A Tag-Based Integrated Diffusion Model for Personalized Location Recommendation

Zheng, Yaolin; Wang, Yulong; Zhang, Lei; Wang, Jingyu; Qi, Qi

doi:10.1007/978-3-319-70139-4_33

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Our contribution enhances the current research on the tag-based recommendation [16], [17]. Application of tag-based recommendation have been exploited in various domains from personalized social media services [18], e-learning environments [19], personalized location recommendation [20], image search [21], personalized news recommendation [22], personalized music recommendation [23], [24], and many others. A fourth category is a hybrid approach that combines two or more of the previously mentioned categories [25], [26].…”

Section: Introductionmentioning

confidence: 88%

Genres and Actors/Actresses as Interpolated Tags for Improving Movie Recommender Systems

Duong-Trung¹,

Nguyen²,

Ha³

et al. 2020

IJACSA

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A movie recommender system has been proven to be a convincing implement on carrying out comprehensive and complicated recommendation which helps users find appropriate movies conveniently. It follows a mechanism that a user can be accurately recommended movies based on other similar interests, e.g. collaborative filtering, and the movies themselves, e.g. contentbased filtering. Therefore, the systems should come with predetermined information either by users or by movies. One interesting research question should be asked: "what if this information is missing or not manually manipulated?" The problem has not been addressed in the literature, especially for the 100K and 1M variations of the MovieLens datasets. This paper exploits the movie recommender system based on movies' genres and actors/actresses themselves as the input tags or tag interpolation. We apply tag-based filtering and collaborative filtering that can effectively predict a list of movies that is similar to the movie that a user has been watched. Due to not depending on users' profiles, our approach has eliminated the effect of the coldstart problem. The experiment results obtained on MovieLens datasets indicate that the proposed model may contribute adequate performance regarding efficiency and reliability, and thus provide better-personalized movie recommendations. A movie recommender system has been deployed to demonstrate our work. The collected datasets have been published on our Github repository to encourage further reproducibility and improvement.

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Section: Introductionmentioning

confidence: 88%

Genres and Actors/Actresses as Interpolated Tags for Improving Movie Recommender Systems

Duong-Trung¹,

Nguyen²,

Ha³

et al. 2020

IJACSA

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show abstract

“…We design several complicated services for vehicular users which contains multiple tasks deployed by in Docker. For example, the location sight recognition service for vehicle drivers includes image pre-process, image segment, object detection [52], feature selection [53], feature vector process [54], image classification [63], image auto-annotation, voice translation, image-voice synthesizing [64] and recommendation based on location [65]. We generate the dataset according to these services including group of tasks for each service.…”

Section: A Simulation Scenariosmentioning

confidence: 99%

Vehicular Edge Computing via Deep Reinforcement Learning

Qi,

2019

Preprint

Self Cite

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The smart vehicles construct Vehicle of Internet which can execute various intelligent services. Although the computation capability of the vehicle is limited, multi-type of edge computing nodes provide heterogeneous resources for vehicular services. When offloading the complicated service to the vehicular edge computing node, the decision should consider numerous factors. The offloading decision work mostly formulate the decision to a resource scheduling problem with single or multiple objective function and some constraints, and explore customized heuristics algorithms. However, offloading multiple data dependency tasks in a service is a difficult decision, as an optimal solution must understand the resource requirement, the access network, the user mobility, and importantly the data dependency. Inspired by recent advances in machine learning, we propose a knowledge driven (KD) service offloading decision framework for Vehicle of Internet, which provides the optimal policy directly from the environment. We formulate the offloading decision of multi-task in a service as a long-term planning problem, and explores the recent deep reinforcement learning to obtain the optimal solution. It considers the future data dependency of the following tasks when making decision for a current task from the learned offloading knowledge. Moreover, the framework supports the pre-training at the powerful edge computing node and continually online learning when the vehicular service is executed, so that it can adapt the environment changes and learns policy that are sensible in hindsight. The simulation results show that KD service offloading decision converges quickly, adapts to different conditions, and outperforms the greedy offloading decision algorithm. Index Termsvehicle of internet, service offloading decision, multi-task, knowledge driven, deep reinforcement learning I. INTRODUCTIONAlong with the development of information technology, the vehicles with advanced computation and communication capabilities spring up. With the promising electronic control units (ECUs), the vehicles construct as Vehicle of Internet which can execute multimedia entertainment services, navigation, remote vehicle diagnosis and even AI based automatic driven services. Moreover, unmanned aerial vehicle (UAV) can be used as patrol robot which travels in the area of industry field. These applications bring new experience for the vehicular users. However, the limited capability of the ECU and the unstable of the vehicular network still impacts the quality of the service. Fortunately, edge computing is combined to traditional vehicular network to strengthen its service capability. The edge computing environment providing for vehicles including computation nodes located with Base Station (BS) and many kinds of computational hot points of WLAN (Wireless Local Area Networks) access technology, such as intelligent roadside unit, cloudlet in buildings and so on. All of these computational nodes and the neighboring vehicles can provide service execution for th...

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A Bayesian Personalized Ranking Algorithm Based on Tag Preference

Jiang

Sun

et al. 2018

2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)

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A Tag-Based Integrated Diffusion Model for Personalized Location Recommendation

Cited by 5 publications

References 22 publications

Genres and Actors/Actresses as Interpolated Tags for Improving Movie Recommender Systems

Genres and Actors/Actresses as Interpolated Tags for Improving Movie Recommender Systems

Vehicular Edge Computing via Deep Reinforcement Learning

A Bayesian Personalized Ranking Algorithm Based on Tag Preference

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