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A hybrid approach for improving predictive accuracy of collaborative filtering algorithms

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Abstract

Recommender systems represent a class of personalized systems that aim at predicting a user’s interest on information items available in the application domain, operating upon user-driven ratings on items and/or item features. One of the most widely used recommendation methods is collaborative filtering that exploits the assumption that users who have agreed in the past in their ratings on observed items will eventually agree in the future. Despite the success of recommendation methods and collaborative filtering in particular, in real-world applications they suffer from the insufficient number of available ratings, which significantly affects the accuracy of prediction. In this paper, we propose recommendation approaches that follow the collaborative filtering reasoning and utilize the notion of lifestyle as an effective user characteristic that can group consumers in terms of their behavior as indicated in consumer behavior and marketing theory. Emanating from a basic lifestyle-based recommendation algorithm we incrementally proceed to the development of hybrid recommendation approaches that address certain dimensions of the sparsity problem and empirically evaluate them providing further evidence of their effectiveness.

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References

  • Aggarwal, C., Wolf, J., Kun-Lung, W., Yu, P. S.: Horting hatches an egg: a new graph-theoretic approach to collaborative filtering. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 201–212. San Diego, CA (1999)

  • Alspector J., Koicz A., Karunanithi N. (1997) Feature-based and clique-based user models for movie selection: a comparative study. User Model. User Adapt. Interact. 7, 297–304

    Article  Google Scholar 

  • Ardissono L., Console L., Torre I. (2001) An adaptive system for the personalized access to news. AI Commun. 14(3): 129–147

    MATH  Google Scholar 

  • Ardissono L., Goy A. (2000) Tailoring the interaction with users in web stores. User Model. User Adapt. Interact. 10, 251–302

    Article  Google Scholar 

  • Arndt, D., Gersten, W.: Data management in analytical customer relationship management. In: Proceedings of the 5th European Conference on Principles and Practice of Knowledge Discovery, Workshop on Data Mining for Marketing Applications, pp. 25–38. Freiburg, Germany (2001)

  • Balabanovic, M.: An adaptive web page recommendation service. In: ACM First International Conference on Autonomous Agents, pp. 378–385. Marina del Ray, CA (1997)

  • Balabanovic M., Shoham Y. (1997) Fab: content-based collaborative recommendation. Commun. ACM 40(3): 66–72

    Article  Google Scholar 

  • Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: using social and content-based information in recommendation. In: Proceedings of the 15th National Conference on Artificial Intelligence, pp. 714–720. Madison, WI (1998)

  • Beatty S.F., Homer P.M., Kahle L.R. (1998) Problems with VALS in international marketing research: an example from an application of the empirical mirror technique. Adv. Consum. Res. 15, 375–380

    Google Scholar 

  • Belch, G.E., Belch, B.A.: Introduction to advertising and promotion: an integrated marketing communications perspective. Irwin, San Diego, CA (1995)

  • Billsus, D., Pazzani, M.: Learning collaborative information filters. In: Proceedings of the Fifteenth National Conference on Machine Learning, pp. 46–54. San Francisco, CA (1998)

  • Bozios, T., Lekakos, G., Skoularidou, V., Chorianopoulos, K.: Advance techniques for personalized advertising in a digital TV environment: the iMedia system. In: eBusiness and eWork Conference, Venice, Italy, pp. 1025–1031 (2001)

  • Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Annual Conference on Uncertainty in Artifical Intelligence, pp. 43–52. San Francisco, CA, (1998)

  • Brusilovsky P. (2001) Methods and techniques of adaptive hypermedia. User Model. User Adapt. Interact. 6(2–3): 87–129

    Article  Google Scholar 

  • Burke R. (2002) Hybrid recommender systems: survey and experiments. User Model. User Adapt. Interact. 12, 331–370

    Article  MATH  Google Scholar 

  • Cawley G.C., Talbot N.L.C. (2003) Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers. Pattern Recognit. 36(1): 2585–2592

    Article  MATH  Google Scholar 

  • Chaney D. (1996) Lifestyles. Routledge, London

    Google Scholar 

  • Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: ACM SIGIR Workshop on Recommender Systems, Berkeley, CA (1999)

  • Domingos P., Pazzani M. (1997) On the optimality of the simple Bayesian classifier under zero-one-loss. Mach. Learn. 29, 103–130

    Article  MATH  Google Scholar 

  • Domshlak, C., Joachims, T.: Efficient and non-parametric reasoning over user preferences. User Model. User Adapt. Interact. (this issue) (2006)

  • Duda R.O., Hart P.E., Stork D.G. (2000) Pattern Classification. Wiley, New York

    Google Scholar 

  • Dzeroski S., Zenko B. (2004) Is combining classifiers with stacking better than selecting the best one? Mach. Learn. 54, 255–273

