Discovery of Web user communities and their role in personalization
- 1.2k Downloads
- 4 Citations
Abstract
One of the major innovations in personalization in the last 20 years was the injection of social knowledge into the model of the user. The user is not considered an isolated individual any more, but a member of one or more communities. User communities have been facilitated by the striking advancements of electronic communications and in particular the penetration of the Web into people’s everyday routine. Communities arise in a number of different ways. Social networking tools typically allow users to proactively connect to each other. Alternatively, data mining tools discover communities of connected Web sites or communities of Web users. In this article, we focus on the latter type of community, which is commonly mined from logs of users’ activity on the Web. We recall how this process has been used to model the users’ interests and personalize Web applications. Collaborative filtering and recommendation are the most widely used forms of community-driven personalization. However, we examine a range of other interesting alternatives that are worth investigating further. This effort leads us naturally to the recent developments on the Web and particularly the advent of the social Web. We explain how this development draws together the different viewpoints on Web communities and introduces new opportunities for community-based personalization. In particular, we propose the concept of active user community and show how this relates to recent efforts on mining social networks and social media.
Keywords
User communities Web mining Web personalization Web communities Social networksReferences
- Adamic L.A., Adar E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)CrossRefGoogle Scholar
- Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, pp. 207–216 (1993)Google Scholar
- Almeida, R.B., Almeida, V.A.F.: A community-aware search engine. In: Feldman, S.I., Uretsky, M., Najork, M., Wills, C.E. (eds.) Proceedings of the Thirteenth International Conference on World Wide Web (WWW), New York, pp. 413–421 (2004)Google Scholar
- Anaya, A.R., Boticario, J.: Clustering learners according to their collaboration. In: Borges, M.R.S., Shen, W., Pino, J.A., Barthès, J.P.A., Luo, J., Ochoa, S.F., Yong, J. (eds.) Proceedings of the 13th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Santiago, pp. 540–545 (2009)Google Scholar
- Ardissono, L., Kuflik, T., Petrelli, D.: Personalization in cultural heritage: the road travelled and the one ahead. User Model. User-Adap. Inter. 22(1–2), 73–99 (2012)Google Scholar
- Baeza-Yates R.A., Ribeiro-Neto B.A.: Modern Information Retrieval. ACM Press, Addison-Wesley (1999)Google Scholar
- Berners-Lee T.: Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web. Harper, San Francisco (2000)Google Scholar
- Blei D.M., Ng A.Y., Jordan M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
- Bouras C., Igglesis V., Kapoulas V., Tsiatsos T.: A web-based virtual community. Int. J. Web Based Comun. 1(2), 127–139 (2005)CrossRefGoogle Scholar
- Boyd, D.M., Ellison, N.B.: Social network sites: definition, history, and scholarship. J. Comput. Mediat. Commun. 13(1), 210–230 (2008)CrossRefGoogle Scholar
- Burke R.D.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W (eds) The Adaptive Web, Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, vol. 4321., pp. 377–408. Springer, Heidelberg (2007)Google Scholar
- Caragea, D., Bahirwani, V., Aljandal, W., Hsu, W.H.: Ontology-based link prediction in the livejournal social network. In: Bulitko, V., Beck, J.C. (eds.) Proceedings of the Eighth Symposium on Abstraction, Reformulation, and Approximation (SARA), Lake Arrowhead (2009)Google Scholar
- Chen W., Liu Z., Sun X., Wang Y.: A game-theoretic framework to identify overlapping communities in social networks. Data Min. Knowl. Discov. 21(2), 224–240 (2010)MathSciNetCrossRefGoogle Scholar
