Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest Recommendation
Abstract
:1. Introduction
- (1)
- Fused multiple factors models [6,7,8,9,10,11]: These works consider the roles of various context factors in order to determine and match user preferences, i.e., geographical, social, temporal, POI category, and textual content factors. The authors employ different advanced techniques to integrate these factors for POI recommendations, i.e., matrix factorization, convolution neural network, graph neural network, recurrent neural network, and deep neural network. However, all of them assume the user-POI rating is reliable and ignore the unreliable rating involved in check-ins. There is no doubt that if unreliable noise data are not excluded, the accuracy of recommendation is limited regardless of the model’s optimization.
- (2)
- Trust-enhanced models [12,13,14,15]: These works [12,13,14] propose a user–user trust matrix modeled by calculating the trust relationship between users. Combining the user trust matrix and the user rating, they achieve the trust-enhanced similarity between users. The work [15] proposes a DeepWalk-based trust similarity measurement running over a co-visited network. Nevertheless, all of them ignore the temporal factor influence on the user similarity, leading to biased similarities. For example, the user visits POI and at time and , respectively, while checks in and at time and , respectively. When not considering time, the similarity between the two users is 1. However, if taking time into account, the similarity between them is 0, since the time of their check-ins on the two POIs is different. Existing methods fail to distinguish the difference. Furthermore, these authors only consider the trust filtering from the view of users, resulting in insufficient trustworthiness evaluations. For example, given a location and the check-in set visited by , could be recommended to due to their stronger correlations on geographical or textual content factors. However, may not be a good choice for when it is rated by other unreliable users. Existing methods fail to consider the trust filtering from the view of locations.
- These works fail to refine the influence of context factors and fusion between the user preference and context models;
- These works ignore the role of the trust filtering from the view of locations.
- We propose a novel bidirectional trust-enhanced collaborative filtering model, which performs the trust filtering from the views of users and locations via leveraging temporal, geographical, and textual content factors. To our knowledge, we are the first to focus on trust filtering from the views of users and locations.
- We refine the influence of context factors against the data sparsity problem.
- We develop a fused framework for the trust filtering and the user preference models, which considers the different impacts of factors on the POIs that users have visited and the POIs that users have not visited.
2. Related Works
2.1. Fused Multiple Factors POI Recommendations
2.2. Trust-Enhanced POI Recommendations
3. Proposed Recommendation Model
3.1. Problem Formulation
3.2. Modeling Trustworthy Community from LBSNs
3.3. Trust User-Based Collaborative Filtering
3.4. Trust Location-Based Collaborative Filtering
3.5. Fused Model
3.6. Time Complexity Analysis
4. Experiments
4.1. Datasets
4.2. Baselines
- Context-influence-enhanced models
- (1)
- ASMF [17]: ASMF is a fused weighted matrix factorization with social factor which defines three types of friendships, including social friends, location friends, and neighboring friends, for POI recommendation.
- (2)
- TA [18]: TA is a temporal factor-enhanced collaborative filtering model that recommends POIs to a given user at a specific time.
- (3)
- ST-RNet [19]: ST-RNet is a spatiotemporal recommender network model which learns the cross-features and the combined features of users, POIs, and time together based on neural network.
- (4)
- SSTPMF [8]: SSTPMF is a POI recommendation model integrating social spatiotemporal information into probabilistic matrix factorization, which develops a multivariable inference approach using the latent social space, geographical space, and POI category space similarities for POI recommendation.
- Trust-enhanced models
- (1)
- SPTW [12]: SPTW is a social pertinent trust walker model for POI recommendation, which is modeled by calculating the level of trust between users in social networks. Combining high probability location category algorithm, SPTW can generate POI recommendation lists.
- (2)
- TECF [15]: TECF is a trust-enhanced collaborative filtering model, which fuses the geographic factor, temporal factor, and trust relationship learned by DeepWalk model running over the user covisiting network, for POI recommendation.
