Cited by 1,567Information Retrieval and Search BehaviorWeb Data Mining and AnalysisRecommender Systems and Techniques

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Topic-sensitive PageRank

Haveliwala, Taher H.·2002·Proceedings of the 11th international conference on World Wide Web

Open this paper in CoChat — free account

Abstract

In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. By using these (precomputed) biased PageRank vectors to generate query-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared.

Integrity signals

Registry

Crossref · 10.1145/511446.511513

Cites

15 of 15 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Authoritative sources in a hyperlinked environment
Kleinberg, J. M. (1999) · Journal of the ACM · doi:10.1145/324133.324140 · Cited by 9,032
Randomized Algorithms
Motwani, R., & Raghavan, P. (1995) · Cambridge University Press eBooks · doi:10.1017/cbo9780511814075 · Cited by 5,098
Rank aggregation methods for the Web
Dwork, C., Kumar, R., Naor, M., & Sivakumar, D. (2001) · International World Wide Web Conference (WWW) · doi:10.1145/371920.372165 · Cited by 1,825
Scaling personalized web search
Jeh, G., & Widom, J. (2003) · Proceedings of the twelfth international conference on World Wide Web - WWW '03 · doi:10.1145/775152.775191 · Cited by 1,195
Placing search in context
Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., & Ruppin, E. (2001) · International World Wide Web Conference (WWW) · doi:10.1145/371920.372094 · Cited by 841
Automatic resource compilation by analyzing hyperlink structure and associated text
Chakrabarti, S., Dom, B., Raghavan, P., Rajagopalan, S., Gibson, D., & Kleinberg, J. M. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00087-7 · Cited by 699
Improved Algorithms for Topic Distillation in a Hyperlinked Environment
Bharat, K. A., & Henzinger, M. R. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130367 · Cited by 678
Improved algorithms for topic distillation in a hyperlinked environment
Bharat, K. A., & Henzinger, M. R. (1998) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/290941.290972 · Cited by 443
The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank
Richardson, M., & Domingos, P. (2001) · Cited by 356

Flagged first, then by citations

Show all 15 references

Research works printed but not linked: 6

10.5555/297805.297827 · DOI
10.5555/297805.297821 · DOI
10.5555/347319.346288 · DOI
10.5555/211390 · DOI
Larry Page . PageRank : Bringing order to the web . Stanford Digital Libraries Working Paper , 1997 . Larry Page. PageRank: Bringing order to the web. Stanford Digital Libraries Working Paper, 1997.
10.5555/347319.346421 · DOI

