revisitation frequency.…”

Section: Introductionmentioning

confidence: 99%

$C^3DRec$: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era

Han¹,

Ma²

2021

Preprint

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Mobile devices enable users to retrieve information at any time and any place. Considering the occasional requirements and fragmentation usage pattern of mobile users, temporal recommendation techniques are proposed to improve the efficiency of information retrieval on mobile devices by means of accurately recommending items via learning temporal interests with short-term user interaction behaviors. However, the enforcement of privacy-preserving laws and regulations, such as the General Data Protection Regulation (GDPR), may overshadow the successful practice of temporal recommendation. The reason is that state-of-the-art recommendation systems require to gather and process the user data in centralized servers but the interaction behaviors data used for temporal recommendation are usually non-transactional data that are not allowed to gather without the explicit permission of users according to GDPR. As a result, if users do not permit services to gather their interaction behaviors data, the temporal recommendation fails to work. To realize the temporal recommendation in the post-GDPR era, this paper proposes 𝐶 3 𝐷𝑅𝑒𝑐, a cloud-client cooperative deep learning framework of mining interaction behaviors for recommendation while preserving user privacy. 𝐶 3 𝐷𝑅𝑒𝑐 constructs a global recommendation model on centralized servers using data collected before GDPR and fine-tunes the model directly on individual local devices using data collected after GDPR. We design two modes to accomplish the recommendation, i.e., pull mode where candidate items are pulled down onto the devices and fed into the local model to get recommended items, and push mode where the output of the local model is pushed onto the server and combined with candidate items to get recommended ones. Evaluation results show that 𝐶 3 𝐷𝑅𝑒𝑐 achieves comparable recommendation accuracy to the centralized approaches, with minimal privacy concern.

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Modeling Spatio-Temporal App Usage for a Large User Population

Cited by 27 publications

References 61 publications

Modeling Spatial Trajectories Using Coarse-Grained Smartphone Logs

Modeling Spatial Trajectories Using Coarse-Grained Smartphone Logs

CoSEM: Contextual and Semantic Embedding for App Usage Prediction

$C^3DRec$: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era

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