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Using Social Tags and User Rating Patterns for Collaborative Filtering

Authors:

Vipul GuptaAuthors Info & Claims

International Journal of Information Systems and Social Change, Volume 8, Issue 2

Pages 25 - 49

https://doi.org/10.4018/IJISSC.2017040102

Published: 01 April 2017 Publication History

Abstract

The overwhelming supply of online information on the Web makes finding better ways to separate important information from the noisy data ever more important. Recommender systems may help users deal with the information overloading issue, yet their performance appears to have stalled in currently available approaches. In this study, the authors propose and examine a novel user profiling approach that uses collaborative tagging information to enhance recommendation performance. They evaluate the proposed hybrid approach, illustrated in the context of movie recommendation. The authors also empirically evaluate various existing recommendation approaches in comparison with the newly proposed approach using sensitivity analyses to investigate the potential use of varied user rating or tagging patterns to improve recommendations accuracy. The results don't just indicate the effective and competitive performance of the suggested approach, but they also suggest important implications and directions for further research, including the potential associated with applying multiple recommendation approaches within a single system based on the different rating or tagging patterns of the user.

References

[1]

Adomavicius, G., Kamireddy, S., & Kwon, Y. 2007, December. Towards more confident recommendations: Improving recommender systems using filtering approach based on rating variance. Proceedings of the17th Workshop on Information Technologies and Systems WITS '07.

[2]

Adomavicius, G., & Tuzhilin, A. 2005. 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, 176, 734-749.

Digital Library

[3]

AlspectorJ.KolczA.KarunanithiN. 1998. Comparing feature-based and clique-based user models for movie selection.Proceedings of the Third ACM Conference on Digital Libraries, Pittsburgh, PA, USA pp. 11-18. 10.1145/276675.276677

[4]

Balabanovic, M., & Shoham, Y. 1997. Fab: Content-based, collaborative recommendation. Communications of the ACM, 403, 66-72.

Digital Library

[5]

Bell, R. M., Koren, Y., & Volinsky, C. 2007. Modeling relationships at multiple scales to improve accuracy of large recommender systems. Proceedings ofKDD'07.

[6]

BreeseF. S.HeckermanD.KadieC. 1998, July. Empirical analysis of predictive algorithms for collaborative filtering.Proc. 14th Conf. Uncertainty in Artificial Intelligence.

[7]

Burke, R. 2002. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 124, 331-370.

Digital Library

[8]

Cantador, I., Bellogin, A., & Vallet, D. 2010. Content-based recommendation in social tagging systems. Proceedings of RecSys, 10Sept, 26-30.

[9]

Cheung, K. M. 2007, January. Trend takes to keyword tagging. Pew Internet & American Life Project.

[10]

Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., & Sartin, M. 1999. Combining content-based and collaborative filters in an online newspaper. Proceedings of theACM SIGIR Workshop on Recommender System.

[11]

Cripe, B. 2007, July. Folksonomy, keywords, & tags: Social & democratic user internet in enterprise content management white paper. Oracle Business & Technology.

[12]

Golder, S. A., & Huberman, B. A. 2005. The structure of collaborative tagging systems. Journal of Information Science.

[13]

Good, N., Schafer, J. B., Konstan, J. A., & Borchers, A. 1999. Combining collaborative filtering with personal agents for better recommendations. AAAI.

[14]

Hayes, C., Avesani, P., & Veeramachaneni, S. 2007. An analysis of the use of tags in a blog recommender system. Proceedings of the International Joint Conferences on Artificial Intelligence.

[15]

HerlockerJ. L.KonstanJ. A.BorchersA.RiedlJ. 1999. An algorithmic framework for performing collaborative filtering.Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA pp. 230-237. 10.1145/312624.312682

[16]

Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 221, 5-53.

Digital Library

[17]

HillW.SteadL.RosensteinM.FurnasG. 1995. Recommending and evaluating choices in a virtual community of use.Proceedings of the ACM Conference on Human Factors in Computing Systems CHI'95 pp. 194-201. New York: ACM Press. 10.1145/223904.223929

[18]

Hornung, T., Koschmider, A., & Oberweis, A. 2007, December. A Recommender system for business process models. Proceedings of the17th Workshop on Information Technologies and Systems WITS '07.

