Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning

Authors

  • Simone Kopeinik Graz University of Technology
  • Dominik Kowald
  • Ilire Hasani-Mavriqi
  • Elisabeth Lex

DOI:

https://doi.org/10.1561/106.00000007

Abstract

Classic resource recommenders like Collaborative Filtering treat users as being just another entity, thereby neglecting non-linear user-resource dynamics that shape attention and interpretation. SUSTAIN, as an unsupervised human category learning model, captures these dynamics. It aims to mimic a learner’s behavior of categorization. In this paper, we use three social bookmarking datasets gathered from BibSonomy, CiteULike and Delicious to investigate SUSTAIN as a user modeling approach to re-rank and enrich Collaborative Filtering following a hybrid recommender strategy. Evaluations against baseline algorithms in terms of recommender accuracy and computational complexity reveal encouraging results. Our approach substantially improves Collaborative Filtering and, depending on the dataset, successfully competes with a computationally much more expensive Matrix Factorization variant. In a further step, we explore SUSTAIN’s dynamics in our specific learning task and show that both, memorization of a user’s history and clus-
tering, contribute to the algorithm’s performance. Finally, we observe that the users’ attentional foci determined by SUSTAIN correlate with the users’ level of curiosity, identified by the SPEAR algorithm. Overall, the results of our study show that SUSTAIN can be used to efficiently model attention-interpretation dynamics of users and can help to improve Collaborative Filtering in resource recommendation tasks.

References

P. Adamopoulos and A. Tuzhilin. On over-specialization and concentration bias of recommendations. In Proc. of RecSys’14, RecSys ’14, pages 153–160, New York, NY,

USA, 2014. ACM.

G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734–749, 2005.

A. Bar, L. Rokach, G. Shani, B. Shapira, and A. Schclar. Improving simple collaborative filtering models using ensemble methods. In Multiple Classifier Systems, pages 1–12. Springer, 2013.

J. Basilico and T. Hofmann. Unifying collaborative and content-based filtering. In Proc. of ICML’04, page 9. ACM, 2004.

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. The Journal of machine Learning research, 3:993–1022, 2003.

P. G. Campos, F. D 퀱ez, and I. Cantador. Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Modeling

and User-Adapted Interaction, pages 1–53, 2013.

I. Cantador, P. Brusilovsky, and T. Kuflik. 2nd workshop on information heterogenity and fusion in recommender systems (hetrec 2011). In Proc. of RecSys’11, RecSys 2011, New York, NY, USA, 2011. ACM.

L. Chen, M. de Gemmis, A. Felfernig, P. Lops, F. Ricci, and G. Semeraro. Human decision making and recommender systems. ACM Trans. Interact. Intell. Syst., 3(3):17:1–17:7, Oct. 2013.

B. Coleho, C. Martins, and A. Almeida. Web intelligence in tourism: User modeling and recommender system. In Proc. of WI-IAT ’10, pages 619–622, Washington, DC, USA, 2010. IEEE Computer Society.

P. Cremonesi, A. Donatacci, F. Garzotto, and R. Turrin. Decision-making in recommender systems: The role of user’s goals and bounded resources. In Proc. of

Decisions@RecSys’12 Workshop, volume 893 of CEUR Workshop Proceedings, pages 1–7. CEUR-WS.org, 2012.

P. Cremonesi, F. Garzotto, and R. Turrin. Investigating the persuasion potential of recommender systems from a quality perspective: An empirical study. ACM Trans.

Interact. Intell. Syst., 2(2):11:1–11:41, June 2012.

S. Dennerlein, M. Rella, V. Tomberg, D. Theiler, T. Treasure-Jones, M. Kerr, T. Ley, M. Al-Smadi, and C. Trattner. Making sense of bits and pieces: A sensemaking tool for informal workplace learning. In Open Learning and Teaching in Educational Communities, pages 391–397. Springer, 2014.

S. Doerfel and R. J ̈aschke. An analysis of tag-recommender evaluation procedures. In Proc. of Recsys’13, pages 343–346, New York, NY, USA, 2013. ACM.

S. Dooms. Dynamic generation of personalized hybrid recommender systems. In Proc. of RecSys’13, RecSys ’13, pages 443–446, New York, NY, USA, 2013. ACM.

R. A. Finke, T. B. Ward, and S. M. Smith. Creative cognition: Theory, research, and applications. 1992.

W.-T. Fu and W. Dong. Collaborative indexing and knowledge exploration: A social learning model. IEEE Intelligent Systems, 27(1):39–46, 2012.

J. Gemmell, T. Schimoler, M. Ramezani, L. Christiansen, and B. Mobasher. Improving folkrank with item-based collaborative filtering. Recommender Systems & the Social Web, 2009.

T. L. Griffiths, M. Steyvers, J. B. Tenenbaum, et al. Topics in semantic representation. Psychological review, 114(2):211, 2007.