    MATH  Google Scholar 

  • Goldberg D., Nichols D., Oki B., Borchers A. (1992) Using collaborative filtering to weave and information tapestry. Commun. ACM 35(12): 61–70

    Article  Google Scholar 

  • Goldberg K., Roeder T., Guptra D., Perkins C. (2001) Eigentaste: a constant-time collaborative filtering algorithm. Inform. Retrieval 4(2): 133–151

    Article  MATH  Google Scholar 

  • Good, N., Schafer, J.B., Konstan, J., Borchers, A., Sarwar, B., Herlocker, J., et al.: Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of the 16th National Conference on Artificial Intelligence (AAAI-99), Menlo Park, CA, pp. 439–446 (1999)

  • Gunter B., Furnham A. (1992) Consumer Profiles: An Introduction to Psychographics. Routledge, London

    Google Scholar 

  • Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C.: Multivariate Data Analysis. Prentice Hall London (1998)

  • Harter S.P. (1996) Variations in relevance assessments and the measurement of retrieval effectiveness. J. Am. Soc. Inform. Sci. 47(1): 37–49

    Article  Google Scholar 

  • Hawkins, I., Best, R.J., Coney, K.A.: Consumer Behavior: Building Marketing Strategy. Irwin/McGraw-Hill New York (1998)

  • Herlocker J., Konstan J. (2001) Content-Independent task-focused recommendation. IEEE Internet Comput. 5, 40–47

    Article  Google Scholar 

  • Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the Twenty-second International Conference on Research and Development in Information Retrieval (SIGIR’99), New York, pp. 230–237 (1999)

  • Herlocker J., Konstan J., Riedl J. (2002) An empirical analysis of design choices in neighborhood-base collaborative filtering algorithms. Inform. Retrieval 5, 287–310

    Article  Google Scholar 

  • Herlocker J., Konstan J., Terveen L.G., Riedl J. (2004) Evaluating collaborative filtering recommender systems. ACM Trans. Inform. Syst. 22(1): 5–53

    Article  Google Scholar 

  • Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: ACM CHI’95 Conference on Human Factors in Computing Systems, Denver, Colorado, 194–201 (1995)

  • Hoffman, T., Puzicha, J.: Latent class models for collaborative filtering. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, Stocholm, Sweden, 688–693 (1999)

  • Hofmann T. (2004) Latent semantic models for collaborative filtering. ACM Trans. Inform. Syst. 22(1): 89–115

    Article  Google Scholar 

  • Hollink, M., van Someren, M., Wielinga, B.: Discovering stages in web navigation for problem- oriented navigation support. User Model. User Adapt. Interact. (this issue) (2006)

  • Jameson, A., Schafer, R., Simons, J., Weis, T.: Adaptive provision of evaluation-oriented information: tasks and techniques. In: Proceedings of the Fourteenth international joint conference on Artificial Intelligence, Montreal, Canada, 1886–1893 (1995)

  • Kara A., Kaynak E. (1997) Markets of a single customer: exploiting conceptual developments in market segmentation. Eur. J. Mark. 31(11/12): 873–895

    Article  Google Scholar 

  • Karypis, G.: Evaluation of item-based top-N recommendation algorithms. In: CIKM 2001, Atlanta, GA, pp. 247–254 (2001)

  • Kobsa A., Koenemann J., Pohl W. (2001) Personalized hypermedia presentation techniques for improving online customer relationships. Knowl. Eng. Rev. 16(2): 111–155

    Article  MATH  Google Scholar 

  • Kotler P. (1994) Marketing Management. Prentice-Hall Englewood Cliffs, NJ

    Google Scholar 

  • Krulwich, B. Lifestyle finder: intelligent user profiling using large-scale demographic data. AI Mag. 37–45 (1997)

  • Lekakos, G., Giaglis, G.: Personalization of advertisements in the digital TV context. In: Shen C., Magoulas, G. (eds.) Adaptable and Adaptive Hypermedia Systems, pp. 264–283. Idea Group PA (2005)

  • Lekakos G., Giaglis G. (2006) Improving the prediction accuracy of recommendation algorithms: approaches anchored on human factors. Interact. Comput. 18(3): 410–431

    Article  Google Scholar 

  • Lekakos, G., Giaglis, G.M.: A lifestyle-based approach for delivering personalized advertisements in digital interactive television. J. Comput. Mediated Commun. 9(2) (2004)

  • Malone T.W., Grant K.R., Turbak F.A., Brobst S.A. (1987) Intelligent information systems. Commun. ACM 30(5): 390–402

    Article  Google Scholar 

  • Maybury, M.: PersonalCasting. In: Workshop on Personalization in Future TV, Sonthofen, Germany, retrieved on 5/6/2005 from www.di.unito.it/∼liliana/UM2001 (2001)

  • Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI), pp. 187–192. Edmonton, Canada (2002)

