research-article

Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine

Authors:

Clement H. C. Leung,

Alice W. S. Chan,

Alfredo Milani,

Yuanxi LiAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 3, Issue 3

Article No.: 47, Pages 1 - 27

https://doi.org/10.1145/2168752.2168761

Published: 01 May 2012 Publication History

Abstract

Effective sharing of diverse social media is often inhibited by limitations in their search and discovery mechanisms, which are particularly restrictive for media that do not lend themselves to automatic processing or indexing. Here, we present the structure and mechanism of an adaptive search engine which is designed to overcome such limitations. The basic framework of the adaptive search engine is to capture human judgment in the course of normal usage from user queries in order to develop semantic indexes which link search terms to media objects semantics. This approach is particularly effective for the retrieval of multimedia objects, such as images, sounds, and videos, where a direct analysis of the object features does not allow them to be linked to search terms, for example, nontextual/icon-based search, deep semantic search, or when search terms are unknown at the time the media repository is built. An adaptive search architecture is presented to enable the index to evolve with respect to user feedback, while a randomized query-processing technique guarantees avoiding local minima and allows the meaningful indexing of new media objects and new terms. The present adaptive search engine allows for the efficient community creation and updating of social media indexes, which is able to instill and propagate deep knowledge into social media concerning the advanced search and usage of media resources. Experiments with various relevance distribution settings have shown efficient convergence of such indexes, which enable intelligent search and sharing of social media resources that are otherwise hard to discover.

References

[1]

Akbarinia, R., Pacitti, E., and Valduriez, P. 2007. Best position algorithms for top-k queries. In Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB’07). 495--506.

Digital Library

[2]

Ángeles Serrano, M., Maguitman, A., Marián Bogu N., Fortunato, S., and Vespignani, A. 2007. Decoding the structure of the WWW: A comparative analysis of Web crawls. ACM Trans. Web 1, 2, 10.

Digital Library

[3]

Anh, V. N. and Moffat, A. 2006. Pruning strategies for mixed-mode querying. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM’06). ACM, New York, NY, 190--197.

Digital Library

[4]

Azimi-Sadjadi, M., Salazar, J., and Srinivasan, S. 2009. An adaptable image retrieval system with relevance feedback using kernel machines and selective sampling. IEEE Trans. Image Process. 18, 7, 1645--1659.

Digital Library

[5]

Azzam, I. A., Leung, C. H. C., and Horwood, J. F. 2004. Implicit concept-based image indexing and retrieval. In Proceedings of the 10th International Multimedia Modeling Conference (MMM’04). Y.-P. P. Chen Ed., IEEE Computer Society, 354.

Digital Library

[6]

Azzam, I. A., Leung, C. H. C., and Horwood, J. F. 2005. A fuzzy expert system for concept-based image indexing and retrieval. In Proceedings of the 11th International Conference on Multi Media Modeling (MMM’05). Y.-P. P. Chen Ed., IEEE Computer Society, 452--457.

Digital Library

[7]

Badjio, E. F. and Poulet, F. 2005. User guidance: From theory to practice, the case of visual data mining. In Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’05). IEEE Computer Society, Los Alamitos, CA, 708--709.

Digital Library

[8]

Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., Ravichandran, D., and Aly, M. 2008. Video suggestion and discovery for YouTube: Taking random walks through the view graph. In Proceedings of the 17th International Conference on the World Wide Web (WWW’08). ACM, New York, NY, 895--904.

Digital Library

[9]

Bergmark, D. 2002. Collection synthesis. In Proceedings of the 2nd ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL’02). ACM, New York, NY, 253--262.

Digital Library

[10]

Bhattacharya, M. 2004. An informed operator approach to tackle diversity constraints in evolutionary search. In Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’04). Vol. 2, IEEE Computer Society, Los Alamitos, CA, 326.

Digital Library

[11]

Bian, J., Liu, Y., Agichtein, E., and Zha, H. 2008. Finding the right facts in the crowd: Factoid question answering over social media. In Proceedings of the 17th International Conference on the World Wide Web (WWW’08). ACM, New York, NY, 467--476.