- Cooley, R., Mobasher, B., Srivastava, J.: Web mining: information and pattern discovery on the world wide web. In: Proceedings of the Ninth International Conference on Tools with Artificial Intelligence (ICTAI), Newport Beach, pp. 558–567 (1997)Google Scholar
- Cooley R., Mobasher B., Srivastava J.: Data preparation for mining world wide web browsing patterns. Knowl. Inform. Syst. 1(1), 5–32 (1999)Google Scholar
- Cui H., Wen J.R., Nie J.Y., Ma W.Y.: Query expansion by mining user logs. IEEE Tran. Knowl. Data Eng. 15(4), 829–839 (2003)CrossRefGoogle Scholar
- Desmarais, M.C., Baker, R.S.J.d.: A review of recent advances in learner and skill modeling in intelligent learning environments. User Model. User-Adap. Inter. 22(1–2), 9–38 (2012)Google Scholar
- Du N., Wang B., Wu B.: Community detection in complex networks. J. Comput. Sci. Technol. 23(4), 672–683 (2008)MathSciNetCrossRefGoogle Scholar
- Farzan R., Brusilovsky P: Social navigation support in a course recommendation system. In: Wade, V.P., Ashman, H., Smyth, B (eds) Adaptive Hypermedia and Adaptive Web-Based Systems, Proceedings of the Fourth International Conference (AH). Lecture Notes in Computer Science, vol. 4018, pp. 91–100. Springer, Dublin (2006)Google Scholar
- Fink J., Kobsa A.: User modeling for personalized city tours. Artif. Intell. Rev. 18(1), 33–74 (2002)zbMATHCrossRefGoogle Scholar
- Flake, G.W., Lawrence, S., Giles, C.L.: Efficient identification of web communities. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Boston, pp. 150–160 (2000)Google Scholar
- Fortunato S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)MathSciNetCrossRefGoogle Scholar
- Gaudioso E., Boticario J.: User modeling on adaptive web-based learning communities. In: Palade, V., Howlett, R.J., Jain, L.C (eds) Proceedings of the Seventh International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES). Lecture Notes in Computer Science, vol. 2774, pp. 260–266. Springer, Oxford (2003)Google Scholar
- George, T., Merugu, S.: A scalable collaborative filtering framework based on co-clustering. In: Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM), Houston, pp. 625–628 (2005)Google Scholar
- Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Philadelphia, pp. 230–237 (1999)Google Scholar
- Hill, W.C., Stead, L., Rosenstein, M., Furnas, G.W.: Recommending and evaluating choices in a virtual community of use. In: Adelson, B., Dumais, S.T., Olson, J.S. (eds.) Proceedings of the Conference on Human Factors in Computing Systems (CHI), Denver, pp. 194–201 (1995)Google Scholar
- Hofmann, T.: Probabilistic latent semantic analysis. In: Laskey, K.B., Prade, H. (eds.) Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI), Stockholm, pp. 289–296 (1999)Google Scholar
- Hofmann T.: Latent semantic models for collaborative filtering. ACM Trans. Inform. Syst. 22(1), 89–115 (2004)CrossRefGoogle Scholar
- Jäschke, R., Marinho, L.B., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in folksonomies. In: Kok, J.N., Koronacki, J., deMántaras, R.L., Matwin, S., Mladenic, D., Skowron, A. (eds.) Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD). Lecture Notes in Computer Science, vol. 4702, pp. 506–514. Springer, Warsaw (2007)Google Scholar
- Jin, X., Zhou, Y., Mobasher, B.: Web usage mining based on probabilistic latent semantic analysis. In: Kim, W., Kohavi, R., Gehrke, J., DuMouchel, W. (eds.) Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Seattle, pp. 197–205 (2004)Google Scholar
- Joachims, T., Freitag, D., Mitchell, T.M.: Web watcher: a tour guide for the world wide web. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI), Nagoya, vol. 1, pp. 770–777 (1997)Google Scholar
- Kashoob, S., Caverlee, J., Kamath, K.: Community-based ranking of the social web. In: Chignell, M.H., Toms, E. (eds.) Proceedings of the 21st ACM Conference on Hypertext and Hypermedia (HT), Toronto, pp. 141–150 (2010)Google Scholar
- Kim, J.: User-generated content (ugc) revolution?: critique of the promise of youtube. Ph.D. thesis, University of Iowa (2010)Google Scholar
