Real-time social recommendation based on graph embedding and temporal context

Liu, Peng; Zhang, Lemei; Gulla, Jon Atle

doi:10.1016/j.ijhcs.2018.02.008

Cited by 29 publications

(25 citation statements)

References 31 publications

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“…At the same time, for the whole trajectory, the length proportion of the anomaly trajectory segment TS 3 in trajectory T is greater than the threshold value t of the anomaly length proportion, so trajectory T is an anomaly trajectory. insert sequence in FM neighborhood into C; (11) for each FM 0 2 N e (FM) do (12) if N e (FM 0 ) ø MinPts then (13) Insert sequence in FM 0 neighborhood that is not included in other clusters into C; (14) end for (15) Cl C; /*insert C into Cl*/ (16) end for…”

Section: á á á S Nnmentioning

confidence: 99%

“…Chen et al 10 used auto-encoder fusing a variety of different features to get more semantical and discriminative context representation in the latent space, so as to analyze the taxi destination. Liu et al 13 used embedding method for real-time personalized search and similar product list recommendation. Dong et al 14 proposed a novel Auto-encoder Regularized deep neural Network (ARNet) and a trip encoding framework trip2vec to learn drivers' driving styles directly from GPS records, by combining supervised and unsupervised feature learning in a unified architecture.…”

Section: Embeddingmentioning

confidence: 99%

“…In this subsection, we choose some subsets from synthetic trajectory data set to if ano i \ r then (07) mark TS i as anomaly trajectory segment; (08) SA TS i ; (09) end for (10) end for (11) for each TS 2 SA do (12) if len(TS)/len(T) . t then (13) mark T as anomaly trajectory; (14) TA T; (15) end for discuss how parameters affects the anomaly detection results.…”

Section: Algorithm Parameters Analysismentioning

confidence: 99%

“…Recall represents the proportion of positive cases in the sample to be correctly predicted. The two criterion are given as equations (13) and 14Precision = TP TP + FP ð13Þ…”

Section: Algorithm Parameters Analysismentioning

confidence: 99%

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Unsupervised learning trajectory anomaly detection algorithm based on deep representation

Wang

Yuan

Pei

et al. 2020

International Journal of Distributed Sensor Networks

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Without ground-truth data, trajectory anomaly detection is a hard work and the result lacks of interpretability. Moreover, in most current methods, trajectories are represented by geometric features or their low-dimensional linear combination, and some hidden features and high-dimensional combined features cannot be found efficiently. Meanwhile, traditional methods still cannot get rid of the limitation of space attributes. Therefore, a novel trajectory anomaly detection algorithm is present in this article. Unsupervised learning mechanism is used to overcome nonground-truth problem and deep representation method is used to represent trajectories in a comprehensive way. First, each trajectory is partitioned into segments according to its open angles, then the shallow features at each point of a segment are extracted and. In this way, each segment is represented as a feature sequence. Second, shallow features are integrated into auto-encoder-based deep feature fusion model, and the fusion feature sequences can be extracted. Third, these fused feature sequences are grouped into different clusters using a unsupervised clustering algorithm, and then segments which quite differ from others are detected as anomalies. Finally, comprehensive experiments are conducted on both synthetic and real data sets, which demonstrate the efficiency of our work.

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Section: á á á S Nnmentioning

confidence: 99%

Section: Embeddingmentioning

confidence: 99%

Section: Algorithm Parameters Analysismentioning

confidence: 99%

“…Recall represents the proportion of positive cases in the sample to be correctly predicted. The two criterion are given as equations (13) and 14Precision = TP TP + FP ð13Þ…”

Section: Algorithm Parameters Analysismentioning

confidence: 99%

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Unsupervised learning trajectory anomaly detection algorithm based on deep representation

Wang

Yuan

Pei

et al. 2020

International Journal of Distributed Sensor Networks

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“…Due to the recent development of social networks, the methods that leverage the social network information to improve traditional recommender systems have been widely studied [20,[28][29][30][31][34][35][36][37][38]. For example, to consider the social network information for making recommendations, Ma et al [20] proposed a fused matrix factorization framework to model the user's social influence and rating matrix simultaneously.…”

Section: Social Recommendationmentioning

confidence: 99%

Learning to Make Document Context-Aware Recommendation with Joint Convolutional Matrix Factorization

Guo

Jiang

et al. 2020

Complexity

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Context-aware recommendation (CR) is the task of recommending relevant items by exploring the context information in online systems to alleviate the data sparsity issue of the user-item data. Prior methods mainly studied CR by document-based modeling approaches, that is, making recommendations by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they cannot effectively utilize contextual information of the documents, which results in a shallow understanding of the documents. Recent works argued that the understanding of document context can be improved by the convolutional neural network (CNN) and proposed the convolutional matrix factorization (ConvMF) to leverage the contextual information of documents to enhance the rating prediction accuracy. However, ConvMF only models the document content context from an item view and assumes users are independent and identically distributed (i.i.d). But in reality, as we often turn to our friends for recommendations, the social relationship and social reviews are two important factors that can change our mind most. Moreover, users are more inclined to interact (buy or click) with the items that they have bought (or clicked). The relationships among items are also important factors that can impact the user’s final decision. Based on the above observations, in this work, we target CR and propose a joint convolutional matrix factorization (JCMF) method to tackle the encountered challenges, which jointly considers the item’s reviews, item’s relationships, user’s social influence, and user’s reviews in a unified framework. More specifically, to explore items’ relationships, we introduce a predefined item relation network into ConvMF by a shared item latent factor and propose a method called convolutional matrix factorization with item relations (CMF-I). To consider user’s social influence, we further integrate the user’s social network into CMF-I by sharing the user latent factor between user’s social network and user-item rating matrix, which can be treated as a regularization term to constrain the recommendation process. Finally, to model the document contextual information of user’s reviews, we exploit another CNN to learn user’s content representations and achieve our final model JCMF. We conduct extensive experiments on the real-world dataset from Yelp. The experimental results demonstrate the superiority of JCMF compared to several state-of-the-art methods in terms of root mean squared error (RMSE) and mean average error (MAE).

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