Digital Library

[12]

Buckley, C., S. A. M. M. 1995. New retrieval approaches using smart : Trec 4. In Proceedings of the Text REtrieval Conference-4. 25--48.

[13]

Chakrabarti, D., Agarwal, D., and Josifovski, V. 2008. Contextual advertising by combining relevance with click feedback. In Proceedings of the 17th International Conference on the World Wide Web (WWW’08). ACM, New York, NY, 417--426.

Digital Library

[14]

Chau, M., Huang, Z., and Chen, H. 2003. Teaching key topics in computer science and information systems through a Web search engine project. J. Educ. Resour. Comput. 3, 3, 2.

Digital Library

[15]

Chen, Z. and Zhu, B. 2002. Some formal analysis of Rocchio’s similarity-based relevance feedback algorithm. Inform. Retriev. 5, 1, 61--86.

Digital Library

[16]

Cheng, E., Jing, F., Li, M., Ma, W.-Y., and Jin, H. 2006. Using implicit relevant feedback to advance Web image search. In Proceedings of the IEEE International Conference on Multimedia and Expo. 1773--1776.

[17]

Diligenti, M., Gori, M., and Maggini, M. 2002. Web page scoring systems for horizontal and vertical search. In Proceedings of the 11th International Conference on the World Wide Web (WWW’02). ACM, New York, NY, 508--516.

Digital Library

[18]

Dimou, C., Batzios, A., Symeonidis, A. L., and Mitkas, P. A. 2006. A multi-agent simulation framework for spiders traversing the semantic Web. In Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI’06). IEEE Computer Society, Los Alamitos, CA, 736--739.

Digital Library

[19]

Dwork, C., Kumar, R., Naor, M., and Sivakumar, D. 2001. Rank aggregation methods for the Web. In Proceedings of the 10th International Conference on the World Wide Web (WWW’01). ACM, New York, NY, 613--622.

Digital Library

[20]

Efthimiadis, E. N. 1996. Query expansion. Ann. Rev. Inform. Syst. Technol. 31, 121--187.

[21]

Eskandari, H., Rabelo, L., and Mollaghasemi, M. 2005. Multiobjective simulation optimization using an enhanced genetic algorithm. In Proceedings of the 37th Conference on Winter Simulation (WSC’05). 833--841.

Digital Library

[22]

Ferragina, P. and Manzini, G. 2005. Indexing compressed text. J. ACM 52, 4, 552--581.

Digital Library

[23]

Goldberg, D. E. and Holland, J. H. 1988. Genetic algorithms and machine learning. Mach. Learn. 3, 95--99. 10.1023/A:1022602019183.

[24]

Halvey, M. J. and Keane, M. T. 2007. Exploring social dynamics in online media sharing. In Proceedings of the 16th International Conference on the World Wide Web (WWW’07). ACM, New York, NY, 1273--1274.

Digital Library

[25]

Hargittai, E. 2004. Do you “google”? Understanding search engine use beyond the hype. First Monday 9, 3.

[26]

Haveliwala. 2003. Topic-sensitive pagerank: A context-sensitive ranking algorithm for Web search. IEEE Trans. Knowl. Data Eng. 15.

Digital Library

[27]

Haveliwala, T. H. 2002. Topic-sensitive pagerank. In Proceedings of the 11th International Conference on the World Wide Web (WWW’02). ACM, New York, NY, 517--526.

Digital Library

[28]

Hoashi, K., Zeitler, E., and Inoue, N. 2002. Implementation of relevance feedback for content-based music retrieval based on user prefences. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’02). ACM, New York, NY, 385--386.

Digital Library

[29]

Hoi, C.-H. and Lyu, M. R. 2004. A novel log-based relevance feedback technique in content-based image retrieval. In Proceedings of the 12th Annual ACM International Conference on Multimedia (MULTIMEDIA’04). ACM, New York, NY, 24--31.

Digital Library

[30]

Iwayama, M. 2000. Relevance feedback with a small number of relevance judgements: Incremental relevance feedback vs. document clustering. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’00). ACM, New York, NY, 10--16.

