# A Tour Recommendation System Considering Implicit and Dynamic Information
## 6 Conclusions  
Tourism has emerged as a significant sector within the global service industry. Due to the rapid development of social media, more people like self-guided tours than package itineraries planned by travel agencies. However, making an itinerary plan for most tourists is still time-consuming because they are unfamiliar with the Point-of-Interest (POI) they plan to visit. This paper proposes a novel recommendation system to help self-guided tours make a better personalized tour plan. First, this research uses the implicit feature extracted from the textual description of the POI as a POI representation. Second, unlike most previous studies ignoring POI visiting orders, this study considers tourists' visiting sequences when conducting clustering. Third, to derive the staying time for different users, time slots, and POIs, the Non-negative Matrix Factorization (NMF) is adopted. The dynamic staying time can make the recommendation result more precise and satisfying. Finally, a personalized itinerary algorithm is proposed to consider user preference, dynamic staying time, and time limitation. The algorithm will return the itinerary with the highest preference score and the longest remaining time.  
The proposed framework is validated through a set of experiments. To show the advantages of the proposed tour recommendation system, seven baseline methods are compared in terms of the metrics of P@K, MAP@K, MRR@K, and NDCG@K. It is found that the proposed approach significantly outperforms baseline methods regarding four commonly used evaluation metrics. The experiments also show that the proposed system can help tourists visit more POIs under the same time limitation and generate tour plans close to the desired time limitation compared to the baseline methods. In addition, the effects of different parameters in the proposed tour recommendation system are analyzed. The result concludes that the proposed tour recommendation system, considering implicit POI features and dynamic staying time, can improve the performance of the recommendation result.  
Some further work can be conducted to improve the proposed system. First, the experiments are limited to data from Tokyo. To provide stronger evidence for the system's generalizability, the experiments expanded to include data from multiple locations should be desired. Second, incorporating the variability of user staying time based on different types of days (weekdays, weekends, holidays) could lead to more accurate predictions of POI staying times. Third, this study utilized NMF to extract the user's staying time across various time intervals and POIs. Exploring other established factorization methods, such as Convolutional NMF and SVD or neural networks, can enhance the findings. Fourth, the availability of POIs at the proper time should be an essential factor when planning the itinerary. Therefore, further study to include the time-window limitation of POIs should be worthwhile. Lastly, it was found that the length of users' itineraries in the dataset affects the performance of the proposed algorithm. Therefore, revising our algorithm by consistently requesting shorter sequences and integrating the set of shorter sequences into a more extended sequence is worthwhile.  
**Author Contributions:** Conceptualization, C-Y.T. and H-Y.J.; Methodology, C-Y.T., K-W.C. and H.-Y.J.; Software, K-W.C.; Validation, H.H.; Formal analysis, K-W.C.; Writing--original draft, K-W.C.; Writing--review & editing, H-Y.J. and H.H.; Supervision, C-Y.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Dataset available on request due to the ongoing project development.  
**Conflicts of Interest:** This research received no external funding.