Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation

Ren, Xiaomin; Song, Meina; Haihong, E; Song, Junde

doi:10.1016/j.neucom.2017.02.005

Cited by 114 publications

(33 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the best of our knowledge, many POI recommender systems integrate the geographical information [4][5][6]12,31], temporal information [2,[32][33][34][35], social information [1,4,36] or other POI characteristic information (reviews, categories, labels, etc.) [37][38][39][40] into traditional recommendation algorithms.…”

Section: Collaborative Filtering Based Methodsmentioning

confidence: 99%

An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

Liu

Wang

et al. 2019

IJGI

View full text Add to dashboard Cite

Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs). Some existing methods are mostly based on collaborative filtering (CF), Markov chain (MC) and recurrent neural network (RNN). However, it is difficult to capture dynamic user's preferences using CF based methods. MC based methods suffer from strong independence assumptions. RNN based methods are still in the early stage of incorporating spatiotemporal context information, and the user's main behavioral intention in the current sequence is not emphasized. To solve these problems, we proposed an attention-based spatiotemporal gated recurrent unit (ATST-GRU) network model for POI recommendation in this paper. We first designed a novel variant of GRU, which acquired the user's sequential preference and spatiotemporal preference by feeding the continuous geographical distance and time interval information into the GRU network in each time step. Then, we integrated an attention model into our network, which is a personalized process and can capture the user's main behavioral intention in the user's check-in history. Moreover, we conducted an extensive performance evaluation on two real-world datasets: Foursquare and Gowalla. The experimental results demonstrated that the proposed ATST-GRU network outperforms the existing state-of-the-art POI recommendation methods significantly regarding two commonly-used evaluation metrics.

show abstract

Section: Collaborative Filtering Based Methodsmentioning

confidence: 99%

An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

Liu

Wang

et al. 2019

IJGI

View full text Add to dashboard Cite

show abstract

“…In addition, the mobile personalized recommendation system can recommend different promotion items under different contexts of shopping venue [23], and can also improve the interest of mobile commerce users and sales performance according to context recommendation [24]. Ren [25] found that context in users' interest mining had a great influence on network users' behaviors, and users were highly dependent on the context when making rational behavior decisions, which produced contextual effects when users adopted mobile personalized recommendation services.…”

Section: Research On Mobile Personalized Recommendation Service Consimentioning

confidence: 99%

Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce

2020

View full text Add to dashboard Cite

A mobile personalized recommendation service satisfies the needs of users and stimulates them to continue to adopt mobile commerce applications. Therefore, how to precisely provide mobile personalized recommendation service is very important for the sustainable development of mobile commerce. However, privacy concerns regarding mobile commerce affect users’ consumption intentions, and also reduce the quality of mobile personalized recommendation services. In order to address this issue and the existing recommendation method problem in the mobile personalized recommendation service, this paper introduces six dimensions of privacy concerns and the relevant contextual information to propose a novel mobile personalized recommendation service based on privacy concerns and context analysis. First, this paper puts forward an intensity measurement method to measure the factors that influence privacy concerns, and then realizes a user-based collaborative filtering recommendation integrated with the intensity of privacy concerns. Second, a context similarity algorithm based on a context ontology-tree is proposed, after which this study realizes a user-based collaborative filtering recommendation integrated with context similarity. Finally, the research produces a hybrid collaborative filtering recommendation integrated with privacy concerns and context information. After experimental verification, the results show that this model can effectively solve the problems of data sparseness and cold starts. More importantly, it can reduce the influence of users’ privacy concerns on the adoption of mobile personalized recommendation services, and promote the sustainable development of mobile commerce.

show abstract

“…[37,50,70,71,75,77,78,90,92,100,[103][104][105][106][107]109,111,128,130,133,136,137,160,161,164,165] f-measure To measure the harmonic mean of recall and precision…”

Section: Precisionmentioning

confidence: 99%

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

et al. 2017

View full text Add to dashboard Cite

Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users' recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS) are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and classified literature, considering the domain of the application models, filters, extraction and evaluation approaches. Second, viewpoints are presented relating to the extraction of literature with analysis on the merit and demerit of each, and the evolving processes between them. Finally, the outstanding challenges and opportunities for future research directions are highlighted.

show abstract

Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation

Cited by 114 publications

References 16 publications

An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

Contact Info

Product

Resources

About