Improving POI Recommendation via Dynamic Tensor Completion

POI recommendation finds significant importance in various real-life applications, especially when meeting with location-based services, e.g., check-ins social networks. In this paper, we propose to solve POI recommendation through a novel model of dynamic tensor, which is among the first triumphs of its kind. In order to carry out timely recommendation, we predict POI by utilizing a completion algorithm based on fast low-rank tensor. Particularly, the dynamic tensor structure is complemented by the fast low-rank tensor completion algorithm so as to achieve prediction with better performance, where the parameter optimization is achieved by a pigeon-inspired heuristic algorithm. In short, our POI recommendation via the dynamic tensor method can take advantage of the intrinsic characteristics of check-ins data due to the multimode features such as current categories, subsequent categories, and temporal information as well as seasons variations are all integrated into the model. Extensive experiment results not only validate the superiority of our proposed method but also imply the application prospect in large-scale and real-time POI recommendation environment.

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General-Purpose Bayesian Tensor Learning With Automatic Rank Determination and Uncertainty Quantification

Frontiers in Artificial Intelligence ◽

10.3389/frai.2021.668353 ◽

2022 ◽

Vol 4 ◽

Author(s):

Kaiqi Zhang ◽

Cole Hawkins ◽

Zheng Zhang

Keyword(s):

Machine Learning ◽

Uncertainty Quantification ◽

Large Scale ◽

Broad Class ◽

Low Rank ◽

Tensor Rank ◽

Tensor Completion ◽

Volume Data ◽

Rank Tensor ◽

Rank Determination

A major challenge in many machine learning tasks is that the model expressive power depends on model size. Low-rank tensor methods are an efficient tool for handling the curse of dimensionality in many large-scale machine learning models. The major challenges in training a tensor learning model include how to process the high-volume data, how to determine the tensor rank automatically, and how to estimate the uncertainty of the results. While existing tensor learning focuses on a specific task, this paper proposes a generic Bayesian framework that can be employed to solve a broad class of tensor learning problems such as tensor completion, tensor regression, and tensorized neural networks. We develop a low-rank tensor prior for automatic rank determination in nonlinear problems. Our method is implemented with both stochastic gradient Hamiltonian Monte Carlo (SGHMC) and Stein Variational Gradient Descent (SVGD). We compare the automatic rank determination and uncertainty quantification of these two solvers. We demonstrate that our proposed method can determine the tensor rank automatically and can quantify the uncertainty of the obtained results. We validate our framework on tensor completion tasks and tensorized neural network training tasks.

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Fast Recursive Low-rank Tensor Learning for Regression

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/257 ◽

2017 ◽

Cited By ~ 1

Author(s):

Ming Hou ◽

Brahim Chaib-draa

Keyword(s):

High Speed ◽

Large Scale ◽

Real Life ◽

Low Rank ◽

Small Scale ◽

Low Rank Approximation ◽

Rank Tensor ◽

Fast Processing ◽

Small Set ◽

Time Required

In this work, we develop a fast sequential low-rank tensor regression framework, namely recursive higher-order partial least squares (RHOPLS). It addresses the great challenges posed by the limited storage space and fast processing time required by dynamic environments when dealing with large-scale high-speed general tensor sequences. Smartly integrating a low-rank modification strategy of Tucker into a PLS-based framework, we efficiently update the regression coefficients by effectively merging the new data into the previous low-rank approximation of the model at a small-scale factor (feature) level instead of the large raw data (observation) level. Unlike batch models, which require accessing the entire data, RHOPLS conducts a blockwise recursive calculation scheme and thus only a small set of factors is needed to be stored. Our approach is orders of magnitude faster than all other methods while maintaining a highly comparable predictability with the cutting-edge batch methods, as verified on challenging real-life tasks.