- Kim S., Fox E.A.: Interest-based user grouping model for collaborative filtering in digital libraries. In: Chen, Z., Chen, H., Miao, Q., Fu, Y., Fox, E.A., Lim, E.P (eds) Digital Libraries: International Collaboration and Cross-Fertilization, Proceedings of the Seventh International Conference on Asian Digital Libraries (ICADL). Lecture Notes in Computer Science, vol. 3334, pp. 533–542. Springer, Shanghai (2004)Google Scholar
- Kittur, A., Suh, B., Pendleton, B.A., Chi, E.H., Suh, B., Mytkowicz, T.: Power of the few vs. wisdom of the crowd: Wikipedia and the rise of the bourgeoisie. In: Presented at alt.CHI at ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), San Jose, pp. 453–462 (2007)Google Scholar
- Kohrs, A., Mérialdo, B.: Clustering for collaborative filtering applications. In: Proceedings of Computational Intelligence for Modelling, Control and Automation, Vienna (1999)Google Scholar
- Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adap. Inter. 22(1–2), 101–123 (2012)Google Scholar
- Konstan J.A., Miller B.N., Maltz D., Herlocker J.L., Gordon L.R., Riedl J.: Grouplens: applying collaborative filtering to usenet news. Commun. ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
- Kosala R., Blockeel H.: Web mining research: a survey. SIGKDD Explor. 2(1), 1–15 (2000)CrossRefGoogle Scholar
- Kumar R., Raghavan P., Rajagopalan S., Tomkins A.: Trawling the web for emerging cyber-communities. Comput. Netw. 31(11-16), 1481–1493 (1999)CrossRefGoogle Scholar
- Lin, Y.R., Sundaram, H., Chi, Y., Tatemura, J., Tseng, B.L.: Blog community discovery and evolution based on mutual awareness expansion. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, Silicon Valley, pp. 48–56 (2007)Google Scholar
- Linden G., Smith B., York J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
- Marlin B.: Modeling user rating profiles for collaborative filtering. In: Thrun, S., Saul, L.K., Schölkopf, B. (eds) Advances in Neural Information Processing Systems (NIPS), Vancouver, British Columbia (2003)Google Scholar
- McDaid, A., Hurley, N.: Detecting highly overlapping communities with model-based overlapping seed expansion. In: N. Memon, R. Alhajj (eds.) Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, pp. 112–119. Odense, Denmark (2010)Google Scholar
- Middleton S.E., Roure D.D., Shadbolt N.R.: Ontology-based recommender systems. In: Staab, S., Studer, R. (eds) Handbook on Ontologies, International Handbooks on Information Systems, pp. 779–796. Springer, Heidelberg (2009)Google Scholar
- Mitrovic, A.: Fifteen years of constraint-based tutors: what we have achieved and where we are going. User Model. User-Adap. Inter. 22(1–2), 39–72 (2012)Google Scholar
- Mobasher, B.: Data mining for web personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, vol. 4321, pp. 90–135. Springer, Heidelberg (2007)Google Scholar
- Mobasher B., Cooley R., Srivastava J.: Automatic personalization based on web usage mining. Commun. ACM 43(8), 142–151 (2000)CrossRefGoogle Scholar
- Mobasher B., Dai H., Luo T., Nakagawa M.: Discovery and evaluation of aggregate usage profiles for web personalization. Data Min. Knowl. Discov. 6(1), 61–82 (2002)MathSciNetCrossRefGoogle Scholar
- Mobasher, B., Jin, X., Zhou, Y.: Semantically enhanced collaborative filtering on the web. In: Berendt, B., Hotho, A., Mladenic, D., van Someren, M., Spiliopoulou, M., Stumme, G. (eds.) Web Mining: From Web to Semantic Web, Revised Selected and Invited Papers of the First European Web Mining Forum, EMWF. Lecture Notes in Computer Science, vol. 3209, pp. 57–76. Springer, Heidelberg (2004)Google Scholar
- Naphade M.R., Smith J.R., Tesic J., Chang S.F., Hsu W.H., Kennedy L.S., Hauptmann A.G., Curtis J.: Large-scale concept ontology for multimedia. IEEE MultiMed. 13(3), 86–91 (2006)CrossRefGoogle Scholar
- Nasraoui O., Frigui H., Krishnapuram R., Joshi A.: Extracting web user profiles using relational competitive fuzzy clustering. Int. J. Artif. Intell. Tools 9(4), 509–526 (2000)CrossRefGoogle Scholar
