Personalized location recommendation using mobile phone usage information

Shi, Hongyu; Chen, Ling; Xu, Zhenxing; Lyu, Dandan

doi:10.1007/s10489-019-01477-6

Cited by 21 publications

(8 citation statements)

References 41 publications

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“…With the in-depth development of education informatization, online education is highly respected by the majority of learners [1]. Online education combines traditional teaching mode with Internet technology, overcoming the drawbacks of traditional education restricted by time, space, and environment and enabling the sharing and reuse of highquality educational resources on a larger scale [2].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Personalized Recommendation Model of High‐Quality Education Resources for College Students Based on Data Mining

Fang

Qiu-yun

2021

Complexity

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With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Personalized Recommendation Model of High‐Quality Education Resources for College Students Based on Data Mining

Fang

Qiu-yun

2021

Complexity

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“…The protocol uses a fixed periodic music tone listening/sleep scheduling mechanism to reduce energy consumption and uses the CSMA/CA access mechanism to compete for channels during the active period. Under the premise of ensuring low power consumption, Shi et al [13] minimized delay and increase throughput. Aiming at the problem that the relatively fixed scheduling period cannot adapt to the adaptive changes of network-assisted music tones, a MAC protocol that adaptively adjusts the duty cycle is proposed: T-MAC (timeout MAC) protocol, which dynamically adjusts the scheduling.…”

Section: Related Workmentioning

confidence: 99%

Wireless Music Playing Buzzer Sensor-Assisted Music Tone Adaptive Control

Shen

2022

Journal of Sensors

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Aiming at the problem of adaptive change of auxiliary music tones, this paper proposes a MAC protocol with a common music tone listening/sleeping type based on a wireless music buzzer sensor. First of all, the new MAC protocol adopts network-wide synchronization, and all sensor nodes in the entire network use the same scheduling table, so that the entire network nodes enter the music tone listening period and the sleep period at the same time. Secondly, the node adaptively adjusts the duty cycle of the node according to the number of data packets in the sending queue, increases the node’s music tone listening time, reduces the end-to-end delay of data packets, and improves the throughput of the network. Then, the experiment adopts a new backoff strategy to adjust the contention window according to the backoff times and collision times of data packets sent by nodes in the last five working cycles, increase the backoff time of sending data packets under high network load, and reduce the appearance of data packets. We build four simulation experiments on the NS2 simulation platform: unassisted music tone adaptive network, single auxiliary music tone adaptive network, auxiliary music tone adaptive convergence network, and random deployment network, which will be based on the auxiliary music tone adaptive MAC protocol, and IEEE802.11 protocol and SMAC protocol are run in four simulation experiments, respectively, and the performance of the three protocols is analyzed according to the tracking files in the simulation experiment. The analysis results show that the simulation wireless sounding buzzer sensor network is adaptive to different auxiliary music tones and different topologies.

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“…In the following experiments, according to visiting time, we split the dataset into the training set (80%) and the test set (20%). Then, use evaluation metrics, i.e., MAP@n and AP@n, were adopted to evaluate the recommendation effectiveness by calculated Equation 13and (14), respectively.…”

Section: The Impact Of Topic Numbermentioning

confidence: 99%

A Convolutional Neural Network and Matrix Factorization-Based Travel Location Recommendation Method Using Community-Contributed Geotagged Photos

Ameen

Chen

et al. 2020

IJGI

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Travel location recommendation methods using community-contributed geotagged photos are based on past check-ins. Therefore, these methods cannot effectively work for new travel locations, i.e., they suffer from the travel location cold start problem. In this study, we propose a convolutional neural network and matrix factorization-based travel location recommendation method to address the problem. Specifically, a weighted matrix factorization method is used to obtain the latent factor representations of travel locations. The latent factor representation for a new travel location is estimated from its photos by using a convolutional neural network. Experimental results on a Flickr dataset demonstrate that the proposed method can provide better recommendations than existing methods.

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Personalized location recommendation using mobile phone usage information

Cited by 21 publications

References 41 publications

[Retracted] Personalized Recommendation Model of High‐Quality Education Resources for College Students Based on Data Mining

[Retracted] Personalized Recommendation Model of High‐Quality Education Resources for College Students Based on Data Mining

Wireless Music Playing Buzzer Sensor-Assisted Music Tone Adaptive Control

A Convolutional Neural Network and Matrix Factorization-Based Travel Location Recommendation Method Using Community-Contributed Geotagged Photos

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