# Context-Aware Point-of-Interest Recommendation Based on Similar User Clustering and Tensor Factorization

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## Abstract

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## 1. Introduction

- We define a user activity model and a user similarity model that can integrate contextual information of users’ check-in behavior to calculate user activity and user similarity;
- A similar user clustering method based on user activity and user similarity is presented to select the most influential active users as clustering centers based on user activity and to cluster users into several similar user clusters according to user similarity;
- A U-L-T tensor that incorporates contextual information using user activity, POI popularity, and time slot popularity as the eigenvalues in the U, L, and T dimensions, which improves the integration of contextual information, is presented;
- The CULT-TF recommendation method based on tensor factorization, which decreases the complexity of the matrix-integrating rich contextual information by clustering similar users, clustering POIs into regions of interest (ROIs), and encoding check-in timestamps to time slots, to realize the reduction of the U, L, and T dimension, respectively, is proposed. In this way, CULT-TF reduces the complexity of the recommendation matrix while integrating the richness of the contextual information.

## 2. Related Work

#### 2.1. POI Recommendation Based on Geographical Influence

#### 2.2. POI Recommendation Based on Temporal Influence

#### 2.3. POI Recommendation Based on Social Influence

#### 2.4. POI Recommendation Based on Text Context Influence

#### 2.5. POI Recommendation Based on Multiple-Context Information

## 3. Overview

#### 3.1. Similar User Clustering

#### 3.2. U-L-T Tensor Modeling

#### 3.3. Tensor Factorization

## 4. The Proposed CULT-TF Method

#### 4.1. Similar User Clustering Based on User Similarity

#### 4.1.1. User Activity Model

#### 4.1.2. User Similarity Model

#### 4.2. U-L-T Tensor Modeling with the Integration of Contextual Information

#### 4.3. TOP-N POIs Based on Tensor Factorization

## 5. Experiments

#### 5.1. Datasets

#### 5.2. Evaluation Metrics

#### 5.3. Clustering Parameter Analysis

#### 5.4. Time Slot Analysis

#### 5.5. Experimental Results and Analysis

- USG [15]: USG is a collaborative recommendation method based on the geographical influence that models the geographical clustering phenomenon by means of a naive Bayesian approach and integrates social influence;
- MGMPFM [17]: MGMPFM is a POI recommendation method based on matrix factorization that models the geographical influence of users’ check-in behavior based on a multicenter Gaussian model (MGM) and fuses social and geographical influence into a matrix factorization framework;
- GeoSoCa [36]: GeoSoCa is a POI recommendation method that exploits geographical correlations, social correlations, and category correlations among users and POIs;
- LORE [28]: LORE is a location recommendation method with sequential influence based on an additive Markov chain (AMC), which integrates sequential influence with geographical influence and social influence into a unified location recommendation framework.

- ULT-TF: This is a recommended method based on CULT-TF that does not conduct similar user clustering, i.e., it does not consider the influence of similar users;
- CLT-TF: This is a simplified version of CULT-TF in terms of the U dimension, which does not introduce the U dimensional eigenvalue “user activity”;
- CUT-TF: This is a simplified version of CULT-TF in terms of the L dimension, which does not introduce the L dimensional eigenvalue “POI popularity”;
- CUL-TF: CUL-TF is a simplified version of CULT-TF in terms of the T dimension, which does not introduce the T dimensional eigenvalue “time slot popularity.”

## 6. Discussion

## 7. Conclusions and Future Work

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 13.**Recommendation accuracy of five recommendation methods. (

**a**) Top-N Precision; (

**b**) Top-N Recall.

**Figure 14.**Recommendation accuracy of CULT-TF and four baseline methods. (

**a**) Top-N Precision; (

**b**) Top-N Recall.

Notation | Description |
---|---|

$U$ | user set |

$L$ | POI set |

$T$ | time slot set |

${u}_{i}$ | the user, with user ID $i$,${u}_{i}\in U$ |

${l}_{j}$ | the location, with POI ID $j$,${l}_{j}\in L$ |

${t}_{k}$ | the time slot, with time slot type ID $k$,${t}_{k}\in T$ |

${U}_{{u}_{i}}$ | the friend set of the user ${u}_{i}$ |

${L}_{{u}_{i}}$ | the POI set checked in by the user ${u}_{i}$ |

${T}_{{u}_{i}}$ | the time slot set checked in by the user ${u}_{i}$ |

${U}_{{l}_{j}}$ | the set of users checking in at a location ${l}_{j}$ |

${U}_{{t}_{k}}$ | the set of users checking in at a time slot ${t}_{k}$ |

${f}_{{u}_{i}}$ | the number of user ${u}_{i}$ check-ins |

${f}_{{l}_{j}}$ | the number of all user check-ins at the location ${l}_{j}$ |

${f}_{{t}_{k}}$ | the number of all user check-ins at the time slot ${t}_{k}$ |

${U}_{{u}_{v}}$ | the friend set of user ${u}_{v}$ |

${T}_{{l}_{j}}$ | the set of time slot types for user check-ins at the location ${l}_{j}$ |

