Abstract
There is a lack of interactive rapid and visual recommender systems for recommending interior design prototype drawing to the consigner. Therefore, the purpose of this study is to propose a virtual reality based recommender system as a platform to retrieve a design drawing from a historical interior design drawings database, and to recommend the retrieved drawing to the consigner as a prototype drawing. The as yet untapped recommender system consists of a virtual reality based query system, a database system and a pattern matching engine. A preliminary case study of the recommender system was made, including a front end data collecting and preprocessing phrase and a back end pattern matching and recommendation phase. The proposed recommender system has been shown to greatly help the designer in storing historical drawing items, extracting relevant design features, as well as to direct the consigner from the query system to the historical database so as to access the most matching design drawing that best suites the consigner’s interests and requirements.
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References
Ricci, F., Rokach, L.: Introduction to Recommender Systems Handbook. Recommender Systems Handbook, pp. 1–35. Springer (2011)
Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)
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 Artificial Intelligence, Madison, Wisconsin, pp. 43–52 (1998)
Pennock, D.M., Horvitz, E.: Analysis of the Axiomatic Foundations of Collaborative Filtering. In: AAAI -99 Workshop on AI for Electronic Commerce at the 16th National Conference on Artificial Intelligence, Orlando, Florida (1999)
Larsen, B., Aone, C.: Fast and Effective Text Mining Using Linear-Time Document Clustering. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, pp. 16–22 (1999)
Nahm, U.Y., Bilenko, M., Mooney, R.J.: Two Approaches to Handling Noisy Variation in Text Mining. In: Proceedings of the ICML-2002 Workshop on Text Learning, Sydney, Australia, pp. 18–27 (2002)
Ye, J.: Cosine Similarity Measures for Intuitionistic Fuzzy Sets and their Applications. Mathematical and Computer Modeling 53(1), 91–97 (2011)
Yates, R.B., Neto, B.R.: Modern Information Retrieval. Addison Wesley, New York (1999)
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Lin, KS., Ke, MC. (2015). A Virtual Reality Based Recommender System for Interior Design Prototype Drawing Retrieval. In: Barbucha, D., Nguyen, N., Batubara, J. (eds) New Trends in Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 598. Springer, Cham. https://doi.org/10.1007/978-3-319-16211-9_15
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DOI: https://doi.org/10.1007/978-3-319-16211-9_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16210-2
Online ISBN: 978-3-319-16211-9
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