Extracting and Ranking Travel Tips from User-Generated Reviews
Open accessAbstract
User-generated reviews are a key driving force behind some of the leading websites, such as Amazon, TripAdvisor, and Yelp. Yet, the proliferation of user reviews in such sites also poses an information overload challenge: many items, especially popular ones, have a large number of reviews, which cannot all be read by the user. In this work, we propose to extract short practical tips from user reviews. We focus on tips for travel attractions extracted from user reviews on TripAdvisor. Our method infers a list of templates from a small gold set of tips and applies them to user reviews to extract tip candidates. For each attraction, the associated candidates are then ranked according to their predicted usefulness. Evaluation based on labeling by professional annotators shows that our method produces high-quality tips, with good coverage of cities and attractions.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
It remains unknown how reviews can help with the unique interests of BLV users, especially providing descriptive information on product appearance.
“Prior work towards general users investigated extracting experiences [21, 24], tips [11, 41] or snippets suitable for product descriptions [7, 25], yet it remains unknown how reviews can help with the unique interests of BLV users, especially providing descriptive information on product appearance.”
From Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users (2021) · cites this paper as [11]
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works
Mining and summarizing customer reviews
Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793 · see citing works
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405 · see citing works
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003 · see citing works
Role of social media in online travel information search
Xiang, Z., & Ulrike Gretzel, B. (2009) · Tourism Management · doi:10.1016/j.tourman.2009.02.016 · Cited by 3,464 · see citing works
Accurate unlexicalized parsing
Klein, D., & Manning, C. D. (2003) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1075096.1075150 · Cited by 3,040 · see citing works
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization
Erkan, G., & Radev, D. R. (2004) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1523 · Cited by 2,975 · see citing works
Assessing Agreement on Classification Tasks: The Kappa Statistic
Carletta, J. (1996) · arXiv · doi:10.48550/arxiv.cmp-lg/9602004 · Cited by 2,088 · see citing works
The impact of online user reviews on hotel room sales
Ye, Q., Law, R., & Gu, B. (2008) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2008.06.011 · Cited by 1,291 · see citing works
Ye, Q., Law, R., Gu, B., & Chen, W. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2010.04.014 · Cited by 1,179 · see citing works
Centroid-based summarization of multiple documents
Radev, D., Jing, H., Styś, M. E., & Tam, D. (2003) · Information Processing & Management · doi:10.1016/j.ipm.2003.10.006 · Cited by 1,050 · see citing works
An algorithm for pronominal anaphora resolution
Lappin, S., & Leass, H. J. (1994) · doi:10.5555/203987.203989 · Cited by 763 · see citing works
Recent automatic text summarization techniques: a survey
Gambhir, M., & Gupta, V. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9475-9 · Cited by 729 · see citing works
Information Retrieval as Statistical Translation
Berger, A. L., & Lafferty, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130371 · Cited by 621 · see citing works
Automatically assessing review helpfulness
Kim, S.-M., Pantel, P., Chklovski, T. A., & Marco Pennacchiotti, B. (2006) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1610075.1610135 · Cited by 527 · see citing works
Building a Large Annotated Corpus of English: The
MarcusMitchell, P., Ann, M., & SantoriniBeatrice (1993) · Computational Linguistics · doi:10.5555/972470.972475 · Cited by 495 · see citing works
Information retrieval as statistical translation
Berger, A. L., & Lafferty, J. (1999) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/312624.312681 · Cited by 396 · see citing works
User-Generated Content and Travel: A Case Study on Tripadvisor.Com
O’Connor, P. (2008) · Springer eBooks · doi:10.1007/978-3-211-77280-5_5 · Cited by 384 · see citing works
Adaptive regularization of weight vectors
Crammer, K., Kulesza, A., & Dredze, M. H. (2013) · Machine Learning · doi:10.1007/s10994-013-5327-x · Cited by 337 · see citing works
Exploiting social context for review quality prediction
Lu, Y., Tsaparas, P., Ntoulas, A., & Polanyi, L. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772761 · Cited by 308 · see citing works
Detection of review spam: A survey
Heydari, A., Tavakoli, M. A., Salim, N. B., & Heydari, Z. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.12.029 · Cited by 301 · see citing works
The Evaluation of Sentence Similarity Measures
Achananuparp, P., Hu, X., & Shen, X. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85836-2_29 · Cited by 225 · see citing works
Searching by Talking: Analysis of Voice Queries on Mobile Web Search
Guy, I. (2016) · Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval · doi:10.1145/2911451.2911525 · Cited by 140 · cited 1× · see citing works
“For distinctive terms, we calculated those that contribute the most to the KL divergence between the language model induced from useful tips and that induced from not-useful tips and vice versa [2, 12] 7 .”