${L}_{{t}_{k}}$ | the set of POIs for user check-ins during the time slot ${t}_{k}$ |

Description | Statistics |
---|---|

Time Range | 1 October 2009–30 September 2010 |

Geographic Range | 19.27° N–71.29° N 67.84° W–159.67° W |

Number of Users | 4844 |

Number of POIs | 7685 |

Number of Social Relations | 186,071 |

Number of Check-ins | 388,148 |

Ranking | User ID | User Activity | Ranking | User ID | User Activity |
---|---|---|---|---|---|

1 | 1863 | 1.000000 | 6 | 620 | 0.608652 |

2 | 1864 | 0.822594 | 7 | 0 | 0.483306 |

3 | 143 | 0.808769 | 8 | 1302 | 0.468147 |

4 | 2149 | 0.732460 | 9 | 212 | 0.414451 |

5 | 35 | 0.623615 | 10 | 208 | 0.385934 |

Cluster No. | User ID of Cluster Center | User Similarity of Cluster |
---|---|---|

1 | 1863 | 1.00592 |

2 | 1864 | 0.72074 |

3 | 143 | 0.90315 |

4 | 2149 | 0.86348 |

5 | 35 | 0.81491 |

6 | 620 | 0.90564 |

7 | 0 | 0.89310 |

Metrics | USG | MGMPFM | GeoSoCa | LORE | CULT-TF |
---|---|---|---|---|---|

Precision@5 | 0.0220 | 0.0288 | 0.0435 | 0.0664 | 0.0855 |

Recall@5 | 0.0127 | 0.0175 | 0.0237 | 0.0363 | 0.0510 |

Precision@10 | 0.0196 | 0.0259 | 0.0385 | 0.0563 | 0.0765 |

Recall@10 | 0.0190 | 0.0280 | 0.0375 | 0.0518 | 0.0687 |

Precision@15 | 0.0179 | 0.0245 | 0.0342 | 0.0501 | 0.0685 |

Recall@15 | 0.0240 | 0.0386 | 0.0475 | 0.0630 | 0.0815 |

Precision@20 Recall@20 | 0.0168 | 0.0230 | 0.0316 | 0.0455 | 0.0639 |

0.0284 | 0.0472 | 0.0566 | 0.0718 | 0.0901 |

Metrics | ULT-TF | CUL-TF | CUT-TF | CLT-TF | CULT-TF |
---|---|---|---|---|---|

Precision@5 | 0.0726 | 0.0742 | 0.0768 | 0.0807 | 0.0855 |

Recall@5 | 0.0382 | 0.0387 | 0.0424 | 0.0478 | 0.0510 |

Precision@10 | 0.0641 | 0.0665 | 0.0706 | 0.0717 | 0.0765 |

Recall@10 | 0.0530 | 0.0546 | 0.0609 | 0.0634 | 0.0687 |

Precision@15 | 0.0576 | 0.0597 | 0.0622 | 0.0656 | 0.0685 |

Recall@15 | 0.0593 | 0.0641 | 0.0730 | 0.0763 | 0.0815 |

Precision@20 Recall@20 | 0.0522 | 0.0573 | 0.0584 | 0.0620 | 0.0639 |

0.0634 | 0.0723 | 0.0804 | 0.0875 | 0.0901 |

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## Share and Cite

**MDPI and ACS Style**

Zhou, Y.; Zhou, K.; Chen, S. Context-Aware Point-of-Interest Recommendation Based on Similar User Clustering and Tensor Factorization. *ISPRS Int. J. Geo-Inf.* **2023**, *12*, 145.
https://doi.org/10.3390/ijgi12040145

**AMA Style**

Zhou Y, Zhou K, Chen S. Context-Aware Point-of-Interest Recommendation Based on Similar User Clustering and Tensor Factorization. *ISPRS International Journal of Geo-Information*. 2023; 12(4):145.
https://doi.org/10.3390/ijgi12040145

**Chicago/Turabian Style**

Zhou, Yan, Kaixuan Zhou, and Shuaixian Chen. 2023. "Context-Aware Point-of-Interest Recommendation Based on Similar User Clustering and Tensor Factorization" *ISPRS International Journal of Geo-Information* 12, no. 4: 145.
https://doi.org/10.3390/ijgi12040145