Sorted by citations

The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
LightGCN
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401063 · Cited by 5,300
Mining of Massive Datasets
Rajaraman, A., & Ullman, J. D. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9781139058452 · Cited by 2,307
TwitterRank
Weng, J., Lim, E., Jiang, J., & He, Q. (2010) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1718487.1718520 · Cited by 1,780
Fast Random Walk with Restart and Its Applications
Tong, H., Faloutsos, C., & Pan, J.-Y. (2006) · Proceedings · doi:10.1109/icdm.2006.70 · Cited by 1,218
Scaling personalized web search
Jeh, G., & Widom, J. (2003) · Proceedings of the twelfth international conference on World Wide Web - WWW '03 · doi:10.1145/775152.775191 · Cited by 1,195
Combating Web Spam with TrustRank
Gyöngyi, Z., García-Molina, H., & Pedersen, J. (2004) · Elsevier eBooks · doi:10.1016/b978-012088469-8.50052-8 · Cited by 1,087
Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search
Haveliwala, T. H. (2003) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2003.1208999 · Cited by 1,046
Self-citation
Supervised random walks
Bäckström, L., & Leskovec, J. (2011) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1935826.1935914 · Cited by 989
Link mining
Getoor, L. C., & Diehl, C. (2005) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1117454.1117456 · Cited by 986
Deeper Inside PageRank
Langville, A. N., & Meyer, C. D. (2004) · Internet Mathematics · doi:10.1080/15427951.2004.10129091 · Cited by 885
A study on similarity and relatedness using distributional and WordNet-based approaches
Agirre, E., Alfonseca, E., Hall, K., Kravalová, J., PAŞCA, M. A., & Soroa, A. (2009) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1620754.1620758 · Cited by 867
Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions
Shen, W., Wang, J., & Han, J. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2327028 · Cited by 759
Entity Linking meets Word Sense Disambiguation: a Unified Approach
Moro, A., Raganato, A., & Roberto Navigli, A. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00179 · Cited by 745
Random walks and diffusion on networks
Masuda, N., Porter, M. A., & Lambiotte, R. (2017) · Physics Reports · doi:10.1016/j.physrep.2017.07.007 · Cited by 701
PageRank Beyond the Web
Gleich, D. F. (2015) · SIAM Review · doi:10.1137/140976649 · Cited by 656
Adaptive web search based on user profile constructed without any effort from users
Sugiyama, K., Hatano, K., & Yoshikawa, M. (2004) · International World Wide Web Conference (WWW) · doi:10.1145/988672.988764 · Cited by 645
Clustering and Community Detection in Directed Networks: A SurveyPreprint
Malliaros, F. D., & Vazirgiannis, M. (2016) · Cited by 637
Managing and Mining Graph Data
Aggarwal, C. C., & Wang, H. (2010) · The Kluwer international series on advances in database systems · doi:10.1007/978-1-4419-6045-0 · Cited by 609
Clustering and community detection in directed networks: A survey
Malliaros, F. D., & Vazirgiannis, M. (2013) · Physics Reports · doi:10.1016/j.physrep.2013.08.002 · Cited by 594
Inside PageRank
Bianchini, M., Gori, M., & Scarselli, F. (2005) · ACM Transactions on Internet Technology · doi:10.1145/1052934.1052938 · Cited by 539
Personalizing PageRank for word sense disambiguation
Agirre, E., & Soroa, A. (2009) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/1609067.1609070 · Cited by 537
A large-scale evaluation and analysis of personalized search strategies
Dou, Z., Song, R., & Wen, J.-R. (2007) · International World Wide Web Conference (WWW) · doi:10.1145/1242572.1242651 · Cited by 533
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
Sun, H., Dhingra, B., Zaheer, M., Mazaitis, K., Salakhutdinov, R., & Cohen, W. W. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1455 · Cited by 554
Automatic multimedia cross-modal correlation discovery
Pan, J.-Y., Yang, H.-J., Faloutsos, C., & Duygulu, P. (2004) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1014052.1014135 · Cited by 484
ObjectRank
Balmin, A., Hristidis, V., & Yannis Papakonstantinou, D. (2004) · Elsevier eBooks · doi:10.1016/b978-012088469-8.50051-6 · Cited by 480
Random Walk Inference and Learning in A Large Scale Knowledge Base
Lao, N., Mitchell, T. M., & Cohen, W. W. (2018) · Figshare · doi:10.1184/r1/6476267 · Cited by 479
Reweighted Random Walks for Graph Matching
Cho, M., Lee, J., & Lee, K. M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15555-0_36 · Cited by 477
A Survey on PageRank Computing
Berkhin, P. (2005) · Internet Mathematics · doi:10.1080/15427951.2005.10129098 · Cited by 470