MethodSelf-citationSelecting a comprehensive set of reviews
Tsaparas, P., Ntoulas, A., & Terzi, E. (2011) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020440 · Cited by 124 · see citing works
Assessing agreement on classification tasks
CarlettaJean (1996) · Computational Linguistics · doi:10.5555/230386.230390 · Cited by 86 · see citing works
Yatani, K., Novati, M., Trusty, A., & Truong, K. N. (2011) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979167 · Cited by 82 · see citing works
User-generated content (UGC) in tourism: benefits and concerns of online consumers
Burgess, S., Sellitto, C., Cox, C., & Buultjens, J. (2009) · Cross Connect (Southern Cross University) · Cited by 74 · see citing works
Selecting a characteristic set of reviews
Lappas, T., Crovella, M. E., & Terzi, E. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339663 · Cited by 70 · see citing works
Identifying helpful reviews based on customer’s mentions about experiences
Min, H.-J., & Park, J. C. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.116 · Cited by 66 · see citing works
A method for measuring sentence similarity and its application to conversational agents
Li, Y., Bandar, Z. A., McLean, D., & O’Shea, J. D. (2004) · University of Salford Institutional Repository (University of Salford) · Cited by 47 · see citing works
Weber, I. G., Ukkonen, A., & Gionis, A. (2012) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2124295.2124369 · Cited by 45 · see citing works
Novelty based Ranking of Human Answers for Community Questions
Omari, A., Carmel, D., Rokhlenko, O., & Szpektor, I. (2016) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2911451.2911506 · Cited by 38 · see citing works
Detecting Experiences from Weblogs
Park, K. C., Jeong, Y., & Myaeng, S. H. (2010) · Cited by 28 · see citing works
Generating comparative summaries from reviews
Sipoš, R., & Joachims, T. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2507879 · Cited by 27 · see citing works
Wicaksono, A. F., & Myaeng, S.-H. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398637 · Cited by 24 · see citing works
Using micro-reviews to select an efficient set of reviews
Nguyen, T.-S., Lauw, H. W., & Tsaparas, P. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505568 · Cited by 14 · see citing works
Pareti, P., Klein, E., & Barker, A. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2567948.2578846 · Cited by 12 · see citing works
Solution mining for specific contextualised problems
Sauer, C., & Roth–Berghofer, T. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187980.2188193 · Cited by 9 · see citing works
Automatic Extraction of Task Statements from Structured Meeting Content
Nagao, K., Inoue, K., Morita, N., & Matsubara, S. (2015) · International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management · doi:10.5220/0005609703070315 · Cited by 8 · see citing works
Wicaksono, A. F., & Myaeng, S.-H. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505520 · Cited by 8 · see citing works
Advice Extraction from Web for Providing Prior Information Concerning Outdoor Activities
Kozawa, S., Okamoto, M., Nagano, S., Cho, K., & Matsubara, S. (2011) · Smart innovation, systems and technologies · doi:10.1007/978-3-642-22158-3_25 · Cited by 6 · see citing works
Polarized review summarization as decision making tool
Cremonesi, P., Facendola, R., Garzotto, F., Guarnerio, M., Natali, M., & Pagano, R. (2014) · International Conference on Advanced Visual Interfaces (AVI) · doi:10.1145/2598153.2600047 · Cited by 6 · see citing works
Ryu, J., Jung, Y., & Myaeng, S.-H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32790-2_33 · Cited by 3 · see citing works
Flagged first, then by citations
1 of the 47 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Personalized Review Recommendation based on Users’ Aspect Sentiment
ChunLi, H., Jiang, W., Wu, J., & Wang, G. (2020) · ACM Transactions on Internet Technology · doi:10.1145/3414841 · Cited by 63 · see citing works
Li, Q., Li, X., Lee, B., & Kim, J. (2021) · Applied Sciences · doi:10.3390/app11188613 · Cited by 56 · see citing works
Trust Model for Online Reviews of Tourism Services and Evaluation of Destinations
Zelenka, J., Azubuike, T., & Pašková, M. (2021) · Administrative Sciences · doi:10.3390/admsci11020034 · Cited by 51 · see citing works
Theorizing vloggers’ approaches and practices in travel vlog production through grounded theory
Gholamhosseinzadeh, M. S. (2023) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2023.2164392 · Cited by 51 · see citing works
Reviewing the Past to Inform the Future: A Literature Review of Social Media in Tourism
Zarezadeh, Z. Z., Rastegar, R., & Ulrike Gretzel, B. (2018) · Czech Journal of Tourism · doi:10.1515/cjot-2018-0006 · Cited by 45 · see citing works
Wang, R., Chen, Z., Zhang, M. R., Li, Z., Liu, Z., Dang, Z., Yu, C., & Chen, X. '. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445547 · Cited by 42 · see citing works
Generating Product Descriptions from User Reviews
Novgorodov, S., Guy, I., Elad, G., & Radinsky, K. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313532 · Cited by 41 · see citing works
Self-citationUnsupervised tip-mining from customer reviews