Digital Library

[31]

Jansen, T. and Wegener, I. 2006. On the local performance of simulated annealing and the (1+1) evolutionary algorithm. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO’06). ACM, New York, NY, 469--476.

Digital Library

[32]

John, A., Adamic, L., Davis, M., Nack, F., Shamma, D. A., and Seligmann, D. D. 2008. The future of online social interactions: What to expect in 2020. In Proceeding of the 17th International Conference on World Wide Web (WWW’08). ACM, New York, NY, 1255--1256.

Digital Library

[33]

Jung, S., Harris, K., Webster, J., and Herlocker, J. L. 2004. Serf: Integrating human recommendations with search. In Proceedings of the 13th ACM International Conference on Information and Knowledge Management (CIKM’04). ACM, New York, NY, 571--580.

Digital Library

[34]

Kaelbling, L. P., Littman, M. L., and Moore, A. P. 1996. Reinforcement learning: A survey. J. Artif. Intell. Res. 4, 237--285.

[35]

Kammenhuber, N., Luxenburger, J., Feldmann, A., and Weikum, G. 2006. Web search clickstreams. In Proceedings of the 6th ACM SIGCOMM Conference on Internet Measurement (IMC’06). ACM, New York, NY, 245--250.

Digital Library

[36]

Khopkar, Y., Spink, A., Giles, C. L., Shah, P., and Debnath, S. 2003. Search engine personalization, an exploratory study. First Monday 8, 7.

[37]

Kobayashi, M. and Takeda, K. 2000. Information retrieval on the Web. ACM Comput. Surv. 32, 2, 144--173.

Digital Library

[38]

Lee, H. T., Leonard, D., Wang, X., and Loguinov, D. 2008. Irlbot: Scaling to 6 billion pages and beyond. In Proceeding of the 17th International Conference on World Wide Web (WWW’08). ACM, New York, NY, 427--436.

Digital Library

[39]

Lerman, K. 2007. User participation in social media: Digg study. In Proceedings of the IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops (WI-IATW’07). IEEE Computer Society, Los Alamitos, CA, 255--258.

Digital Library

[40]

Leung, C. H. C. and Liu, J. 2007. Multimedia data mining and searching through dynamic index evolution. In Proceedings of the International Conference on Visual Information Systems (VISUAL’07). 298--309.

Digital Library

[41]

Li, H., Lee, W. C., Sivasubramaniam, A., and Giles, L. 2007. SearchGen: A synthetic workload generator for scientific literature digital libraries and search engines. In Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL’07). ACM, New York, NY, 137--146.

Digital Library

[42]

Lin, W.-H., Jin, R., and Hauptmann, A. 2003. Web image retrieval re-ranking with relevance model. In Proceedings of the IEEE/WIC International Conference on Web Intelligence (WI’03). IEEE Computer Society, Los Alamitos, CA, 242.

Digital Library

[43]

Liu, J., Feng, L., and Xing, Y. 2006. A pruning-based approach for supporting top-k join queries. In Proceedings of the 15th International Conference on the World Wide Web (WWW’06). ACM, New York, NY, 891--892.

Digital Library

[44]

Lourenço, A. G. and Belo, O. O. 2006. Catching Web crawlers in the act. In Proceedings of the 6th International Conference on Web Engineering (ICWE’06). ACM, New York, NY, 265--272.

Digital Library

[45]

Maass and Nowak. 2005. Text indexing with errors. In Proceedings of the 16th Symposium on Combinatorial Pattern Matching (CPM).

Digital Library

[46]

Martin, C. D. 2007. Blogger ethics and YouTube common sense. SIGCSE Bull. 39, 4, 11--12.

Digital Library

[47]

Milani, A., Leung, C., and Chan, A. 2008. Adaptive search engines as discovery games: An evolutionary approach. In Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia. ACM, New York, NY, 444--449.

Digital Library

[48]

Milani, A., Leung, C., and Chan, A. 2009. Community adaptive search engines. Int. J. Adv. Intell. Paradigms 1, 4, 432--443.

