ContextsPOI (Slides)
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The document analyzes the influence of contextual information on point-of-interest recommendation systems, highlighting data sparsity as a primary challenge. Experiments conducted compare various models and evaluation metrics, revealing that geographical and temporal data significantly impact recommendations, while categorical information performs poorly. It concludes with recommendations for future work involving enhanced datasets and new evaluation metrics tailored for contextual information in recommender systems.
ContextsPOI (Slides)
- 1. A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation Hossein A. Rahmani, WI, University College London Mohammad Aliannejadi, IRLab, University of Amsterdam Mitra Baratchi, STAR, Leiden University Fabio Crestani, USI-IR, Universitá della Svizzera italiana (USI) The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval July 23-27, 2023 | Taipei, Taiwan Hybrid Conference Invited Paper ACM Transactions on Information Systems (TOIS) 40, no. 4 (2022): 1-35.
- 2. Introduction ● Point-of-Interest Recommender Systems (POI RecSys) ● Data Sparsity as a main problem in POI RecSys ● Using of contextual information to address to this problem ● Different types of contextual information ● Which context has more impact? ● Different experiments on Matrix Factorization (linear) and Neural Network (non-linear) models 2
- 3. Contextual Information in POI 3
- 4. RQ1 How effectively do different models incorporate multiple contextual factors in recommendation? Experiment 1: Focus on Contexts ● The impact of each contextual information ● Considering different combinations to evaluate the combined use of contextual information in the recommendation process ● Enabling to understand better how each of the contextual factors would affect each of the models 4
- 5. Experiment 1: Focus on Contexts (RQ1) ● Temporal context is crucial across all contextual models ● Solely using categorical information won't improve performance ● Incorporating all available contextual information may not enhance performance ● Geographical and temporal information are particularly important in different contextual combinations 5
- 6. RQ2 How can different evaluation metrics capture the effect of contextual information on various models? Experiment 2: Focus on Metrics ● Studying the results of different evaluation metrics ● Comparing evaluation metrics on the use of contextual information ○ Most common evaluation metrics in IR and RecSys: Precision, Recall, and nDCG ● How different evaluation metrics can capture the effect of contextual information on various models 6
- 7. Experiment 2: Focus on Metrics (RQ2) ● Contextual information leads to diverse effects on traditional evaluation metrics for POI recommendation models ○ E.g., a model may excel in nDCG but perform differently in precision and recall ● Due to the distinct nature of evaluation metrics, varied model performance is observed. ● Proposing of novel evaluation metrics tailored to contextual information is recommended 7
- 8. RQ3 How can we incorporate different contextual factors in linear and non-linear models? Experiment 3: Focus on Contextual Models ● Studying the results of the proposed models based on the selected evaluation metrics ● Comparing the results of different context-aware POI models on different evaluation metrics 8
- 10. Experiment 3: Focus on Contextual Models (RQ3) ● Dataset size greatly affects model performance ● Neural network models outperform matrix factorization due to better user-POI representation ● The fusion of contextual information improves performance in both approaches ● Using all contextual information may lead to inaccurate results and poor performance 10
- 11. Experiment 3: Focus on Contextual Models (RQ3) (Cont.) ● Sparse datasets benefit from matrix factorization with geographical data ● Temporal and geographical information enhance both matrix factorization and neural network models ● Categorical information shows the weakest performance among contextual models 11
- 12. RQ4 How do models incorporating contextual information perform for users with different behavior? Experiment 4: Focus on Users Behavior ● Studying the users’ behavior based on different contexts ● Considering three different behavioral habit experiments based on geographical, temporal, and user preference information 12
- 16. Experiment 4: Focus on Users Behavior (RQ4) ● Accurate recommendations are achieved in smaller neighborhoods ● High user movement range leads to less accurate recommendations ● Non-linear models excel in modeling consecutive check-ins ● Recommendations perform worse with greater time gaps between user check-ins ● POI recommendation models underperform when users explore extensively 16
- 17. Findings (Summary) ● Carefully evaluating and selecting contextual information instead of fusing all ● Assessing the impact of different combinations and fusion methods ● Geographical and temporal data are more influential than social and categorical information ● IR common metrics yield diverse results for contextual recommendation models 17
- 18. Conclusion ● Analyzed and evaluated contextual info in POI recommendation systems ● Conducted an extensive survey, comparing different approaches (geographical, temporal, social, and categorical) ● Experimented on benchmark datasets using popular evaluation metrics ● Studied the impact of contextual information combinations on recommendation performance 18
- 19. Conclusion (Cont.) ● Explored contextual info importance in POI recommendation for linear and non-linear models ● Analyzed the influence of user behaviors (geographical distance, temporal density, exploration) on recommendation quality 19
- 20. Future Work ● Additional info like comments, tips, or images can enhance POI recommendation models ● Generalization requires diverse datasets with sufficient user, POI, and category information ● Introducing new evaluation metrics for testing contextual information in recommender systems ● Investigating datasets with distinct characteristics to understand their impact on performance 20
- 21. ● This work was in part supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) mobility grant between Switzerland and Iran, and in part by the NWO (No. 016.Vidi.189.039 and No. 314-99-301). ● Work done while Hossein A. Rahmani was affiliated with Università della Svizzera italiana (USI), Switzerland. Acknowledgement 21
- 22. CREDITS: This presentation template was created by Slidesgo, and includes icons by Flaticon, and infographics & images by Freepik ´ Thanks! Do you have any questions? hossein.rahmani.22@ucl.ac.uk https://github.com/rahmanidashti/ContextsPOI 22