  • Milenkovic M. (1998) Delivering interactive services via a digital TV infrastructure. IEEE Multimedia 5(4): 34–43

    Article  MathSciNet  Google Scholar 

  • Mooney, R. J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference in Digital Libraries, San Antonio, TX, pp. 195–204 (2000)

  • Moore, A.W., Lee, M.S.: Efficient algorithms for minimizing cross validation error. In: Proceedings of the 11th International Conference on Machine Learning, San Francisco, CA, pp. 190–198 (1994)

  • Mowen J.C., Minor M. (1998) Consumer Behavior, 5th edn. Prentice-Hall Upper Saddle River, New Jersey

    Google Scholar 

  • O’Mahony, M., Hurley, N., Silvestre, G.: Promoting recommendations: an attack on collaborative filtering. In: Proceedings of the Thirteenth International Conference on Database and Expert Systems Applications, Aix-en-Provence, France, pp. 494–503 (2002)

  • Pazzani M. (1999) A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5/6): 393–408

    Article  Google Scholar 

  • Pazzani M., Billsus D. (1997) Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27, 313–331

    Article  Google Scholar 

  • Peltier J.W., Schibrowsky J.A., Schultz D.E., Davis J. (2002) Interactive psychographics: cross-selling in the banking industry. J. Advert. Res. 42(2): 7–22

    Google Scholar 

  • Pennock, D.M., Horvitz, E., Lawrence, S., Giles, C.L.: Collaborative filtering by personality diagnosis: a hybrid memory and model-based approach. In: Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, San Francisco, CA, pp. 473–480 (2000)

  • Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of NetNews. In: ACM Conference on Computer Supported Cooperative Work, pp. 175–186. Chapel Hill, NC (1994)

  • Resnick P., Varian H.R. (1997) Recommender systems. Commun. ACM 40(3): 56–58

    Article  Google Scholar 

  • Rich E. (1979) User modeling via stereotypes. Cognit. Sci. 3, 329–354

    Article  Google Scholar 

  • Rich E. (1983) Users are individuals: individualizing user models. Int. J. Man-Mach. Stud. 18, 199–214

    Article  Google Scholar 

  • Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: ACM e-commerce conference, Minneapolis, Minnesota, pp. 158–167 (2000)

  • Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Item based collaborative filtering recommendation algorithms. In: Proceedings of the Tenth International World Wide Web Conference, pp. 285–295 (2001)

  • Sarwar, B.M., Konstan, J., Borchers, A., Herlocker, J., Miller, B., Riedl, J.: Using Filtering Agents to Improve Prediction Quality in the Grouplens Research Collaborative Filtering System, CSCW, Seattle, WA, pp. 345–354 (1998)

  • Schafer J.B., Konstan J.A., Riedl J. (2001) Electronic-commerce recommender systems. J. Data Mining Knowl. Discov. 5(1): 115–152

    Article  MATH  Google Scholar 

  • Schwab, I., Pohl, W., Koychev, I.: Learning to recommend from positive evidence. In: Proceedings of the Intelligent User Interfaces, New Orleans, LA, pp. 241–247 (2000)

  • Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “Word of Mouth” In: ACM CHI’95 Conference on Human Factors in Computing Systems, Denver, Colorado, pp. 210–217 (1995)

  • Smyth B., Cotter P. (2000) A personalized television listings service. Commun. ACM 43(8): 107–111

    Article  Google Scholar 

  • Ungar, L., Foster, D.: Clustering methods for collaborative filtering. In: Workshop on Recommendation Systems at the Fifteenth National Conference on Artifical Intelligence, Madison, Wisconsin, pp. 112–128 (1998)

  • van Setten, M., Veenstra, M., Nijholt, A.: Prediction strategies: combining prediction techniques to optimize personalization. In: AH ’2002 Workshop on personalization in Future TV, Malaga, Spain, pp. 29–38 (2002)

  • Vyncke P. (2002) Lifestyle segmentation: from attitudes, interests and opinions, to values, aesthetic styles, life visions and media preferences. Eur. J. Commun. 17(4): 445–464

    Article  Google Scholar 

  • Webb G., Pazzani M., Billsus D. (2001) Machine learning for user modeling. User Model. User Adapt. Interact. 11, 19–29

    Article  MATH  Google Scholar 

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  1. Department of Management Science and Technology, Athens University of Economics and Business, 47 Evelpidon Str., 11362, Athens, Greece

    George Lekakos & George M. Giaglis

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  1. George Lekakos
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  2. George M. Giaglis
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Correspondence to George Lekakos.

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Lekakos, G., Giaglis, G.M. A hybrid approach for improving predictive accuracy of collaborative filtering algorithms. User Model User-Adap Inter 17, 5–40 (2007). https://doi.org/10.1007/s11257-006-9019-0

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