TriRank
He, X., Chen, T., Kan, M., & Chen, X. (2015) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2806416.2806504 · Cited by 465
LifeLogging: Personal Big Data
Gurrin, C. G., Smeaton, A. F., & Doherty, A. (2014) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000033 · Cited by 459
Web page classification
Qi, X., & Davison, B. D. (2009) · ACM Computing Surveys · doi:10.1145/1459352.1459357 · Cited by 444
A Survey of Eigenvector Methods for Web Information Retrieval
Langville, A. N., & Meyer, C. D. (2005) · SIAM Review · doi:10.1137/s0036144503424786 · Cited by 422
Algorithms for estimating relative importance in networks
White, S., & Smyth, P. (2003) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/956750.956782 · Cited by 418
Extrapolation methods for accelerating PageRank computations
Kamvar, S. D., Haveliwala, T. H., Manning, C. D., & Golub, G. H. (2003) · International World Wide Web Conference (WWW) · doi:10.1145/775152.775190 · Cited by 410
Self-citation
Automatic identification of user interest for personalized search
Qiu, F., & Cho, J. J. (2006) · International World Wide Web Conference (WWW) · doi:10.1145/1135777.1135883 · Cited by 399
Automatic identification of user goals in Web search
Lee, U., Liu, Z., & Cho, J. J. (2005) · International World Wide Web Conference (WWW) · doi:10.1145/1060745.1060804 · Cited by 399
Graph Neural Networks for Natural Language Processing: A Survey
Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., & Long, B. (2023) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000096 · Cited by 417
Random walk with restart: fast solutions and applications
Tong, H., Faloutsos, C., & Pan, J.-Y. (2007) · Knowledge and Information Systems · doi:10.1007/s10115-007-0094-2 · Cited by 381
Temporal recommendation on graphs via long- and short-term preference fusion
Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., & Sun, J. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835896 · Cited by 377
Discovering authorities in question answer communities by using link analysis
Jurczyk, P., & Agichtein, E. (2007) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1321440.1321575 · Cited by 375
From Word To Sense Embeddings: A Survey on Vector Representations of Meaning
Camacho-Collados, J., & Pilehvar, M. T. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11259 · Cited by 393
Social Network Analysis and Mining for Business Applications
Bonchi, F., Castillo, C., Gionis, A., & Jaimes, A. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961194 · Cited by 374
A Survey of Signed Network Mining in Social Media
Tang, J., Chang, Y., Aggarwal, C. C., & Liu, H. (2016) · ACM Computing Surveys · doi:10.1145/2956185 · Cited by 367
Fast incremental and personalized PageRank
Bahmani, B., Chowdhury, A., & Goel, A. (2010) · Proceedings of the VLDB Endowment · doi:10.14778/1929861.1929864 · Cited by 350
Fast discovery of connection subgraphs
Faloutsos, C., McCurley, K. S., & Tomkins, A. (2004) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1014052.1014068 · Cited by 346
Understanding and combating link farming in the twitter social network
Ghosh, S., Viswanath, B., Kooti, F., Sharma, N. K., Korlam, G., Benevenuto, F., Ganguly, N., & Gummadi, K. P. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187836.2187846 · Cited by 355
The Perron-Frobenius theorem: some of its applications
Pillai, S. U., Suel, T., & Cha, S. (2005) · IEEE Signal Processing Magazine · doi:10.1109/msp.2005.1406483 · Cited by 338
Automatic Keyphrase Extraction via Topic Decomposition
Liu, Z., Huang, W., Zheng, Y., & Sun, M. (2010) · Cited by 338
CubeSVD
Sun, J.-T., Zeng, H.-J., Liu, H., Lu, Y., & Chen, Z. (2005) · International World Wide Web Conference (WWW) · doi:10.1145/1060745.1060803 · Cited by 336
Abstractive Document Summarization with a Graph-Based Attentional Neural Model
Tan, J., Wan, X., & Xiao, J. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1108 · Cited by 331
Web search personalization with ontological user profiles
Sieg, A., Mobasher, B., & Burke, R. (2007) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1321440.1321515 · Cited by 322
Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison
Raganato, A., Camacho-Collados, J., & Roberto Navigli, A. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1010 · Cited by 320
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
Sun, J., Qu, H., Chakrabarti, D., & Faloutsos, C. (2006) · Proceedings · doi:10.1109/icdm.2005.103 · Cited by 313
Challenging the long tail recommendation