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Nonconvex Low-Rank Tensor Completion from Noisy Data

Operations Research ◽

10.1287/opre.2021.2106 ◽

2021 ◽

Author(s):

Changxiao Cai ◽

Gen Li ◽

H. Vincent Poor ◽

Yuxin Chen

Keyword(s):

Large Scale ◽

Linear Time ◽

Practical Interest ◽

Optimal Estimation ◽

Low Rank ◽

Estimation Accuracy ◽

Tensor Completion ◽

Minimal Sample ◽

Completion Problem ◽

Rank Tensor

This paper investigates a problem of broad practical interest, namely, the reconstruction of a large-dimensional low-rank tensor from highly incomplete and randomly corrupted observations of its entries. Although a number of papers have been dedicated to this tensor completion problem, prior algorithms either are computationally too expensive for large-scale applications or come with suboptimal statistical performance. Motivated by this, we propose a fast two-stage nonconvex algorithm—a gradient method following a rough initialization—that achieves the best of both worlds: optimal statistical accuracy and computational efficiency. Specifically, the proposed algorithm provably completes the tensor and retrieves all low-rank factors within nearly linear time, while at the same time enjoying near-optimal statistical guarantees (i.e., minimal sample complexity and optimal estimation accuracy). The insights conveyed through our analysis of nonconvex optimization might have implications for a broader family of tensor reconstruction problems beyond tensor completion.

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Low Rank Tensor Completion with Poisson Observations

IEEE Transactions on Pattern Analysis and Machine Intelligence ◽

10.1109/tpami.2021.3059299 ◽

2021 ◽

pp. 1-1

Author(s):

Xiongjun Zhang ◽

Michael Kwok-Po Ng

Keyword(s):

Low Rank ◽

Tensor Completion ◽

Rank Tensor

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Low-rank tensor completion based on non-convex logDet function and Tucker decomposition

Signal Image and Video Processing ◽

10.1007/s11760-020-01845-7 ◽

2021 ◽

Author(s):

Chengfei Shi ◽

Zhengdong Huang ◽

Li Wan ◽

Tifan Xiong

Keyword(s):

Low Rank ◽

Tensor Completion ◽

Tucker Decomposition ◽

Rank Tensor

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Robust low-rank tensor completion via transformed tensor nuclear norm with total variation regularization

Neurocomputing ◽

10.1016/j.neucom.2020.12.110 ◽

2021 ◽

Vol 435 ◽

pp. 197-215

Author(s):

Duo Qiu ◽

Minru Bai ◽

Michael K. Ng ◽

Xiongjun Zhang

Keyword(s):

Total Variation ◽

Nuclear Norm ◽

Low Rank ◽

Total Variation Regularization ◽

Tensor Completion ◽

Rank Tensor ◽

Tensor Nuclear Norm

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Low-rank tensor completion via combined Tucker and Tensor Train for color image recovery

Applied Intelligence ◽

10.1007/s10489-021-02833-1 ◽

2021 ◽

Author(s):

Tianheng Zhang ◽

Jianli Zhao ◽

Qiuxia Sun ◽

Bin Zhang ◽

Jianjian Chen ◽

...

Keyword(s):

Color Image ◽

Low Rank ◽

Image Recovery ◽

Tensor Completion ◽

Rank Tensor

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Modelling of Bi-Directional Spatio-Temporal Dependence and Users’ Dynamic Preferences for Missing POI Check-In Identification

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33015458 ◽

2019 ◽

Vol 33 ◽

pp. 5458-5465 ◽

Cited By ~ 2

Author(s):

Dongbo Xi ◽

Fuzhen Zhuang ◽

Yanchi Liu ◽

Jingjing Gu ◽

Hui Xiong ◽

...

Keyword(s):

Large Scale ◽

Real Life ◽

Location Prediction ◽

Temporal Dependence ◽

Great Opportunity ◽

Mobility Data ◽

Proposed Model ◽

Poi Recommendation ◽

Spatio Temporal ◽

Dynamic Preferences

Human mobility data accumulated from Point-of-Interest (POI) check-ins provides great opportunity for user behavior understanding. However, data quality issues (e.g., geolocation information missing, unreal check-ins, data sparsity) in real-life mobility data limit the effectiveness of existing POIoriented studies, e.g., POI recommendation and location prediction, when applied to real applications. To this end, in this paper, we develop a model, named Bi-STDDP, which can integrate bi-directional spatio-temporal dependence and users’ dynamic preferences, to identify the missing POI check-in where a user has visited at a specific time. Specifically, we first utilize bi-directional global spatial and local temporal information of POIs to capture the complex dependence relationships. Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users’ dynamic preferences. Moreover, the dynamic preferences are transformed into the same space as the dependence relationships to form the final model. Finally, the proposed model is evaluated on three large-scale real-world datasets and the results demonstrate significant improvements of our model compared with state-of-the-art methods. Also, it is worth noting that the proposed model can be naturally extended to address POI recommendation and location prediction tasks with competitive performances.

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