Author Details

In content-based 3D object retrieval, searching for a query object in an extensive database is essential. The existing retrieval algorithms adopt the naïve search algorithm in searching for queryobjects. This approach leads to a high cost of search and retrieval that needs to be addressed. In this research, we introduced an algorithm that calculates each cluster’s representative, that is, a 3D object that has the least dissimilarity on average to each object in the cluster. This is to improve the overall retrieval performance of [1]. We first compute the optimal hierarchical level of the database using a dendrogram, and then calculate the total number of clusters in the database. Afterwards, we calculate the feature descriptor of each cluster. When a user chooses a query object, our system then compares the feature descriptor of the query object with each of the cluster ’s representation and search the cluster with the smallest distance to the query, thereby improving the querysearching and improving the system. The proposed system was implemented, and the system's performance was evaluated against the benchmark datasets. This revealed that the execution time was reduced by 21% and increased Precision and Recall by 30.7% and 33.1%, respectively. In the future, it is suggested that the technique be improved by incorporating different machine learning algorithms and comparing the results.

Keywords

3D Object Retrieval, Hierarchical Clustering, Cluster Representative.

Full Text

References

R. Getto, A. Kuijper, and W. F. Dieter. Unsupervised 3D Object Retrieval with Parameter-Free Hierarchical Clustering. In Proceedings of CGI 17, 2017, 27-30 Yokohama, Japan.

P. Min, M. Kazhdan, and T. Funkhouser. A Comparison of Text and Shape Matching for Retrieval of Online 3D Models. Research and Advanced Technology for Digital Libraries 2014, 209-220 Bath:UK

B. Bostus, D. Keim, D. Saupe, and T. Schreck, Content based 3D object retrieval. IEEE Computer Graphics and Application, 27(4), 2007, 22-27.

Y. Gao, Q. Dai. View-based 3D object Retrieval. Neurocomputing, 252(C), 2015, 58-66.

F. Yu,Z. Lu, and H. Luo. Three Dimensional Model Analysis and processing. China: Springer 2011.

H. Laga, Y. Guo, H. Tabia,R. B.Fisher, andM. Bennamoun. 3D shape Analysis Fundamentals, Theory, and Application.2019, USA: Wiley

J. W. H. Tangelder, and R. C. Veltkamp. A survey of content-based 3D shape retrieval methods. Multimedia Tools and Applications, 39(3), 2007, 441-471

B. Li, Y. Lu, C. Li. A. Godil, T. Schreck, M. Aono, and H. Johan. Shrec’14 track: extended large scale sketch-based 3D shape retrieval. In: Eurographics Workshop on 3D Object Retrieval. 2014,121–130. Strasbourg: France.

D. Xu, and H. Li. 3D shape retrieval integrated with classification information. In Image and Graphics. Fourth International Conference on Image and Graphics, 2007, 774-779Sichuan, China.

P. Papadakis, I. Pratikakis, T. Trafalis, T. Theoharis, and S. Perantonis. Relevance feedback in content-based 3D Object Retrieval: A comparative study. Computer-Aided Design and Applications, 5(5), 2008, 753–763.

D. V. Vranic.3D model retrieval. (Doctoral Dissertation). University of Leipzig, Leipzig, Germany, 2004.

B. Li, and H. Johan. 3D model retrieval using hybrid features and class information. Multimedia Tools and Applications, 62(3), 2013, 821–846.

S. Hou, K. Lou, and K. Ramani, K. SVM-based semantic clustering and retrieval of a 3D model database. Computer-Aided Design and Applications, 2(1),2005, 155–164.

C. Ip, Y.Regli, L. W. C. Sieger, andA. Shokoufandeh. Automated learning of model classifications. In Proceedings of the eighth ACM symposium on Solid modeling and applications 2003, 322-327. Washington: USA.