Yin, H., Cui, B., Li, J., Yao, J., & Chen, C. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2311906.2311916 · Cited by 343
Link analysis ranking: algorithms, theory, and experiments
Borodin, A., Roberts, G. O., Rosenthal, J. S., & Tsaparas, P. (2005) · ACM Transactions on Internet Technology · doi:10.1145/1052934.1052942 · Cited by 307
Personalized query expansion for the web
Chirita, P. A., Firan, C. S., & Nejdl, W. (2007) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1277741.1277746 · Cited by 290
Towards Scaling Fully Personalized PageRank: Algorithms, Lower Bounds, and Experiments
Fogaras, D., Rácz, B., Csalogány, K., & Sarlós, T. (2005) · Internet Mathematics · doi:10.1080/15427951.2005.10129104 · Cited by 283
A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks
Antonakaki, D., Fragopoulou, P., & Ioannidis, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114006 · Cited by 290
ItemRank: a random-walk based scoring algorithm for recommender engines
Gori, M., & Pucci, A. (2007) · Use Siena air (University of Siena) · Cited by 277
Exploring folksonomy for personalized search
Xu, S., Bao, S., Fei, B., Su, Z., & Yu, Y. H. (2008) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1390334.1390363 · Cited by 275
Adaptive methods for the computation of PageRank
Kamvar, S. D., Haveliwala, T. H., & Golub, G. H. (2004) · Linear Algebra and its Applications · doi:10.1016/j.laa.2003.12.008 · Cited by 270
Self-citation
Using ODP metadata to personalize search
Chirita, P. A., Nejdl, W., Paiu, R., & Kohlschütter, C. (2005) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1076034.1076067 · Cited by 269
Neural Abstractive Text Summarization with Sequence-to-Sequence Models
Shi, T., Keneshloo, Y., Ramakrishnan, N., & Reddy, C. K. A. (2021) · ACM/IMS Transactions on Data Science · doi:10.1145/3419106 · Cited by 278
Graph-based Natural Language Processing and Information Retrieval
Mihalcea, R. F., & Radev, D. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511976247 · Cited by 261
Center-piece subgraphs
Tong, H., & Faloutsos, C. (2006) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150448 · Cited by 260
Random Walks for Knowledge-Based Word Sense Disambiguation
Agirre, E., de Lacalle, O. L., & Soroa, A. (2013) · Computational Linguistics · doi:10.1162/coli_a_00164 · Cited by 258
Higher-Order Web Link Analysis Using Multilinear Algebra
Kolda, T. G., Bader, B. W., & Kenny, J. P. (2006) · Proceedings · doi:10.1109/icdm.2005.77 · Cited by 257
Embeddings for Word Sense Disambiguation: An Evaluation Study
Iacobacci, I., Pilehvar, M. T., & Navigli, R. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1085 · Cited by 253
To personalize or not to personalize
Teevan, J. B., Dumais, S., & Liebling, D. J. (2008) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1390334.1390364 · Cited by 251
Survey on web spam detection
Спирин, Н. А., & Han, J. (2012) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2207243.2207252 · Cited by 250
EigenRank
Liu, N., & Yang, Q. (2008) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1390334.1390351 · Cited by 252
Mining topic-level influence in heterogeneous networks
Liu, L., Tang, J., Han, J., Jiang, M., & Yang, S. (2010) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1871437.1871467 · Cited by 248
Personalized social search based on the user's social network
Carmel, D., Zwerdling, N., Guy, I., Ofek-Koifman, S., Har’El, N., Ronen, I., Uziel, E., Yogev, S., & Chernov, S. (2009) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1645953.1646109 · Cited by 244
Finding and Ranking Knowledge on the Semantic Web
Ding, L., Pan, R., Finin, T., Joshi, A., Peng, Y., & Kolari, P. (2005) · Lecture notes in computer science · doi:10.1007/11574620_14 · Cited by 236
Clustering the tagged web
Ramage, D., Heymann, P., Manning, C. D., & García-Molina, H. (2009) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1498759.1498809 · Cited by 228
Automatic performance tuning of sparse matrix kernels
Vuduc, R. W., & Demmel, J. W. (2003) · Cited by 226
InfoGather
Yakout, M., Ganjam, K., Chakrabarti, K., & Chaudhuri, S. (2012) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2213836.2213848 · Cited by 224
ProjE: Embedding Projection for Knowledge Graph Completion
Shi, B., & Weninger, T. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10677 · Cited by 218
Ranking the web frontier
Eiron, N., McCurley, K. S., & Tomlin, J. A. (2004) · International World Wide Web Conference (WWW) · doi:10.1145/988672.988714 · Cited by 216
A personalized search engine based on web-snippet hierarchical clustering
