Abstract
In this chapter, we apply our model with object typicality to recommendation system and propose a typicality-based recommendation approach named ROT and a typicality-based collaborative filtering approach named TyCo, which are different from previous recommendation methods. To the best of our knowledge, there is no work on applying typicality to recommender systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adomavicius G, Tuzhilin A (2005) Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions. IEEE Trans on Knowl and Data Eng 17(6): 734–749.
Konstan JA, Miller BN, Maltz D et al (1997) GroupLens: Applying Collaborative Filtering to Usenet News. ACM Commun 40(3): 77–87.
Sarwar B, Karypis G, Konstan J et al (2001) Item-Based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the 10th International Conference on World Wide Web. ACM Press, New York, pp 285–295.
Pazzani M, Billsus D (1997) Learning and Revising User Profiles: The Identification of Interesting Web Sites. Mach Learn 27(3): 313–331.
Mooney RJ, Roy L (2000) Content-Based Book Recommending Using Learning for Text Categorization. In: DL’ 00: Proceedings of the Fifth ACM Conference on Digital libraries. ACM Press, New York, pp 195–204.
Melville P, Mooney RJ, Nagarajan R (2002) Content-Boosted Collaborative Filtering for Improved Recommendations. In: Eighteenth National Conference on Artificial Intelligence, American Association for Artificial Intelligence, Menlo Park, pp 187–192.
Balabanovi’c M, Shoham Y (1997) Fab: Content-Based, Collaborative Recommendation. ACM Commun 40(3): 66–72.
Koutrika G, Bercovitz B, Ikeda R et al (2009) Flexible Recommendations for Course Planning. In: Proceedings of the 2009 IEEE International Conference on Data Engineering, IEEE Computer Society, Washington DC, pp 1467–1470.
Ge Y, Xiong H, Tuzhilin A et al (2010) An Energy-Efficient Mobile Recommender System. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and DataMining. ACMPress, New York, pp 899–908.
Abbassi Z, Lakshmanan LVS (2009) On Efficient Recommendations for Online Exchange Markets. In: Proceedings of the 2009 IEEE International Conference on Data Engineering. IEEE Computer Society, Washington DC, pp 712–723.
Ma H, King I, Lyu MR (2007) Effective Missing Data Prediction for Collaborative Filtering. In: SIGIR’ 07: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, New York, pp 39–46.
Wang J, de Vries AP, Reinders MJT (2006) Unifying User-Based and Item-Based Collaborative Filtering Approaches by Similarity Fusion. In: SIGIR’ 06. ACM Press, New York, pp 501–508.
Pazzani MJ, Billsus D (2007) Content-based Recommendation Systems. The Adaptive Web: Methods and Strategies of Web Personalization, pp 325–341.
Lang K (1995) Newsweeder: Learning to filter netnews. In: Proceedings of the 12th International Machine Learning Conference, pp 331–339.
Shardanand U, Maes P (1995) Social Information Filtering: Algorithms for Automating Word of Mouth. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM Press/Addison-Wesley, New York, pp 210–217.
Schein AI, Popescul A, Ungar LH et al (2002) Methods and Metrics for Cold-Start Recommendations. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, New York, pp 253–260.
Terveen L, Hill W, Amento B et al (1997) Phoaks: A System for Sharing Recommendations. ACM Commun 40(3): 59–62.
Aggarwal CC, Wolf JL, Wu K et al (1999) Horting Hatches an Egg: A New Graph-Theoretic Approach to Collaborative Filtering. In: KDD’ 99. ACM, New York, pp 201–212.
Herlocker JL, Konstan JA, Borchers A et al (1999) An Algorithmic Framework for Performing Collaborative Filtering. In: SIGIR’ 99. ACM Press, New York, pp 230–237.
Deshpande M, Karypis G (2004) Item-based Top-n Recommendation Algorithms. ACM Trans Inf Syst 22(1): 143–177.
Herlocker JL, Konstan JA, Terveen LG et al (2004) Evaluating Collaborative Filtering Recommender Systems. ACM Trans Inf Syst 22(1): 5–53.
Huang Z, Chen H, Zeng D (2004) Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering. ACM Trans Inf Syst 22(1): 116–142.