Digital Library

[49]

Minetou, C. G. 2005. Grouping users’ communities in an interactive Web-based learning system: A data mining approach. In Proceedings of the 5th IEEE International Conference on Advanced Learning Technologies (ICALT’05). IEEE Computer Society, Los Alamitos, CA, 474--475.

Digital Library

[50]

Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., and Bhattacharjee, B. 2007. Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement (IMC’07). ACM, New York, NY, 29--42.

Digital Library

[51]

Over, P., Leung, C. H. C., Ip, H. H. S., and Grubinger, M. 2004. Multimedia retrieval benchmarks. IEEE Multimedia 11, 2, 80--84.

Digital Library

[52]

Panda, N. and Chang, E. Y. 2006. Efficient top-k hyperplane query processing for multimedia information retrieval. In Proceedings of the 14th Annual ACM International Conference on Multimedia (MULTIMEDIA’06). ACM, New York, NY, 317--326.

Digital Library

[53]

Piszcz, A. and Soule, T. 2006. Dynamics of evolutionary robustness. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO’06). ACM, New York, NY, 871--878.

Digital Library

[54]

Rocchio, J. et al. 1971. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing, 313--323.

[55]

Rudinac, S., Larson, M., and Hanjalic, A. 2009. Semantic-theme-based video retrieval using multimodal pseudo-relevance feedback. In Proceedings of the 15th Annual Conference of the Advanced School for Computing and Imaging.

[56]

Rui, Y., Huang, T., Ortega, M., and Mehrotra, S. 1998. Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Trans. Circuits Syst. Video Technol. 8, 5, 644--655.

Digital Library

[57]

Saha, S. and Bandyopadhyay, S. 2007. A genetic clustering technique using a new line symmetry based distance measure. In Proceedings of the 15th International Conference on Advanced Computing and Communications (ADCOM’07). IEEE Computer Society, Alamitos, CA, 365--370.

Digital Library

[58]

Shaw, R. and Schmitz, P. 2006. Community annotation and remix: A research platform and pilot deployment. In Proceedings of the 1st ACM International Workshop on Human-Centered Multimedia (HCM’06). ACM, New York, NY, 89--98.

Digital Library

[59]

Shen, X. and Zhai, C. 2005. Active feedback in ad hoc information retrieval. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’05). ACM, New York, NY, 59--66.

Digital Library

[60]

Soliman, M. A., Ilyas, I. F., and Chang, K. C. C. 2008. Probabilistic top-k and ranking-aggregate queries. ACM Trans. Datab. Syst. 33, 3, 1--54.

Digital Library

[61]

Spertus, E., Sahami, M., and Buyukkokten, O. 2005. Evaluating similarity measures: A large-scale study in the orkut social network. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD’05). ACM, New York, NY, 678--684.

Digital Library

[62]

Sun, Y., Zhuang, Z., and Giles, C. L. 2007. A large-scale study of robots.txt. In Proceedings of the 16th International Conference on the World Wide Web (WWW’07). ACM, New York, NY, 1123--1124.

Digital Library

[63]

Tao, D., Li, X., and Maybank, S. 2007. Negative samples analysis in relevance feedback. IEEE Trans. Knowl. Data Eng. 568--580.

Digital Library

[64]

Tao, D., Tang, X., and Li, X. 2008. Which components are important for interactive image searching? IEEE Trans. Circuits Syst. Video Technol. 18, 1, 3--11.

Digital Library

[65]

Theobald, M., Weikum, G., and Schenkel, R. 2004. Top-k query evaluation with probabilistic guarantees. In Proceedings of the 13th International Conference on Very Large Data Bases (VLDB’04). 648--659.

Digital Library

[66]

Theobald, M., Schenkel, R., and Weikum, G. 2005. Efficient and self-tuning incremental query expansion for top-k query processing. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’05). ACM, New York, NY, 242--249.

Digital Library

[67]

Theobald, M., Bast, H., Majumdar, D., Schenkel, R., and Weikum, G. 2008. Topx: Efficient and versatile top-k query processing for semistructured data. VLDB J. 17, 1, 81--115.

