Abstract
Semantic tags of points of interest (POIs) are a crucial prerequisite for location search, recommendation services, and data cleaning. However, most POIs in location-based social networks (LBSNs) are either tag-missing or tag-incomplete. This article aims to develop semantic annotation techniques to automatically infer tags for POIs. We first analyze two LBSN datasets and observe that there are two types of tags, category-related ones and sentimental ones, which have unique characteristics. Category-related tags are hierarchical, whereas sentimental ones are category-aware. All existing related work has adopted classification methods to predict high-level category-related tags in the hierarchy, but they cannot apply to infer either low-level category tags or sentimental ones.
In light of this, we propose a latent-class probabilistic generative model, namely the spatial-temporal topic model (STM), to infer personal interests, the temporal and spatial patterns of topics/semantics embedded in users’ check-in activities, the interdependence between category-topic and sentiment-topic, and the correlation between sentimental tags and rating scores from users’ check-in and rating behaviors. Then, this learned knowledge is utilized to automatically annotate all POIs with both category-related and sentimental tags in a unified way. We conduct extensive experiments to evaluate the performance of the proposed STM on a real large-scale dataset. The experimental results show the superiority of our proposed STM, and we also observe that the real challenge of inferring category-related tags for POIs lies in the low-level ones of the hierarchy and that the challenge of predicting sentimental tags are those with neutral ratings.
- Bruno Antunes, Ana Alves, and Francisco C. Pereira. 2008. Semantics of place: Ontology enrichment. In Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI’08). 342--351. Google ScholarDigital Library
- Ricardo Baeza-Yates and Berthier Ribeiro-Neto. 1999. Modern Information Retrieval. Vol. 463. Addison Wesley Longman, Boston, MA. Google ScholarDigital Library
- Grigory Begelman, Philipp Keller, and Frank Smadja. 2006. Automated tag clustering: Improving search and exploration in the tag space. In Proceedings of the Collaborative Web Tagging Workshop at WWW 2006. 15--33.Google Scholar
- David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993--1022. Google ScholarDigital Library
- Zhiyuan Cheng, James Caverlee, Kyumin Lee, and Daniel Z. Sui. 2011. Exploring millions of footprints in location sharing services. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM’11). 81--88.Google Scholar
- Yohan Chon, Yunjong Kim, and Hojung Cha. 2013. Autonomous place naming system using opportunistic crowdsensing and knowledge from crowdsourcing. In Proceedings ofthe 12th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN’13). 19--30. Google ScholarDigital Library
- Yohan Chon, Nicholas D. Lane, Fan Li, Hojung Cha, and Feng Zhao. 2012. Automatically characterizing places with opportunistic crowdsensing using smartphones. In Proceedings of the 14th ACM International Conference on Ubiquitous Computing (UbiComp’12). 481--490. Google ScholarDigital Library
- Mahashweta Das, Gautam Das, and Vagelis Hristidis. 2011. Leveraging collaborative tagging for Web item design. In Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’11). 538--546. Google ScholarDigital Library
- Jacob Eisenstein, Brendan O’Connor, Noah A. Smith, and Eric P. Xing. 2010. A latent variable model for geographic lexical variation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’10). 1277--1287. Google ScholarDigital Library
- Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2013. Exploring temporal effects for location recommendation on location-based social networks. In Proceedings of the 7th ACM Conference on Recommended Systems (RecSys’13). 93--100. Google ScholarDigital Library
- Thomas Hofmann. 1999. Probabilistic latent semantic analysis. In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI’99). Google ScholarDigital Library
- Liangjie Hong, Amr Ahmed, Siva Gurumurthy, Alexander J. Smola, and Kostas Tsioutsiouliklis. 2012. Discovering geographical topics in the twitter stream. In Proceedings of the 21st International Conference on World Wide Web (WWW’12). 769--778. Google ScholarDigital Library
- Liangjie Hong, Dawei Yin, Jian Guo, and Brian D. Davison. 2011. Tracking trends: Incorporating term volume into temporal topic models. In Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’11). Google ScholarDigital Library
- Bo Hu and Martin Ester. 2013. Spatial topic modeling in online social media for location recommendation. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13). 25--32. Google ScholarDigital Library
- Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’04). 168--177. Google ScholarDigital Library