Zhu, D., Lappas, T., & Zhang, J. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.01.011 · Cited by 32 · see citing works
Extracting API Tips from Developer Question and Answer Websites
Wang, S., Phan, N., Wang, Y., & Zhao, Y. (2019) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2019.00058 · Cited by 29 · see citing works
BITOUR: A Business Intelligence Platform for Tourism Analysis
Bustamante, A., Sebastiá, L., & Onaindía, E. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9110671 · Cited by 23 · see citing works
A Review Selection Method for Finding an Informative Subset from Online Reviews
Zhang, J., Wang, C., & Chen, G. (2020) · INFORMS journal on computing · doi:10.1287/ijoc.2019.0950 · Cited by 18 · see citing works
Generating Tips from Product Reviews
Hirsch, S., Novgorodov, S., Guy, I., & Nus, A. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441755 · Cited by 15 · see citing works
Self-citationMining graphs from travel blogs: a review in the context of tour planning
Haris, E., & Gan, K. H. (2017) · Information Technology & Tourism · doi:10.1007/s40558-017-0095-2 · Cited by 14 · see citing works
Descriptions from the Customers
Novgorodov, S., Guy, I., Elad, G., & Radinsky, K. (2020) · ACM Transactions on Internet Technology · doi:10.1145/3418202 · Cited by 12 · see citing works
Self-citationGuy, I. (2012) · Springer eBooks · doi:10.1007/978-1-0716-2197-4_22 · Cited by 12 · see citing works
Self-citationVoyageur: An Experiential Travel Search Engine
Evensen, S., Feng, A., Halevy, A. Y., Li, J., Li, V., Li, Y., Liu, H., Mihaila, G. A., Morales, J., Nuno, N., et al. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3314125 · Cited by 8 · see citing works
On the Identification of Suggestion Intents from Vietnamese Conversational Texts
Ngo, T.-L., Pham, K. L., Takeda, H., Pham, S. B., & Phan, X.-H. (2017) · International Symposium on Information and Communication Technology · doi:10.1145/3155133.3155201 · Cited by 6 · see citing works
Deep or simple models for semantic tagging?
Li, J., Li, Y., Wang, X., & Tan, W.-C. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3407790.3407844 · Cited by 6 · see citing works
Как подсказки веб-интерфейса индуцируют онлайн-доверие: обзор литературы
Косова, Е., & Горбунова, Е. (2023) · Психологические исследования · doi:10.54359/ps.v16i87.1353 · Cited by 3 · see citing works
Semantic model to extract tips from hotel reviews
Kumar, S. H., & Chowdary, C. R. (2020) · Electronic Commerce Research · doi:10.1007/s10660-020-09446-9 · Cited by 3 · see citing works
TipScreener: A Framework for Mining Tips for Online Review Readers
Luo, H., Song, W., & Zhou, W. (2022) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer17040087 · Cited by 3 · see citing works
Guy, I., & Radinsky, K. (2017) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3077136.3096469 · Cited by 2 · see citing works
Self-citationAspect-Based Personalized Review Ranking
ChunLi, H., & WenJun, J. (2018) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/smartworld.2018.00231 · Cited by 2 · see citing works
Learning to Generate Tips from Song Reviews
Zang, J., Gao, C., Chen, Y., Xu, R., Zhou, L., & Wang, X. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.01.049 · Cited by 2 · see citing works
Extracting Salient Facts from Company Reviews with Scarce Labels
Li, J.-F., Bhutani, N., Whedon, A., Chieh-Yang, H., Hruschka, E., & Suhara, Y. (2022) · AAAI Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD) · doi:10.3390/cmsf2022003009
Richen: Automated enrichment of Git documentation with usage examples and scenarios
Shen, C., Yang, W., Jia, H., Pan, M., & Zhou, Y. (2024) · Journal of Software Evolution and Process · doi:10.1002/smr.2662 · Cited by 1 · see citing works
Analyzing the Support Level for Tips Extracted from Product Reviews
Farber, M., Carmel, D., Kuchy, L., & Mejer, A. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531805 · Cited by 1 · see citing works
Self-citationDistilling Salient Reviews with Zero Labels
Huang, C.-Y., Li, J., Bhutani, N., Whedon, A., Junior, E. R. H., & Suhara, Y. (2022) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/2022.fever-1.3
Academic Article Classification Algorithm Based on Pre-trained Model and Keyword Extraction
Zhou, Z., Zheng, D., Qiu, Z., Lin, R., Wu, Z., & Yuan, C. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-4549-6_12
Stylistic Pattern Guided Tip Extraction from Music Reviews
Zang, J., & Dong, A. (2022) · International Conference on Data Intelligence and Security (ICDIS) · doi:10.1109/icdis55630.2022.00077
- Authors
- Guy, I., Mejer, A., Nus, A., & Raiber, F.
- Affiliations
- Ben-Gurion University of the Negev (IL)
- Yahoo Research, Haifa, Israel
- Technion – Israel Institute of Technology (IL)
- Venue
- Proceedings of the 26th International Conference on World Wide Web
- Published
- 2017-04-03
- Pages
- 987-996
- OpenAlex
- W2604359435
- Cited by
- 32
- References
- 47
- Citations by year
- 2024: 1 · 2023: 3 · 2022: 7 · 2021: 4 · 2020: 7 · 2019: 3 · 2018: 3 · 2017: 3 · 2012: 1
- FWCI
- 3.36
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- user-generated reviews, review summarization, template-based extraction, tourist attractions, TripAdvisor, information overload
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