J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22(1):5–53, Jan. 2004.

Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM’08. Eighth IEEE International Conference on, pages 263–272. IEEE, 2008.

C.-L. Huang, P.-H. Yeh, C.-W. Lin, and D.-C. Wu. Utilizing user tag-based interests in recommender systems for social resource sharing websites. Knowledge-Based

Systems, 56:86–96, 2014.

G. Jawaheer, P. Weller, and P. Kostkova. Modeling user preferences in recommender systems: A classification framework for explicit and implicit user feedback. ACM Trans. Interact. Intell. Syst., 4(2):8:1–8:26, June 2014.

P. B. Kantor, L. Rokach, F. Ricci, and B. Shapira. Recommender systems handbook. Springer, 2011.

W. Kintsch and P. Mangalath. The construction of meaning. Topics in Cognitive Science, 3(2):346–370, 2011.

D. Kowald, E. Lacic, and C. Trattner. Tagrec: Towards a standardized tag recommender benchmarking framework. In Proc. of HT’14, New York, NY, USA, 2014. ACM.

R. Krestel, P. Fankhauser, and W. Nejdl. Latent dirichlet allocation for tag recommendation. In Proc. of Recsys’09, pages 61–68. ACM, 2009.

E. Lacic, D. Kowald, P. Seitlinger, C. Trattner, and D. Parra. Recommending items in social tagging systems using tag and time information. In Proc. of the Social Personalization Workshop colocated with HT’14, 2014.

J. Law. Actor network theory and material semiotics. The new Blackwell companion to social theory, pages 141–158, 2009.

G. Loewenstein. The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, pages 75–98, 1994.

J. Lorince, S. Zorowitz, J. Murdock, and P. M. Todd. â€supertagger†behavior in building folksonomies. In Proc. of WebSci ’14, WebSci ’14, pages 129–138, New York, NY, USA, 2014. ACM.

J. Lorince, S. Zorowitz, J. Murdock, and P. M. Todd. The wisdom of the few? S ̧ supertaggers in collaborative filtering systems. The Journal of Web Science, 1(1):16–32, 2015.

B. C. Love, D. L. Medin, and T. M. Gureckis. Sustain: a network model of category learning. Psychological review, 111(2):309, 2004.

X. Ning and G. Karypis. Slim: Sparse linear methods for top-n recommender systems. In Data Mining (ICDM), 2011 IEEE 11th International Conference on, pages

–506. IEEE, 2011.

M. G. Noll, C.-m. Au Yeung, N. Gibbins, C. Meinel, and N. Shadbolt. Telling experts from spammers: Expertise ranking in folksonomies. In Proc. of SIGIR’09, SIGIR ’09,

pages 612–619, New York, NY, USA, 2009. ACM.

D. Parra and S. Sahebi. Recommender systems : Sources of knowledge and evaluation metrics. In Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis, pages 149–175. Springer-Verlag, 2013.

D. Parra-Santander and P. Brusilovsky. Improving collaborative filtering in social tagging systems for the recommendation of scientific articles. In Proc. of WIIAT’10, volume 1, pages 136–142. IEEE, 2010.

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proc. of WWW’01, pages 285–295. ACM, 2001.

J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen. Collaborative filtering recommender systems. In The adaptive web, pages 291–324. Springer, 2007.

P. Seitlinger, D. Kowald, S. Kopeinik, I. Hasani-Mavriqi, E. Lex, and T. Ley. Attention please! a hybrid resource recommender mimicking attention-interpretation dynamics. In Proc. of WWW’15, pages 339–345. International World Wide Web Conferences Steering Committee, 2015.

P. Seitlinger, D. Kowald, C. Trattner, and T. Ley. Recommending tags with a model of human categorization. In Proc. of CIKM’13, pages 2381–2386, New York, NY, USA, 2013. ACM.

L. Shi. Trading-off among accuracy, similarity, diversity, and long-tail: A raph-based recommendation approach. In Proc. of RecSys’13, pages 57–64, New York, NY, USA,

ACM.

Y. Shi, M. Larson, and A. Hanjalic. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Comput. Surv.,

(1):3:1–3:45, May 2014.

C.-m. A. Yeung, M. G. Noll, N. Gibbins, C. Meinel, and N. Shadbolt. Spear: Spamming-resistant expertise analysis and ranking in collaborative tagging systems.

Computational Intelligence, 27(3):458–488, 2011.

H. Yin, B. Cui, J. Li, J. Yao, and C. Chen. Challenging the long tail recommendation. Proc. of VLDB Endow., 5(9):896–907, May 2012. [45] N. Zheng and Q. Li. A recommender system based on tag and time information for social tagging systems. Expert Systems with Applications, 38(4):4575–4587, 2011.

Downloads

Published

2016-10-03

Issue

Section

Articles