Abstract
The Point Of Interest (POI) sequence recommendation is the key task in itinerary and travel route planning. Existing works usually consider the temporal and spatial factors in travel planning. However, the external environment, such as the weather, is usually overlooked. In fact, the weather is an important factor because it can affect a user’s check-in behaviors. Furthermore, most of the existing research is based on a static environment for POI sequence recommendation. While the external environment (e.g., the weather) may change during travel, it is difficult for existing works to adjust the POI sequence in time. What’s more, people usually prefer the attractive routes when traveling. To address these issues, we first conduct comprehensive data analysis on two real-world check-in datasets to study the effects of weather and time, as well as the features of the POI sequence. Based on this, we propose a model of Dynamic Personalized POI Sequence Recommendation with fine-grained contexts (DPSR for short). It extracts user interest and POI popularity with fine-grained contexts and captures the attractiveness of the POI sequence. Next, we apply the Monte Carlo Tree Search model (MCTS for short) to simulate the process of recommending POI sequence in the dynamic environment, i.e., the weather and time change after visiting a POI. What’s more, we consider different speeds to reflect the fact that people may take different transportation to transfer between POIs. To validate the efficacy of DPSR, we conduct extensive experiments. The results show that our model can improve the accuracy of the recommendation significantly. Furthermore, it can better meet user preferences and enhance experiences.
- [1] . 2005. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS) 23, 1 (2005), 103–145.Google ScholarDigital Library
- [2] . 2018. CLoSe: Contextualized location sequence recommender. In Proceedings of the 12th ACM Conference on Recommender Systems. ACM, 470–474.Google ScholarDigital Library
- [3] . 1993. A note on the prize collecting traveling salesman problem. Mathematical Programming 59, 1-3 (1993), 413–420.Google ScholarDigital Library
- [4] . 2018. Content-Aware hierarchical Point-of-Interest embedding model for successive POI recommendation. In IJCAI. 3301–3307.Google Scholar
- [5] . 2012. Improved algorithms for orienteering and related problems. ACM Transactions on Algorithms (TALG) 8, 3 (2012), 1–27.Google ScholarDigital Library
- [6] . 2014. TripPlanner: Personalized trip planning leveraging heterogeneous crowdsourced digital footprints. IEEE Transactions on Intelligent Transportation Systems 16, 3 (2014), 1259–1273.Google ScholarDigital Library
- [7] . 2016. Learning points and routes to recommend trajectories. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2227–2232.Google ScholarDigital Library
- [8] . 2019. Effective and efficient reuse of past travel behavior for route recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 488–498.Google ScholarDigital Library
- [9] . 2010. Automatic construction of travel itineraries using social breadcrumbs. In Proceedings of the 21st ACM Conference on Hypertext and Hypermedia. ACM, 35–44.Google ScholarDigital Library
- [10] . 2018. Preference aware travel route recommendation with temporal influence. In Proceedings of the 2nd ACM SIGSPATIAL Workshop on Recommendations for Location-Based Services and Social Networks. ACM, 2.Google ScholarDigital Library
- [11] . 2019. Brand purchase prediction based on time-evolving user behaviors in e-commerce. Concurrency and Computation: Practice and Experience 31, 1 (2019), e4882.1–e4882.15.Google ScholarCross Ref
- [12] . 2014. A survey on algorithmic approaches for solving tourist trip design problems. Journal of Heuristics 20, 3 (2014), 291–328.Google ScholarDigital Library
- [13] . 2020. Enhancing personalized trip recommendation with attractive routes. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI’20), Vol. 34. 662–669.Google ScholarCross Ref
- [14] . 2013. Probability: A Graduate Course. Vol. 75. Springer Science & Business Media.Google ScholarCross Ref
- [15] . 2022. Enhancing N-Gram based metrics with semantics for better evaluation of abstractive text summarization. Journal of Computer Science and Technology 37, 5 (Oct. 2022), 1118–1133.