[19]

Htun, Z., & Tar, P. P. 2014. A resource recommender systems based on social tagging data. Machine Learning and Applications: An International Journal MLAIJ, 11.

[20]

Jin, R., Si, L., Zhai, C., & Callan, J. 2003. Collaborative filtering with decoupled models for preferences and ratings. Proceedings ofCIKM'03.

[21]

Kipp, M. E. I., & Campbell, D. G. 2007. Patterns and inconsistencies in collaborative tagging systems: An examination of tagging practices. Proceedings of American Society for Information Science and Technology, 431, 1-18.

[22]

Kittler, J. 1975. Mathematical methods of feature selection in pattern recognition. International Journal of Man-Machine Studies, 75, 609-637.

[23]

Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. 1997. GroupLens: Applying collaborative filtering to usenet news. Communications of the ACM, 403, 77-87.

Digital Library

[24]

Koschmider, A., & Oberweis, A. 2014. Recommendation-based business process design. In Handbook on Business Process Management Vol. 1, pp. 323-336.

[25]

KrulwichB.BurkeyC. 1996. Learning user information interests through extraction of semantically significant phrases.Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access, Stanford, CA, USA.

[26]

LangK. 1995. Newsweeder: Learning to filter netnews.Proc. 12th Int'l Conf. Machine Learning.

[27]

Linden, G., Smith, B., & York, J. 2003. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 71, 76-80.

Digital Library

[28]

LynchC. 2001. Personalization and recommender systems in the larger context: new directions and research questions.Proceedings of the 2nd DELOS Network of Excellence Workshop on Personalization and Recommender Systems in Digital Libraries, Dublin, Ireland.

[29]

Maes, P. 1994. Agents that reduce work and information overload . Communications of the ACM, 377, 31-40.

[30]

Marinho, L. B., Nanopoulos, A., Schmidt-Thieme, L., Jaschke, R., Hotho, A., Stumme, G., & Symeonidis, P. 2011. Social tagging recommender systems. Recommender Systems Handbook.

[31]

Melville, P., Mooney, R. J., & Nagarajan, R. 2002. Content-boosted collaborative filtering for improved recommendations. Proc. Of the Eighteenth National Conf. on AI.

[32]

Meteren, R. V., & Someren, M. V. 2000. Using content-based filtering for recommendation. Machine Learning in the New Information Age, MLnet / ECML2000 Workshop, Spain.

[33]

MooneyR. J.RoyL. 1999 Content-based book recommending using learning for text categorization.Proc. ACM SIGIR '99 Workshop Recommender Systems: Algorithms and Evaluation.

[34]

Netflix. 2015, January 29. Form 10-K Annual Report.

[35]

O'Reilly, T. 2005, September. What is web 2.0, design patterns and business models for the next generation of software. O'Reilly Network.

[36]

Parsons, J., Ralph, P., & Gallagher, K. 2004. Using viewing time to infer user preference in recommender systems. Proceedings of theAAAI Workshop on Semantic Web Personalization.

[37]

PazzaniM.MuramatsuJ.BillsusD. 1996. Syskill & Webert: Identifying interesting web sites.Proceedings of the 13th National Conference on Artificial Intelligence, Portland, OR, USA pp. 54-61.

[38]

Pazzani, M. J. 1999. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 135, 393-408.

[39]

PengJ.ZengD.ZhaoH.WangF. 2010. Collaborative filtering in social tagging systems based on joint item-tag recommendations.Proceedings of the 19th ACM international conference on Information and knowledge management pp. 809-818. 10.1145/1871437.1871541

[40]

Rajaraman, A. 2008. More data usually beats better algorithms. Retrieved from http://anand.typepad.com/datawocky/2008/03/more-data-usual.html

[41]

RalphP.ParsonsJ. 2006. A framework for automatic online personalization.Proceedings of the 39th Hawaii International Conference on System Science.

