Geo topic model

Kurashima, Toshio; Iwata, Tomoharu; Hoshide, Takahide; Takaya, Noriko; Fujimura, Kikuo

doi:10.1145/2433396.2433444

Cited by 122 publications

(4 citation statements)

References 22 publications

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“…For example, as users are likely to visit venues nearby, such spatial effects were modelled as exponential relationships, probability distribution, or power law relationships (Yang et al 2008, Ye et al 2011, Kurashima et al 2013, Liu and Seah 2015. Some works also modelled the periodicity of check-ins (Yuan et al 2013, Gao et al 2013, social relationships of users (Cheng et al 2012, Gao et al 2012) and POI tips (Yang et al 2013).…”

Section: Background and Related Workmentioning

confidence: 99%

RegNet: a neural network model for predicting regional desirability with VGI data

Shi

Liu

et al. 2020

International Journal of Geographical Information Science

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Volunteered geographic information can be used to predict regional desirability. A common challenge regarding previous works is that intuitive empirical models, which are inaccurate and bring in perceptual bias, are traditionally used to predict regional desirability. This results from the fact that the hidden interactions between user online check-ins and regional desirability haven't been revealed and clearly modelled yet. To solve the problem, a novel neural network model 'RegNet' is proposed. The user check-in history is input into a neural network encoder structure firstly for redundancy reduction and feature learning. The encoded representation is then fed into a hidden-layer structure and the regional desirability is predicted. The proposed RegNet is data-driven and can adaptively model the unknown mappings from input to output, without presumed bias and prior knowledge. We conduct experiments with real-world datasets and demonstrate RegNet outperforms state-of-the-art methods in terms of ranking quality and prediction accuracy of rating. Additionally, we also examine how the structure of encoder affects RegNet performance and suggest on choosing proper sizes of encoded representation. This work demonstrates the effectiveness of data-driven methods in modelling the hidden unknown relationships and achieving a better performance over traditional empirical methods.

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Section: Background and Related Workmentioning

confidence: 99%

RegNet: a neural network model for predicting regional desirability with VGI data

Shi

Liu

et al. 2020

International Journal of Geographical Information Science

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“…Cong et al (2013) applied temporal preference to improve the efficiency of POI prediction. Kurashima et al (2013) presented a topic model to sample POI based on topics and the distance to visited POIs of a target user. Some recent works explored geographic patterns to better understand the geographic influence on the prediction results.…”

Section: Traditional Point-of-interest Recommendationmentioning

confidence: 99%

Pattern-based dual learning for point-of-interest (POI) recommendation

Thaipisutikul

Chen

2020

IMDS

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PurposeTourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.Design/methodology/approachThe authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.FindingsThe authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.Originality/valueThis study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.

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“…T. Iwata et al [16] took time into consideration and proposed a topic model for tracking time-varying consumer purchasing behavior. To recommend locations to be visited, T. Kurashima et al [17] proposed a Geo topic model to analyze the location log data of multiple users. C. Chemudugunta et al [18] suggested that a model can be used for information retrieval by matching documents both at a general topic level and at a specific level, and C. Lin et al [19] proposed the Joint Sentiment Topic (JST) model, which can be used to analyze the sentiment tendency of documents.…”

Section: Topic Distillation Modelmentioning

confidence: 99%

Improved LDA Model for Credibility Evaluation of Online Product Reviews

Wang

Zhang

Huang

et al. 2019

IEICE Trans. Inf. & Syst.

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When individuals make a purchase from online sources, they may lack first-hand knowledge of the product. In such cases, they will judge the quality of the item by the reviews other consumers have posted. Therefore, it is significant to determine whether comments about a product are credible. Most often, conventional research on comment credibility has employed supervised machine learning methods, which have the disadvantage of needing large quantities of training data. This paper proposes an unsupervised method for judging comment credibility based on the Biterm Sentiment Latent Dirichlet Allocation (BS-LDA) model. Using this approach, first we derived some distributions and calculated each comment's credibility score via them. A comment's credibility was judged based on whether it achieved a threshold score. Our experimental results using comments from Amazon.com demonstrated that the overall performance of our approach can play an important role in determining the credibility of comments in some situation.

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Geo topic model

Cited by 122 publications

References 22 publications

RegNet: a neural network model for predicting regional desirability with VGI data

RegNet: a neural network model for predicting regional desirability with VGI data

Pattern-based dual learning for point-of-interest (POI) recommendation

Improved LDA Model for Credibility Evaluation of Online Product Reviews

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