DOI: Google ScholarDigital Library - [16] . 2009. A time-context-based collaborative filtering algorithm. In 2009 IEEE International Conference on Granular Computing. IEEE, 209–213.Google ScholarCross Ref
- [17] . 2017. Unifying multi-source social media data for personalized travel route planning. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 893–896.Google ScholarDigital Library
- [18] . 2020. Personalized review recommendation based on users’ aspect sentiment. ACM Transactions on Internet Technology 20, 4 (
Oct. 2020), Article42 , 26 pages.DOI: Google ScholarDigital Library - [19] . 2019. An attention-based spatiotemporal LSTM network for next POI recommendation. IEEE Transactions on Services Computing 14, 6 (2019), 1585–1597.Google Scholar
- [20] . 2021. Review summary generation in online systems: Frameworks for supervised and unsupervised scenarios. ACM Transactions on the Web 15, 3 (
May 2021), Article13 , 33 pages.DOI: Google ScholarDigital Library - [21] . 2006. Improved Monte-Carlo search. University of Tartu, Estonia, Technical Report 1.Google Scholar
- [22] The largest review site. 12th December, 2022. www.yelp.com.Google Scholar
- [23] . 2016. Recommendation systems in real applications: Algorithm and parallel architecture. In International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage. Springer, 45–58.Google ScholarDigital Library
- [24] . 2020. Geography-aware sequential location recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2009–2019.Google ScholarDigital Library
- [25] . 2017. Personalized itinerary recommendation with queuing time awareness. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 325–334.Google ScholarDigital Library
- [26] . 2019. Tour recommendation and trip planning using location-based social media: A survey. Knowledge and Information Systems 60, 3 (2019), 1247–1275.Google ScholarDigital Library
- [27] . 2018. Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency. Knowledge and Information Systems 54, 2 (2018), 375–406.Google ScholarDigital Library
- [28] . 2013. Personalized point-of-interest recommendation by mining users’ preference transition. In ACM International Conference on Conference on Information & Knowledge Management.Google ScholarDigital Library
- [29] . 2016. https://www.yongliu.org/datasets/.Google Scholar
- [30] . 2014. Exploiting geographical neighborhood characteristics for location recommendation. In ACM International Conference on Conference on Information & Knowledge Management.Google ScholarDigital Library
- [31] . 2019. Hierarchical gating networks for sequential recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 825–833.Google ScholarDigital Library
- [32] . 2017. A deep recurrent collaborative filtering framework for venue recommendation. In Proceedings of the 2017 ACM Conference on Information and Knowledge Management. 1429–1438.Google ScholarDigital Library
- [33] . 2019. Value-aware recommendation based on reinforced profit maximization in E-commerce systems. arXiv:1902.00851. http://arxiv.org/abs/1902.00851Google Scholar
- [34] . 2009. www.foursquare.com.Google Scholar
- [35] . 2010. Factorization machines. In 2010 IEEE International Conference on Data Mining. IEEE, 995–1000.Google ScholarDigital Library
- [36] . 1957. The cosine-Haversine formula. The American Mathematical Monthly 64, 1 (1957), 38–40.Google ScholarCross Ref
- [37] . 2022. Improving friend recommendation for online learning with fine-grained evolving interest. Journal of Computer Science and Technology 37, 6 (Nov. 2022), 1444–1463.
DOI: Google ScholarDigital Library - [38] . 2017. Point-of-Interest recommendations: Capturing the geographical influence from local trajectories. In 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA’17/IUCC’17). IEEE, 1122–1129.Google ScholarCross Ref
- [39] . 2020. Where to go next: Modeling long-and short-term user preferences for point-of-interest recommendation. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI’20).Google ScholarCross Ref
- [40] . 2016. Understanding the impact of weather for POI recommendations. In RecTour@ RecSys. 16–23.Google Scholar
- [41] . 2011. The orienteering problem: A survey. European Journal of Operational Research 209, 1 (2011), 1–10.Google ScholarCross Ref
- [42] . 2014. Fine-grained feature-based social influence evaluation in online social networks. IEEE Transactions on Parallel and Distributed Systems 25, 9 (2014), 2286–2296.Google ScholarCross Ref
- [43] . 2019. Empowering A* search algorithms with neural networks for personalized route recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 539–547.Google ScholarDigital Library
- [44] . 2016. Improving personalized trip recommendation by avoiding crowds. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 25–34.Google ScholarDigital Library
- [45] . 2016. https://api.darksky.net/forecast/.Google Scholar
- [46] . 2021. Exploiting temporal dynamics in product reviews for dynamic sentiment prediction at the aspect level. ACM Transactions on Knowledge Discovery from Data 15, 4 (2021), 68:1–68:29.Google ScholarDigital Library
- [47] . 2019. CFM: Convolutional factorization machines for context-aware recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. AAAI Press, 3926–3932.Google ScholarCross Ref
- [48] . 2015. Topic based context-aware travel recommendation method exploiting geotagged photos. Neurocomputing 155 (2015), 99–107.Google ScholarDigital Library
- [49] . 2013. Time-aware point-of-interest recommendation. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 363–372.Google ScholarDigital Library
- [50] . 2021. Exploring weather data to predict activity attendance in event-based social network. ACM Transactions on the Web (TWEB) 15, 2 (2021) 10:1–10:25.Google ScholarDigital Library
- [51] . 2020. Discovering subsequence patterns for next POI pecommendation. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI’20), (Ed.). ijcai.org, 3216–3222.Google Scholar
- [52] . 2020. Where to go next: A spatio-temporal gated network for next POI recommendation. IEEE Transactions on Knowledge and Data Engineering 34, 5 (2020), 2512–2524.Google ScholarCross Ref
- [53] . 2021. Photo2Trip: Exploiting visual contents in geo-tagged photos for personalized tour recommendation. IEEE Transactions on Knowledge and Data Engineering 33, 4 (
Apr. 2021), 1708–1721.DOI: Google ScholarDigital Library
Index Terms
- Dynamic Personalized POI Sequence Recommendation with Fine-Grained Contexts
Recommendations
Personalized Interest Sustainability Modeling for Sequential POI Recommendation
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementSequential point-of-interest (POI) recommendation endeavors to capture users' dynamic interests based on their historical check-ins, subsequently predicting the next POIs that they are most likely to visit.Existing methods conventionally capture users' ...
Personalized POI recommendation based on check-in data and geographical-regional influence
ICMLSC '19: Proceedings of the 3rd International Conference on Machine Learning and Soft ComputingNowadays, many people like to share the places they visited to their friends in the location-based social networks (LBSNs). Therefore, LBSNs have accumulated large-scale user check-in data and the availability of these data enables many location-based ...
Item recommendation based on context-aware model for personalized u-healthcare service
A personalized service in the ubiquitous environment is to provide services or items, which reflect personal tastes, attitudes, and contexts. It is impossible to reflect the context information generated in u-healthcare environments due to the existing ...
Comments