Hu Y, Koren Y, Volinsky C (2008) Collaborative Filtering for Implicit Feedback Datasets. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, IEEE Computer Society, Washington DC, pp 263–272.
Umyarov A, Tuzhilin A (2008) Improving Collaborative Filtering Recommendations Using External Data. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining. IEEE Computer Society, Washington DC, pp 618–627.
Cacheda F, Carneiro V, Fernández D et al (2011) Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposals for Scalable, High-Performance Recommender Systems. ACM Trans Web 5: 2: 1–2: 33.
Burke R (2002) Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12: 331–370.
Hill W, Stead L, Rosenstein M et al (1995) Recommending and Evaluating Choices in a Virtual Community of use. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM Press/Addison-Wesley, New York, CHI’ 95, pp 194–201.
Cohen WW, Fan W (2000) Web-Collaborative Filtering: Recommending Music by Crawling the web. Comput Netw 33: 685–698.
Lee WS (2001) Collaborative Learning and Recommender Systems. In: Proceedings of the Eighteenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco, pp 314–321.
Billsus D, Pazzani MJ (1998) Learning Collaborative Information Filters. In: Proceedings of the Fifteenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco, pp 46–54.
Breese JS, Heckerman D, Kadie C (1998) Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence 1998. Morgan Kaufmann, San Francisco, pp 43–52.
Hannon J, Bennett M, Smyth B (2010) Recommending Twitter Users to Follow Using Content and Collaborative Filtering Approaches. In: Proceedings of the Fourth ACM Conference on Recommender Systems, ACM Press, New York, pp 199–206.
Salton G (ed) (1988) Automatic Text Processing. Addison-Wesley, Boston.
Zhang L, Zhang Y (2010) Discriminative Factored Prior Models for Personalized Content-Based Recommendation. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, ACM Press, New York, pp 1569–1572.
Gunawardana A, Meek C (2008) Tied Boltzmann Machines for Cold Start Recommendations. In: Proceedings of the 2008 ACM Conference on Recommender Systems, ACM Press, New York, pp 19–26.
Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann Machines for Collaborative Filtering. In: Proceedings of the 24th International Conference on Machine Learning. ACM Press, New York, pp 791–798.
Chu W, Park ST (2009) Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models. In: Proceedings of the 18th International Conference on World Wide Web. ACM Press, New York, pp 691–700.
Nakamura A, Abe N (1998) Collaborative Filtering Using Weighted Majority Prediction Algorithms. In: Proceedings of the Fifteenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco, pp 395–403.
Kawamae N, Sakano H, Yamada T (2009) Personalized Recommendation Based on the Personal Innovator degree. In: Proceedings of the Third ACM Conference on Recommender Systems. ACM Press, New York, pp 329–332.
Zhao S, Du N, Nauerz A et al (2008) Improved Recommendation Based on Collaborative Tagging Behaviors. In: Proceedings of the 13th International Conference on Intelligent User Interfaces, ACM Press, New York, pp 413–416.
Sieg A, Mobasher B, Burke R (2010) Improving the Effectiveness of Collaborative Recommendation With Ontology-Based User Profiles. In: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, ACM Press, New York, pp 39–46.
Getoor L, Sahami M (1999) Using Probabilistic Relational Models for Collaborative Filtering. In: In Workshop on Web Usage Analysis and User Profiling (WEBKDD’99).
Hofmann T (2003) Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval. ACM Press, New York, pp 259–266.
Hofmann T (2004) Latent Semantic Models for Collaborative Filtering. ACM Trans Inf Syst 22: 89–115.
Marlin B (2003) Modeling User Rating Profiles for Collaborative Filtering. In: NIPS*17, MIT Press, Boston.
Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet Allocation. J Mach Learn Res 3: 993–1022.
Ungar L, Foster D (1998) Clustering Methods for Collaborative Filtering. In: Proceedings of the Workshop on Recommendation Systems. AAAI Press, Menlo Park.
Soboroff IM, Nicholas CK (1999) Combining Content and Collaboration in Text Filtering. In: Proceedings of the IJCAI99 Workshop on Machine Learning for Information Filtering, pp 86–91.
Gunawardana A, Meek C (2009) A Unified Approach to Building Hybrid Recommender Systems. In: Proceedings of the Third ACM Conference on Recommender Systems. ACM Press, New York, pp 117–124.