- Nazir, A., Raza, S., Chuah, C.N.: Unveiling facebook: a measurement study of social network based applications. In: Papagiannaki, K., Zhang, Z.L. (eds.) Proceedings of the Eighth ACM SIGCOMM Conference on Internet Measurement, Vouliagmeni, pp. 43–56 (2008)Google Scholar
- O’Connor, M., Herlocker, J.L.: Clustering items for collaborative filtering. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley (1999)Google Scholar
- Orwant J.: Heterogeneous learning in the doppelgänger user modeling system. User Model. User-Adap. Inter. 4(2), 107–130 (1995)CrossRefGoogle Scholar
- Paliouras, G., Papatheodorou, C., Karkaletsis, V., Spyropoulos, C.D.: Clustering the users of large web sites into communities. In: Langley, P. (ed.) Proceedings of the Seventeenth International Conference on Machine Learning (ICML), Stanford, pp. 719–726 (2000)Google Scholar
- Parra, D., Brusilovsky, P.: Collaborative filtering for social tagging systems: an experiment with citeulike. In: Bergman, L.D., Tuzhilin, A., Burke, R.D., Felfernig, A., Schmidt-Thieme, L. (eds.) Proceedings of the ACM Conference on Recommender Systems (RecSys), New York, pp. 237–240 (2009)Google Scholar
- Pathak, N., DeLong, C., Banerjee, A., Erickson, K.: Social topic models for community extraction. In: Proceedings of the Second International Workshop on Advances in Social Network Mining and Analysis (SNAKDD), Las Vegas, pp. 77–96 (2008)Google Scholar
- Pazzani, M.J., Muramatsu, J., Billsus, D.: Syskill & Webert: Identifying interesting web sites. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference (AAAI/IAAI), Portland, vol. 1, pp. 54–61 (1996)Google Scholar
- Perkowitz M., Etzioni O.: Towards adaptive web sites: conceptual framework and case study. Artif. Intell. 118(1-2), 245–275 (2000)zbMATHCrossRefGoogle Scholar
- Pierrakos D., Paliouras G.: Personalizing web directories with the aid of web usage data. IEEE Trans. Knowl. Data Eng. 22(9), 1331–1344 (2010)CrossRefGoogle Scholar
- Pierrakos D., Paliouras G., Papatheodorou C., Spyropoulos C.D.: Web usage mining as a tool for personalization: a survey. User Model. User-Adap. Inter. 13(4), 311–372 (2003)CrossRefGoogle Scholar
- Rheingold H.: The Virtual Community: Homesteading on the Electronic Frontier. Addison-Wesley, New York (1993)Google Scholar
- Rich E.: User modeling via stereotypes. Cogn. Sci. 3(4), 329–354 (1979)CrossRefGoogle Scholar
- Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Application of dimensionality reduction in recommender system—a case study. Technical Report CS-TR 00-043, Computer Science and Engineering Department, University of Minnesota (2000)Google Scholar
- Schafer J.B., Konstan J.A., Riedl J.: E-commerce recommendation applications. Data Min. Knowl. Discov. 5, 115–153 (2001)zbMATHCrossRefGoogle Scholar
- Schroedl, S., Kesari, A., Neumeyer, L.: Personalized ad placement in web search. In: Proceedings of the 4th Annual International Workshop on Data Mining and Audience Intelligence for Online Advertising (AdKDD), Washington USA (2010)Google Scholar
- Schuler D.: Community networks: building a new participatory medium. Commun. ACM 37(1), 38–51 (1994)CrossRefGoogle Scholar
- Seth A., Zhang J., Cohen R: Bayesian credibility modeling for personalized recommendation in participatory media. In: Bra, P.D., Kobsa, A., Chin, D.N (eds) Proceedings of the 18th International Conference on User Modeling, Adaptation, and Personalization (UMAP) Lecture Notes in Computer Science, vol 6075., pp. 279–290. Springer, Big Island (2010)Google Scholar
- Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth”. In: Katz, I.R., Mack, R.L., Marks, L., Rosson, M.B., Nielsen, J. (eds.) Proceedings of the Conference on Human Factors in Computing Systems (CHI), Denver, pp. 210–217 (1995)Google Scholar
- Siersdorfer, S., Sizov, S.: Social recommender systems for web 2.0 folksonomies. In: Cattuto, C., Ruffo, G., Menczer, F. (eds.) Proceedings of the 20th ACM Conference on Hypertext and Hypermedia (HYPERTEXT), Torino, pp. 261–270 (2009)Google Scholar
- Smyth B.: A community-based approach to personalizing web search. IEEE Comput. 40(8), 42–50 (2007)CrossRefGoogle Scholar