- Yohan Jo and Alice H. Oh. 2011. Aspect and sentiment unification model for online review analysis. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM’11). 815--824. Google ScholarDigital Library
- Carsten Keßler, Krzysztof Janowicz, and Mohamed Bishr. 2009. An agenda for the next generation gazetteer: Geographic information contribution and retrieval. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS’09). 91--100. Google ScholarDigital Library
- Heung-Nam Kim, Ae-Ttie Ji, Inay Ha, and Geun-Sik Jo. 2010. Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electronic Commerce Research and Applications 9, 1, 73--83.Google ScholarCross Ref
- John Krumm and Dany Rouhana. 2013. Placer: Semantic place labels from diary data. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’13). 163--172. Google ScholarDigital Library
- Bin Liu, Yanjie Fu, Zijun Yao, and Hui Xiong. 2013. Learning geographical preferences for point-of-interest recommendation. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13). 1043--1051. Google ScholarDigital Library
- Qiaozhu Mei, Xu Ling, Matthew Wondra, Hang Su, and ChengXiang Zhai. 2007. Topic sentiment mixture: Modeling facets and opinions in weblogs. In Proceedings of the 16th International World Wide Web Conference (WWW’07). 171--180. Google ScholarDigital Library
- Samaneh Moghaddam and Martin Ester. 2011. ILDA: Interdependent LDA model for learning latent aspects and their ratings from online product reviews. In Proceedings of the 34th Annual ACM SIGIR Conference (SIGIR’11). 665--674. Google ScholarDigital Library
- Axel Rottmann, Oscar Martinez Mozos, Cyrill Stachniss, and Wolfram Burgard. 2005. Semantic place classification of indoor environments with mobile robots using boosting. In Proceedings of the 20th AAAI Conference on Artificial Intelligence (AAAI’05), Vol. 5. 1306--1311. Google ScholarDigital Library
- Jie Tang, Sen Wu, Jimeng Sun, and Hang Su. 2012. Cross-domain collaboration recommendation. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). 1285--1293. Google ScholarDigital Library
- Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. 2008. ArnetMiner: Extraction and mining of academic social networks. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). 990--998. Google ScholarDigital Library
- Lu-An Tang, Yu Zheng, Jing Yuan, Jiawei Han, Alice Leung, Wen-Chih Peng, and Thomas La Porta. 2014. A framework of traveling companion discovery on trajectory data streams. ACM Transactions on Intelligent Systems and Technology 5, 1, Article No. 3. Google ScholarDigital Library
- Ivan Titov and Ryan McDonald. 2008a. Modeling online reviews with multi-grain topic models. In Proceedings of the 17th International Conference on World Wide Web (WWW’08). 111--120. Google ScholarDigital Library
- Ivan Titov and Ryan T. McDonald. 2008b. A joint model of text and aspect ratings for sentiment summarization. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACL’08), Vol. 8. 308--316.Google Scholar
- Pooja Viswanathan, Tristram Southey, James Little, and Alan Mackworth. 2011. Place classification using visual object categorization and global information. In Proceedings of the 8th Canadian Conference on Computer and Robot Vision (CRV’11). 1--7. Google ScholarDigital Library
- Hanna M. Wallach, David M. Mimno, and Andrew McCallum. 2009. Rethinking LDA: Why priors matter. In Proceedings of the 23rd Annual Conference on Neural Information Processing Systems (NIPS’09). 1973--1981. Google ScholarDigital Library
- Hongning Wang, Yue Lu, and Chengxiang Zhai. 2010. Latent aspect rating analysis on review text data: A rating regression approach. In Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’10). 783--792. Google ScholarDigital Library
- Weiqing Wang, Hongzhi Yin, Ling Chen, Yizhou Sun, Shazia Sadiq, and Xiaofang Zhou. 2015. Geo-SAGE: A geographical sparse additive generative model for spatial item recommendation. In Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’15). 1255--1264. Google ScholarDigital Library
- Xuerui Wang and Andrew McCallum. 2006. Topics over time: A non-Markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’06). 424--433. Google ScholarDigital Library
- Baoguo Yang and Suresh Manandhar. 2014. Exploring user expertise and descriptive ability in community question answering. In Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’14). IEEE, Los Alamitos, CA, 320--327.Google ScholarDigital Library
- Liu Yang, Minghui Qiu, Swapna Gottipati, Feida Zhu, Jing Jiang, Huiping Sun, and Zhong Chen. 2013. Cqarank: Jointly model topics and expertise in community question answering. In Proceedings of the ACM Conference of Information and Knowledge Management (CIKM’13). ACM, New York, NY, 99--108. Google ScholarDigital Library
- Mao Ye, Dong Shou, Wang-Chien Lee, Peifeng Yin, and Krzysztof Janowicz. 2011a. On the semantic annotation of places in location-based social networks. In Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’11). 520--528. Google ScholarDigital Library
- Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. 2011b. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th Annual ACM SIGIR Conference (SIGIR’11). 325--334. Google ScholarDigital Library
- Hongzhi Yin, Bin Cui, Ling Chen, Zhiting Hu, and Zi Huang. 2014. A temporal context-aware model for user behavior modeling in social media systems. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (SIGMOD’14). 1543--1554. Google ScholarDigital Library
- Hongzhi Yin, Bin Cui, Ling Chen, Zhiting Hu, and Xiaofang Zhou. 2015a. Dynamic user modeling in social media systems. ACM Transactions on Information Systems 33, 3, Article No. 10. Google ScholarDigital Library
- Hongzhi Yin, Bin Cui, Zi Huang, Weiqing Wang, Xian Wu, and Xiaofang Zhou. 2015b. Joint modeling of users’ interests and mobility patterns for point-of-interest recommendation. In ACM Multimedia. 819--822. Google ScholarDigital Library
- Hongzhi Yin, Bin Cui, Hua Lu, Yuxin Huang, and Junjie Yao. 2013a. A unified model for stable and temporal topic detection from social media data. In Proceedings of the 29th IEEE International Conference on Data Engineering (ICDE’13). 661--672. Google ScholarDigital Library
- Hongzhi Yin, Yizhou Sun, Bin Cui, Zhiting Hu, and Ling Chen. 2013b. LCARS: A location-content-aware recommender system. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KIDD’13). 221--229. Google ScholarDigital Library
- Hongzhi Yin, Xiaofang Zhou, Yingxia Shao, Hao Wang, and Shazia Sadiq. 2015. Joint modeling of user check-in behaviors for point-of-interest recommendation. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM’15). 1631--1640. Google ScholarDigital Library
- Zhijun Yin, Liangliang Cao, Jiawei Han, Chengxiang Zhai, and Thomas Huang. 2011. Geographical topic discovery and comparison. In Proceedings of the International Conference on World Wide Web (WWW’11). 247--256. Google ScholarDigital Library
- Jing Yuan, Yu Zheng, and Xing Xie. 2012. Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). 186--194. Google ScholarDigital Library
- Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat Thalmann. 2013a. Time-aware point-of-interest recommendation. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’13). 363--372. Google ScholarDigital Library
- Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat Thalmann. 2013b. Who, where, when and what: Discover spatio-temporal topics for Twitter users. In Proceeding of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13). 605--613. Google ScholarDigital Library
- Quan Yuan, Gao Cong, Kaiqi Zhao, Zongyang Ma, and Aixin Sun. 2015. Who, where, when, and what: A nonparametric Bayesian approach to context-aware recommendation and search for Twitter users. ACM Transactions on Information Systems 33, 1, Article No. 2. Google ScholarDigital Library
- Yongfeng Zhang, Haochen Zhang, Min Zhang, Yiqun Liu, and Shaoping Ma. 2014. Do users rate or review? Boost phrase-level sentiment labeling with review-level sentiment classification. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’14). 1027--1030. Google ScholarDigital Library
- Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee-Peng Lim, Hongfei Yan, and Xiaoming Li. 2011. Comparing Twitter and traditional media using topic models. In Proceedings of the 33rd European Conference on Information Retrieval (ECIR’11). 338--349. Google ScholarDigital Library
Index Terms
- A Spatial-Temporal Topic Model for the Semantic Annotation of POIs in LBSNs
Recommendations
Geo topic model: joint modeling of user's activity area and interests for location recommendation
WSDM '13: Proceedings of the sixth ACM international conference on Web search and data miningThis paper proposes a method that analyzes the location log data of multiple users to recommend locations to be visited. The method uses our new topic model, called Geo Topic Model, that can jointly estimate both the user's interests and activity area ...
Topic-based User Profile Model for POI Recommendations
ISMSI '18: Proceedings of the 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm IntelligenceLocation-based services (LBSs) provide users with points of interest (POIs) based on a limited distance from the user current location. Recently, the increased number of POIs in LBSs often results in overwhelming place suggestions generated to users ...
Towards an integrated view of semantic annotation for POIs with spatial and textual information
IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial IntelligenceCategories of Point of Interest (POI) facilitate location-based services from many aspects like location search and POI recommendation. However, POI categories are often incomplete and new POIs are being consistently generated, this rises the demand for ...
Comments