Leung CWK, Chan SCF, Chung FL (2007) Applying Cross-Level Association Rule Mining to Cold-Start Recommendations. In: Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology-Workshops, IEEE Computer Society, Washington DC, USA, WI-IATW’ 07, pp 133–136.
Lecue F (2010) Combining Collaborative Filtering and Semantic Content-Based Approaches to Recommend Web Services. In: Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing. IEEE Computer Society, Washington DC, pp 200–205.
Degemmis M, Lops P, Semeraro G (2007) A Content-Collaborative Recommender that Exploits Wordnet-Based User Profiles for Neighborhood Formation. User Modeling and User-Adapted Interaction 17: 217–255.
Zhang W (2008) Relational Distance-Based Collaborative Filtering. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, USA, SIGIR’ 08, pp 877–878.
Xu R, Wunsch D (2005) Survey of Clustering Algorithms. IEEE Trans Neural Network 16(3): 645–678.
Li C, Biswas G (2002) Unsupervised Learning with Mixed Numeric and Nominal Data. IEEE Trans Knowl Data Eng 14: 673–690.
Cai Y, Leung HF (2008) Multi-Prototype Concept and Object Typicality in Ontology. In: Proceedings of the 21st International Florida Artificial Intelligence Research Society Conference. AAAI Press, pp 470–475.
Murphy GL (2002) The Big Book of Concepts. MIT Press, Boston.
Vanpaemel W, Storms G, Ons B (2005) A Varying Abstraction Model for Categorization. In: CogSci2005. Lawrence Erlbaum, Mahwah, pp 2277–2282.
Barsalou LW (1992) Cognitive Psychology: An Over View for Cognitive Scientists. Lawrence Erlbaum, Hillsdale.
Barsalou LW (1985) Ideals, Central Tendency, and Frequency of Instantiation as Determinants of Graded Structure in Categories. J Exp Psychol Learn Mem Cogn 11(4): 629–654.
Lesot MJ, Mouillet L, Meunier BB (2005) Fuzzy Prototypes Based on Typicality Degrees. In: Proceedings of the 8th Fuzzy Days’04, Springer, Heidelberg.
Tang J, Zhang J, Yao L et al (2008) Arnetminer: Extraction and Mining of Academic Social Networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, KDD’ 08, pp 990–998.
Hofmann T (1999) Probabilistic Latent Semantic Analysis. In: Proceedings of Uncertainty in Artificial Intelligence, UAI99, pp 289–296.
Santini S, Jain R (1995) Similarity Matching. In: ACCV, pp 571–580.
Vozalis M, Margaritis KG (2004) Unison-cf: A Multiple-Component, Adaptive Collaborative Filtering System. In: Proceedings of the Third International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH 2004), pp 255–264.
Xue GR, Lin C, Yang Q et al (2005) Scalable Collaborative Filtering Using Cluster-Based Smoothing. In: SIGIR’ 05: Proceedingsof the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, New York, pp 114–121.
Nati NS, Jaakkola T (2003) Weighted Low-Rank Approximations. In: 20th International Conference on Machine Learning. AAAI Press, pp 720–727.
Li B, Yang Q, Xue X (2009) Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction. In: IJCAI, pp 2052–2057.
Ueberla JP (1996) An Extended Clustering Algorithm for Statistical Language Models. IEEE Trans on Speech and Audio Proceedings 4 (4): 313–316.
Connor M, Herlocker J (2001) Clustering Items for Collaborative Filtering. https:Citeseer.ist.psu.edu/connor01clustering.html. Accessed 12 May 2011.
Katsuhiro Honda AN, Ichihashi H (2008) Collaborative Filtering By User-Item Clustering Based on Structural Balancing Approach. IJCSNSInternational Journal of Computer Science and Network Security 8(12): 190–195.
Galotti KM (2004) Cognitive Psychology In and Out of the Laboratory, 3rd Edn. Wadsworth, Belmont.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2012 Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Cai, Y., Au Yeung, Cm., Leung, Hf. (2012). Applications. In: Fuzzy Computational Ontologies in Contexts. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25456-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-25456-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25455-0
Online ISBN: 978-3-642-25456-7
eBook Packages: Computer ScienceComputer Science (R0)