- Snoek, C., Worring, M., van Gemert, J., Geusebroek, J.M., Smeulders, A.W.M.: The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Nahrstedt, K., Turk, M., Rui, Y., Klas, W., Mayer-Patel, K. (eds.) Proceedings of the 14th ACM International Conference on Multimedia, Santa Barbara, pp. 421–430 (2006)Google Scholar
- Spiliopoulou, M., Faulstich, L.: WUM—a tool for www ulitization analysis. In: Atzeni, P., Mendelzon, A.O., Mecca, G. (eds.) Selected Papers of the International Workshop on World Wide Web and Databases (WebDB). Lecture Notes in Computer Science, vol. 1590, pp. 184–103. Springer, Heidelberg (1998)Google Scholar
- Srivastava J., Cooley R., Deshpande M., Tan P.N.: Web usage mining: discovery and applications of usage patterns from web data. SIGKDD Explor. 1(2), 12–23 (2000)CrossRefGoogle Scholar
- Staab S., Angele J., Decker S., Erdmann M., Hotho A., Maedche A., Schnurr H.P., Studer R., Sure Y.: Semantic community web portals. Comput. Netw. 33(1-6), 473–491 (2000)CrossRefGoogle Scholar
- Stock O., Zancanaro M., Busetta P., Callaway C., Kruger A., Kruppa M., Kuflik T., Not E., Rocchi C.: Adaptive, intelligent presentation of information for the museum visitor in peach. User Model. User-Adap. Inter. 17, 257–304 (2007)CrossRefGoogle Scholar
- Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 19p. doi: 10.1155/2009/421425 (2009)
- Sun J., Tsourakakis C.E., Hoke E., Faloutsos C., Eliassi-Rad T.: Two heads better than one: pattern discovery in time-evolving multi-aspect data. Data Min. Knowl. Discov. 17(1), 111–128 (2008)MathSciNetCrossRefGoogle Scholar
- Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: Pu, P., Bridge, D.G., Mobasher, B., Ricci, F. (eds.) Proceedings of the ACM Conference on Recommender Systems (RecSys), Lausanne, pp. 43–50 (2008)Google Scholar
- Tao, X., Li, Y., Zhong, N., Nayak, R.: Ontology mining for personalized web information gathering. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, Silicon Valley, pp. 351–358 (2007)Google Scholar
- Toch, E., Wang, Y., Cranor, L.F.: Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems. User Model. User-Adap. Inter. 22(1–2), 203–220 (2012)Google Scholar
- Ungar, L.H., Foster, D.P.: Clustering methods for collaborative filtering. In: Proceedings of the Workshop on Recommender Systems at the 15th National Conference on Artificial Intelligence, Madison (1998)Google Scholar
- van Hage, W.R., Stash, N., Wang, Y., Aroyo, L.: Finding your way through the Rijksmuseum with an adaptive mobile museum guide. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) The Semantic Web: Research and Applications, Proceedings of the Seventh Extended Semantic Web Conference (ESWC), Part I. Lecture Notes in Computer Science, vol. 6088, pp. 46–59. Springer, Heraklion (2010)Google Scholar
- Wei, K., Huang, J., Fu, S.: A survey of e-commerce recommender systems. In: Proceedings of the International Conference on Service Systems and Service Management, Tokyo, pp. 1–5 (2007)Google Scholar
- Wu K.L., Yu P.S., Ballman A.: Speedtracer: a web usage mining and analysis tool. IBM Syst. J. 37(1), 89–105 (1998)CrossRefGoogle Scholar
- Xu Z., Tresp V., Rettinger A., Kersting K.: Social network mining with nonparametric relational models. In: Giles, C.L., Smith, M., Yen, J., Zhang, H (eds) Revised Selected Papers of the Second International Workshop on Advances in Social Network Mining and Analysis (SNAKDD) Lecture Notes in Computer Science, vol 5498., pp. 77–96. Springer, Heidelberg (2010)Google Scholar
- Yan T.W., Jacobsen M., Garcia-Molina H., Dayal U.: From user access patterns to dynamic hypertext linking. Comput. Netw. 28(7–11), 1007–1014 (1996)Google Scholar
- Zhou, Y., Davis, J.: Discovering web communities in the blogspace. In: Proceedings of the 40th Hawaii International Conference on Systems Science (HICSS), Waikoloa, p. 85 (2007)Google Scholar
- Zhuge H.: Communities and emerging semantics in semantic link network: discovery and learning. IEEE Trans. Knowl. Data Eng. 21(6), 785–799 (2009)CrossRefGoogle Scholar