4.3. Parameter Settings
4.4. Evaluation Metrics
4.5. Experimental Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Notions | Descriptions |
---|---|
U, L, | The user set, POI set, POI category set |
R | The user-POI rating matrix |
The frequency or rating of on , | |
The trust weighted matrix | |
The direct trust relationship between and | |
The indirect trust relationship between and | |
The user similarity matrix | |
The geographical similarity between and | |
The check-in behavior similarity between and | |
The trust user influence matrix | |
The trust location influence matrix | |
The geographical correlation between and | |
The textual content correlation between and | |
The trust-enhanced context factor fused matrix | |
The user preference matrix | |
The K-dimensional potential matrix for users | |
The K-dimensional potential matrix for POIs | |
The K-dimensional potential matrix for POI categories |
Dataset | |Users| | |POIs| | |Categories| | |Check-Ins| | Density% |
---|---|---|---|---|---|
Gowalla | 4159 | 24919 | 225 | 301191 | 0.291 |
Foursquare | 3475 | 21657 | 157 | 289467 | 0.385 |
Dataset | K | Metrics | ASMF | TA | ST-RNet | SSTPMF | SPTW | TECF | BiTCF |
---|---|---|---|---|---|---|---|---|---|
Gowalla | 5 | precision@5 | 0.0512 | 0.0571 | 0.0620 | 0.0522 | 0.0508 | 0.0649 | 0.0566 |
recall@5 | 0.0341 | 0.0353 | 0.0382 | 0.0343 | 0.0336 | 0.0444 | 0.0347 | ||
10 | precision@5 | 0.0574 | 0.0571 | 0.0620 | 0.0624 | 0.0565 | 0.0649 | 0.0644 | |
recall@5 | 0.0359 | 0.0353 | 0.0382 | 0.0401 | 0.0348 | 0.0444 | 0.0419 | ||
20 | precision@5 | 0.0617 | 0.0571 | 0.0620 | 0.0657 | 0.0605 | 0.0649 | 0.0739 | |
recall@5 | 0.0362 | 0.0353 | 0.0382 | 0.0433 | 0.0349 | 0.0444 | 0.0490 | ||
40 | precision@5 | 0.0613 | 0.0571 | 0.0620 | 0.0651 | 0.0607 | 0.0649 | 0.0731 | |
recall@5 | 0.0371 | 0.0353 | 0.0382 | 0.0437 | 0.0360 | 0.0444 | 0.0475 | ||
Foursquare | 5 | precision@5 | 0.0553 | 0.0609 | 0.0635 | 0.0568 | 0.0550 | 0.0673 | 0.0598 |
recall@5 | 0.0361 | 0.0355 | 0.0399 | 0.0366 | 0.0356 | 0.0472 | 0.0389 | ||
10 | precision@5 | 0.0597 | 0.0609 | 0.0635 | 0.0645 | 0.0589 | 0.0673 | 0.0684 | |
recall@5 | 0.0374 | 0.0355 | 0.0399 | 0.0429 | 0.0366 | 0.0472 | 0.0468 | ||
20 | precision@5 | 0.0621 | 0.0609 | 0.0635 | 0.0682 | 0.0613 | 0.0673 | 0.0783 | |
recall@5 | 0.0371 | 0.0355 | 0.0399 | 0.0460 | 0.0362 | 0.0472 | 0.0526 | ||
40 | precision@5 | 0.0607 | 0.0609 | 0.0635 | 0.0684 | 0.0603 | 0.0673 | 0.0755 | |
recall@5 | 0.0364 | 0.0355 | 0.0399 | 0.0455 | 0.0355 | 0.0472 | 0.0492 |
Dataset | Metrics | ASMF | TA | ST-RNet | SSTPMF | SPTW | TECF | BiTCF |
---|---|---|---|---|---|---|---|---|
Gowalla | precision@5 | 0.0617 | 0.0571 | 0.0620 | 0.0657 | 0.0605 | 0.0649 | 0.0739 |
recall@5 | 0.0362 | 0.0353 | 0.0382 | 0.0433 | 0.0349 | 0.0444 | 0.0490 | |
precision@10 | 0.0536 | 0.0503 | 0.0555 | 0.0588 | 0.0533 | 0.0568 | 0.0649 | |