Ferragina, P., & Gullì, A. (2005) · International World Wide Web Conference (WWW) · doi:10.1145/1062745.1062760 · Cited by 210
Trust-based recommendation systems
Andersen, R., Borgs, C., Chayes, J., Feige, U., Flaxman, A. D., Kalai, A. T., Mirrokni, V., & Tennenholtz, M. (2008) · International World Wide Web Conference (WWW) · doi:10.1145/1367497.1367525 · Cited by 210
Scalable Graph Embedding for Asymmetric Proximity
Zhou, C., Liu, Y., Liu, X., Liu, Z., & Gao, J. Y. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10878 · Cited by 209
Large Language Models on Graphs: A Comprehensive Survey
Jin, B., Liu, G., Han, C., Jiang, M., Ji, H., & Han, J. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3469578 · Cited by 278
A review of keyphrase extraction
Papagiannopoulou, E., & Tsoumakas, G. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1339 · Cited by 212
TripleRank: Ranking Semantic Web Data by Tensor Decomposition
Franz, T., Schultz, A., Sizov, S., & Staab, S. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04930-9_14 · Cited by 206
Modeling and broadening temporal user interest in personalized news recommendation
Li, L., Li, Z., Yang, F., & Li, T. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.11.020 · Cited by 199
Dynamic personalized pagerank in entity-relation graphs
Chakrabarti, S. (2007) · International World Wide Web Conference (WWW) · doi:10.1145/1242572.1242650 · Cited by 197
Mining query logs: turning search usage data into knowledge
Silvestri, F. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000013 · Cited by 194
Multimedia search reranking
Mei, T., Rui, Y., Li, S., & Tian, Q. C. (2014) · ACM Computing Surveys · doi:10.1145/2536798 · Cited by 196
Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine
Smyth, B., Balfe, E., Freyne, J., Briggs, P. M., Coyle, M., & Boydell, O. (2004) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-004-5270-4 · Cited by 193
A genome-wide systems analysis reveals strong link between colorectal cancer and trimethylamine N-oxide (TMAO), a gut microbial metabolite of dietary meat and fat
Xu, R., Wang, Q., & Li, L. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s7-s4 · Cited by 193
Personalized Search on the World Wide Web
Micarelli, A., Gasparetti, F., Sciarrone, F., & Gauch, S. (2007) · Springer eBooks · doi:10.1007/978-3-540-72079-9_6 · Cited by 192
Supervised Random Walks: Predicting and Recommending Links in Social NetworksPreprint
Bäckström, L., & Leskovec, J. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1011.4071 · Cited by 192
Cognos
Ghosh, S., Sharma, N. K., Benevenuto, F., Ganguly, N., & Gummadi, K. P. (2012) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2348283.2348361 · Cited by 190
Hustle and Brand: The Sociotechnical Shaping of Influence
Carter, D. (2016) · Social Media + Society · doi:10.1177/2056305116666305 · Cited by 189
CQArank
Yang, L., Qiu, M., Gottipati, S., Zhu, F., Jiang, J., Sun, H., & Chen, Z. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505720 · Cited by 187
A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks
Noulas, A., Scellato, S., Lathia, N., & Mascolo, C. (2012) · 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing · doi:10.1109/socialcom-passat.2012.70 · Cited by 184
Hyper-graph matching via reweighted random walks
Lee, J., Cho, M., & Lee, K. M. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995387 · Cited by 183
iCrowd
Fan, J., Li, G., Ooi, B. C., Tan, K., & Feng, J. (2015) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2723372.2750550 · Cited by 182

Authors: Haveliwala, T. H.
Affiliations: Stanford University (US)
Corresponding author: Haveliwala, Taher H.
Venue: Proceedings of the 11th international conference on World Wide Web
Publisher: ACM
Published: 2002-05-07
Pages: 517-526
DOI: 10.1145/511446.511513
OpenAlex: W2170344111
Cited by: 1,567
References: 15
Citations by year: 2026: 32 · 2025: 22 · 2024: 17 · 2023: 32 · 2022: 21 · 2021: 44 · 2020: 54 · 2019: 67 · 2018: 79 · 2017: 81 · 2016: 87 · 2015: 79 · 2014: 97 · 2013: 78 · 2012: 93
FWCI: 44.04
Type: conference paper
Language: en
Open access: closed
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: International World Wide Web Conference (WWW) (publishedVersion)
Topics: Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
Keywords: PageRank, web page ranking, search result ranking, context-aware retrieval, web link structure

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

Topic-sensitive PageRank

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Citations

Cites

Cited by

Related

Metadata

Record

About these checks