[42]

ResnickP.IacovouN.SuchakM.BergstromP.RiedlJ. 1994. GroupLens: An open architecture for collaborative filtering of netnews.Proc. Of the Conference on Computer Supported Cooperative Work, Chapel Hill, NC, USA pp. 175-186. 10.1145/192844.192905

[43]

Wikipedia. n. d. RMSE. Retrieved from https://en.wikipedia.org/wiki/Root-mean-square_deviation

[44]

Sarwar, B. M., Karypis, G., Konstan, J. A., & Riedl, J. 2000. Application of dimensionality reduction in recommender systems - A Case Study. Proceedings of the ACM WebKDD Workshop.

[45]

SarwarB. M.KarypisG.KonstanJ. A.RiedlJ. 2001. Item-based collaborative filtering recommendation algorithms.Proceedings of the 10th International conference on World Wide Web, Hong Kong pp. 285-295.

[46]

SarwarB. M.KarypisG.KonstanJ. A.RiedlJ. 2002. Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering.Proceedings of the Fifth International Conference on Computer and Information Technology.

[47]

Schafer, J. B., Konstan, J. A., & Riedl, J. 2001. E-commerce recommendation applications. Data Mining and Knowledge Discovery, 51, 115-153.

[48]

Shepitsen, A., Gemmell, J., Mobasher, B., & Burke, R. 2008. Personalized recommendation in social tagging systems using hierarchical clustering. Proceedings ofRecSys'08.

[49]

Shirky, C. 2006. Ontology is overrated: Categories, links, and tags. Retrieved from http://www.shirky.com/writings/ontology_overrate.html

[50]

Silva, E., Camilo-Junior, C., Pascoal, L., & Rosa, T. 2016. An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering. Expert Systems with Applications, 53, 204-218.

Digital Library

[51]

Tso-Sutter, K. H. L., Marinho, L. B., & Schmidt-Thieme, L. 2008. Tag-aware recommender systems by fusion of collaborative filtering algorithms. Proceedings ofSAC'08.

[52]

Wei, C., Shaw, M. J., & Easley, R. F. 2002. A survey of recommendation systems in electronic commerce. In R.T. Rust & P.K. Kannes Eds., E-Service: New Directions in Theory and Practice. Sharpe Publisher.

[53]

Wu, H., Zubair, M., & Maly, K. 2006, August 22-25. Harvesting social knowledge from folksonomies. Proceedings of HT '06, Odense, Denmark.

[54]

Yang, Y., & Chute, C. G. 1994. An example-based mapping method for text categorization and retrieval. ACM Transactions on Information Systems, 123, 252-277.

Digital Library

[55]

Zhao, S., Du, N., Nauerz, A., Zhang, X., Yuan, Q., & Fu, R. 2008. Improved recommendation based on collaborative tagging behaviors. Proceedings ofIUI'08 pp. 413-416.

Cited By

Yuan ZLee JZhang S(2021)Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering AlgorithmComplexity10.1155/2021/44765602021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/4476560
Xu DShaikh N(2018)A Heuristic Approach for Ranking Items Based on Inputs from Multiple ExpertsInternational Journal of Information Systems and Social Change10.4018/IJISSC.20180701019:3(1-22)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.4018/IJISSC.2018070101
Dash AShaikh N(2018)Impact of Retailer Generated Online Content on the Perceived Helpfulness of Product ReviewsInternational Journal of Information Systems and Social Change10.4018/IJISSC.20180401059:2(61-77)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.4018/IJISSC.2018040105

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Published In

International Journal of Information Systems and Social Change Volume 8, Issue 2

April 2017

87 pages

ISSN:1941-868X

EISSN:1941-8698

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IGI Global

United States

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Published: 01 April 2017

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Cited By

Yuan ZLee JZhang S(2021)Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering AlgorithmComplexity10.1155/2021/44765602021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/4476560
Xu DShaikh N(2018)A Heuristic Approach for Ranking Items Based on Inputs from Multiple ExpertsInternational Journal of Information Systems and Social Change10.4018/IJISSC.20180701019:3(1-22)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.4018/IJISSC.2018070101
Dash AShaikh N(2018)Impact of Retailer Generated Online Content on the Perceived Helpfulness of Product ReviewsInternational Journal of Information Systems and Social Change10.4018/IJISSC.20180401059:2(61-77)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.4018/IJISSC.2018040105

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