{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T16:57:17Z","timestamp":1790960237983,"version":"4.1.0"},"reference-count":23,"publisher":"Association for Computing Machinery (ACM)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2015,6]]},"abstract":"<jats:p>\n            Advertising in social network has become a multi-billion-dollar industry. A main challenge is to identify key influencers who can effectively contribute to the dissemination of information. Although the influence maximization problem, which finds a seed set of\n            <jats:italic>k<\/jats:italic>\n            most influential users based on certain propagation models, has been well studied, it is not target-aware and cannot be directly applied to online advertising. In this paper, we propose a new problem, named Keyword-Based Targeted Influence Maximization (KB-TIM), to find a seed set that maximizes the expected influence over users who are relevant to a given advertisement. To solve the problem, we propose a sampling technique based on weighted reverse influence set and achieve an approximation ratio of (1 \u2212 1\/\n            <jats:italic>e \u2212 \u03b5<\/jats:italic>\n            ). To meet the instant-speed requirement, we propose two disk-based solutions that improve the query processing time by two orders of magnitude over the state-of-the-art solutions, while keeping the theoretical bound. Experiments conducted on two real social networks confirm our theoretical findings as well as the efficiency. Given an advertisement with 5 keywords, it takes only 2 seconds to find the most influential users in a social network with billions of edges.\n          <\/jats:p>","DOI":"10.14778\/2794367.2794376","type":"journal-article","created":{"date-parts":[[2015,7,30]],"date-time":"2015-07-30T14:37:34Z","timestamp":1438267054000},"page":"1070-1081","source":"Crossref","is-referenced-by-count":158,"title":["Real-time targeted influence maximization for online advertisements"],"prefix":"10.14778","volume":"8","author":[{"given":"Yuchen","family":"Li","sequence":"first","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kian-Lee","family":"Tan","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2015,6]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.122"},{"key":"e_1_2_1_2_1","volume-title":"Influence maximization in social networks: Towards an optimal algorithmic solution. CoRR, abs\/1212.0884","author":"Borgs C.","year":"2012","unstructured":"C. Borgs , M. Brautbar , J. T. Chayes , and B. Lucier . Influence maximization in social networks: Towards an optimal algorithmic solution. CoRR, abs\/1212.0884 , 2012 . C. Borgs, M. Brautbar, J. T. Chayes, and B. Lucier. Influence maximization in social networks: Towards an optimal algorithmic solution. CoRR, abs\/1212.0884, 2012."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735703.2735706"},{"key":"e_1_2_1_4_1","volume-title":"Efficient topic-aware influence maximization using preprocessing. CoRR, abs\/1403.0057","author":"Chen W.","year":"2014","unstructured":"W. Chen , T. Lin , and C. Yang . Efficient topic-aware influence maximization using preprocessing. CoRR, abs\/1403.0057 , 2014 . W. Chen, T. Lin, and C. Yang. Efficient topic-aware influence maximization using preprocessing. CoRR, abs\/1403.0057, 2014."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835934"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557047"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.118"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-0000(03)00026-6"},{"key":"e_1_2_1_9_1","volume-title":"Using complex systems analysis to advance marketing theory development","author":"Goldenberg J.","year":"2001","unstructured":"J. Goldenberg , B. Libai , and Muller. Using complex systems analysis to advance marketing theory development . Academy of Marketing Science Review , 2001 . J. Goldenberg, B. Libai, and Muller. Using complex systems analysis to advance marketing theory development. Academy of Marketing Science Review, 2001."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1963192.1963217"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1086\/226707"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964858.1964870"},{"key":"e_1_2_1_13_1","first-page":"295","volume-title":"EDBT","author":"Aslay \u00c7igdem","year":"2014","unstructured":"\u00c7igdem Aslay , N. Barbieri , F. Bonchi , and R. A. Baeza-Yates . Online topic-aware influence maximization queries . In EDBT , pages 295 -- 306 , 2014 . \u00c7igdem Aslay, N. Barbieri, F. Bonchi, and R. A. Baeza-Yates. Online topic-aware influence maximization queries. In EDBT, pages 295--306, 2014."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.79"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956769"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/3120676.3120705"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281239"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/PASSAT\/SocialCom.2011.152"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2588561"},{"key":"e_1_2_1_20_1","volume-title":"Real-time targeted influence maximization for online advertisements. Technical report","author":"Li Y.","year":"2015","unstructured":"Y. Li , D. Zhang , and K.-L. Tan . Real-time targeted influence maximization for online advertisements. Technical report , 2015 . http:\/\/www.comp.nus.edu.sg\/~a0047194\/. Y. Li, D. Zhang, and K.-L. Tan. Real-time targeted influence maximization for online advertisements. Technical report, 2015. http:\/\/www.comp.nus.edu.sg\/~a0047194\/."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2593670"},{"key":"e_1_2_1_22_1","volume-title":"Approximation algorithms","author":"Vazirani V. V.","year":"2001","unstructured":"V. V. Vazirani . Approximation algorithms . Springer , 2001 . V. V. Vazirani. Approximation algorithms. 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