Digital Library

[68]

Torjmen, M., Pinel-Sauvagnat, K., and Boughanem, M. 2008. Using pseudo-relevance feedback to improve image retrieval results. In Advances in Multilingual and Multimodal Information Retrieval. Lecture Notes in Computer Science, vol. 5152, Springer Berlin, 665--673.

Digital Library

[69]

Van Uden, M. 1998. Rocchio: Relevance feedback in learning classification algorithms. In Proceedings of the ACM SIGIR Conference.

[70]

Vinay, V., Wood, K., Milic-Frayling, N., and Cox, I. J. 2005. Comparing relevance feedback algorithms for Web search. In Special Interest Tracks and Posters of the 14th International Conference on the World Wide Web (WWW’05). ACM, New York, NY, 1052--1053.

Digital Library

[71]

Vlachou, A., Doulkeridis, C., Nørvåg, K., and Vazirgiannis, M. 2008. On efficient top-k query processing in highly distributed environments. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’08). ACM, New York, NY, 753--764.

Digital Library

[72]

White, R. W. and Kelly, D. 2006. A study on the effects of personalization and task information on implicit feedback performance. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM’06). ACM, New York, NY, 297--306.

Digital Library

[73]

Widyantoro, D. H., Ioerger, T. R., and Yen, J. 2003. Tracking changes in user interests with a few relevance judgments. In Proceedings of the 12th International Conference on Information and Knowledge Management (CIKM’03). ACM, New York, NY, 548--551.

Digital Library

[74]

Wong, R. C. F. and Leung, C. H. C. 2008. Automatic semantic annotation of real world Web images. IEEE Trans. Pattern Anal. Mach. Intell. 30, 11, 1933--1944.

Digital Library

[75]

Yan, R., Hauptmann, E., and Jin, R. 2003. Multimedia search with pseudo-relevance feedback. In Proceedings of the International Conference on Image and Video Retrieval. 238--247.

Digital Library

[76]

Yang, S. and Şima Uyar. 2006. Adaptive mutation with fitness and allele distribution correlation for genetic algorithms. In Proceedings of the ACM Symposium on Applied Computing (SAC’06). ACM, New York, NY, 940--944.

Digital Library

[77]

Yang, Y., Wu, F., Xu, D., Zhuang, Y., and Chia, L.-T. 2010. Cross-media retrieval using query dependent search methods. Pattern Recogn. 43, 8, 2927--2936.

Digital Library

[78]

Zhang, C., Chai, J. Y., and Jin, R. 2005. User term feedback in interactive text-based image retrieval. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’05). ACM, New York, NY, 51--58.

Digital Library

[79]

Zhang, R. and Zhang, Z. 2007. Effective image retrieval based on hidden concept discovery in image database. IEEE Trans. Image Process. 16, 2, 562--572.

Digital Library

[80]

Zhou, X. S. and Huang, T. S. 2003. Relevance feedback in image retrieval: A comprehensive review. Multimedia Syst. 8, 536--544. 10.1007/s00530-002-0070-3.

[81]

Zhuang, Y., Yang, Y., and Wu, F. 2008. Mining semantic correlation of heterogeneous multimedia data for cross-media retrieval. IEEE Trans. Multimedia 10, 2, 221--229.

Digital Library

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Index Terms

Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine
1. Information systems
  1. Information retrieval
    1. Document representation

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 3, Issue 3

May 2012

384 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2168752

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Publication History

Published: 01 May 2012

Accepted: 01 February 2011

Revised: 01 December 2010

Received: 01 April 2010

Published in TIST Volume 3, Issue 3

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Martinez-Castano RLosada DPichel J(2022)Real-Time Focused Extraction of Social Media UsersIEEE Access10.1109/ACCESS.2022.316897710(42607-42622)Online publication date: 2022
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Kuang NLeung C(2019)Performance Effectiveness of Multimedia Information Search Using the Epsilon-Greedy Algorithm2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)10.1109/ICMLA.2019.00160(929-936)Online publication date: Dec-2019
https://doi.org/10.1109/ICMLA.2019.00160
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