recall@10 | 0.0423 | 0.0411 | 0.0447 | 0.0489 | 0.0405 | 0.0505 | 0.0586 | |
precision@20 | 0.0475 | 0.0452 | 0.0502 | 0.0537 | 0.0476 | 0.0520 | 0.0574 | |
recall@20 | 0.0500 | 0.0493 | 0.0519 | 0.0562 | 0.0471 | 0.0577 | 0.0641 | |
precision@40 | 0.0446 | 0.0417 | 0.0468 | 0.0501 | 0.0432 | 0.0492 | 0.0545 | |
recall@40 | 0.0528 | 0.0517 | 0.0540 | 0.0583 | 0.0495 | 0.0600 | 0.0660 | |
Foursquare | precision@5 | 0.0621 | 0.0609 | 0.0635 | 0.0682 | 0.0613 | 0.0673 | 0.0783 |
recall@5 | 0.0371 | 0.0355 | 0.0399 | 0.0460 | 0.0362 | 0.0472 | 0.0526 | |
precision@10 | 0.0552 | 0.0535 | 0.0566 | 0.0609 | 0.0541 | 0.0602 | 0.0729 | |
recall@10 | 0.0427 | 0.0419 | 0.0462 | 0.0524 | 0.0420 | 0.0531 | 0.0600 | |
precision@20 | 0.0492 | 0.0472 | 0.0510 | 0.0562 | 0.0483 | 0.0543 | 0.0643 | |
recall@20 | 0.0501 | 0.0490 | 0.0523 | 0.0590 | 0.0472 | 0.0608 | 0.0697 | |
precision@40 | 0.0461 | 0.0441 | 0.0473 | 0.0535 | 0.0440 | 0.0522 | 0.0616 | |
recall@40 | 0.0524 | 0.0509 | 0.0541 | 0.0622 | 0.0486 | 0.0635 | 0.0725 |
Dataset | n | Metrics | ASMF | TA | ST-RNet | SSTPMF | SPTW | TECF | BiTCF |
---|---|---|---|---|---|---|---|---|---|
Gowalla | 3 | precision@5 | 0.0166 | 0.0150 | 0.0184 | 0.0201 | 0.0158 | 0.0199 | 0.0227 |
recall@5 | 0.0145 | 0.0141 | 0.0159 | 0.0176 | 0.0138 | 0.0179 | 0.0208 | ||
5 | precision@5 | 0.0179 | 0.0157 | 0.0197 | 0.0218 | 0.0168 | 0.0214 | 0.0263 | |
recall@5 | 0.0162 | 0.0145 | 0.0181 | 0.0200 | 0.0142 | 0.0150 | 0.0227 | ||
10 | precision@5 | 0.0192 | 0.0169 | 0.0210 | 0.0238 | 0.0179 | 0.0225 | 0.0300 | |
recall@5 | 0.0176 | 0.0152 | 0.0195 | 0.0213 | 0.0150 | 0.0218 | 0.0251 | ||
Foursquare | 3 | precision@5 | 0.0174 | 0.0155 | 0.0188 | 0.0207 | 0.0162 | 0.0202 | 0.0234 |
recall@5 | 0.0149 | 0.0142 | 0.0165 | 0.0184 | 0.0141 | 0.0189 | 0.0210 | ||
5 | precision@5 | 0.0189 | 0.0172 | 0.0205 | 0.0229 | 0.0182 | 0.0226 | 0.0274 | |
recall@5 | 0.0156 | 0.0155 | 0.0180 | 0.0210 | 0.0150 | 0.0216 | 0.0233 | ||
10 | precision@5 | 0.0207 | 0.0188 | 0.0220 | 0.0243 | 0.0200 | 0.0239 | 0.0311 | |
recall@5 | 0.0185 | 0.0176 | 0.0203 | 0.0225 | 0.0174 | 0.0232 | 0.0260 |
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An, J.; Jiang, W.; Li, G. Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest Recommendation. Sensors 2023, 23, 4140. https://doi.org/10.3390/s23084140
An J, Jiang W, Li G. Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest Recommendation. Sensors. 2023; 23(8):4140. https://doi.org/10.3390/s23084140
Chicago/Turabian StyleAn, Jingmin, Wei Jiang, and Guanyu Li. 2023. "Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest Recommendation" Sensors 23, no. 8: 4140. https://doi.org/10.3390/s23084140
APA StyleAn, J., Jiang, W., & Li, G. (2023). Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest Recommendation. Sensors, 23(8), 4140. https://doi.org/10.3390/s23084140