Y. Wang,Z. Liu,F. Pang, andH. Li. Boosting 3D model retrieval with class vocabularies and distance vector revision. In TENCON 2015-2015 IEEE Region 10 Conference 2015, 1–5 Macao: China

A. Tatsuma,and M. Aono. Multi-Fourier spectra descriptor and augmentation with spectral clustering for 3D shape retrieval. The Visual Computer 25(8), 2009, 785–804

K. Bajaj, and A. Arora, Improving the intrusion detection using discriminative machine learning approach and improve the Time Complexity by Data Mining Feature selection methods, International Journal of computer science, vol. 76, Aug,2013

B. S. Everitt, S. Landau, M. Leese and D. Stahl Cluster Analysis. Wiley, London, 2011, p348.

P. Shilane, P. Min, M. Kazhdan, and T. Funkhouser. The Princeton shape benchmark. Proceedings of the Shape Modeling International 2004, pp.167– 178, Washington: USA.

A. Godil, Z. Lian, H. Dutagaci, and R. Fang, T. P.Vanamali, and C. P. Cheung. Benchmarks, performance evaluation and contests for 3D shape retrieval. Code Vectors Clustering. IEEE Journal: 2018, pp 7-18

Enhancing Personalized Book Recommender System.

Abstract Views :105 | PDF Views:0

Authors

Abdulgafar Usman ¹, Abubakar Roko ¹, Aminu B. Muhammad ¹, Abba Almu ¹

Affiliations
1 Department of Computer Science, Usmanu Danfodiyo University, Sokoto., NG

Source

International Journal of Advanced Networking and Applications, Vol 14, No 3 (2022), Pagination: 5486-5492

Abstract

Recommender systems (Rs) are widely used to provide recommendations for a collection of items or products that may be of interest to user or a group of users. Because of its superior performance, Content-Based Filtering (CBF) is one of the approaches that are commonly utilized in real-world Rs using Time-Frequency and Inverse Document Frequency (TF-IDF) to calculate document similarities. However, it computesdocument similarity directly in the word-count space. We propose a user-based collaborative filtering (UBCF) method to solve the problem of limited in content analysis which leads to a low prediction rate for large vocabulary. In this study, we present an algorithm that utilises Euclidean distance similarity function, to solve the identified problem. Th e performance of the proposed scheme was evaluated against the benchmark scheme usingdifferent performance metrics. The proposed scheme was implemented and an experimentally tested by using the benchmark datasets (Amazon review datasets). The results revealed that, the proposed scheme achieved better performance than the existing recommender system in terms of Root Mean Square Error (RMSE) which reduces the errors by 29% and also increase the Precision and Recall by 51.4%, and 55.8% respectively in the 1 million datasets.

Keywords

Recommender System, Content-Based, Collaborative Filtering, Personalized Recommendations, Similarity Function.

Full Text

References

M. Prem, and S. Vikas. Recommender Systems. (Encyclopedia of Machine Learning), 2010.

C. Pan, and W. Li. Research paper recommendation with topic analysis. In computer design and Application IEEE 2010, 4-264.

P. Pu, L. Chen, and R, Hu. A user-centric evaluation framework for recommender systems. In Proceedings of the fifth ACM conference on Recommender Systems(RecSys’11), ACM, New York, NY, USA; 2010, P. 57-164.

S. Sanjeevan, S. Alireza, R. Hossein, and M. Asad. Recommender systems in e-commerce. In Proceedings of the World Automation Congress (WAC). IEEE, 2014, 179–184

H. Kang, and J. Seong, SVM and collaborative filtering-based prediction of user preference for digital fashion recommendation systems. IEICE Transactions on Information and Systems, 2007, 2100 –2103.

L. George, and C. Petros. A hybrid approach for movie recommendation. Multimedia Tools and Applications. Conference on Innovative Applications of Artificial Intelligence2008, 55– 70.

J. Bobadilla, F. Ortega, A. Hernando, and A. GutiéRrez. Recommender systems survey. Knowledge-Based System, 9(46): 2013, 109 -132.

H. Zamani, and A. Shakery. A language model-based framework for multi-publisher content-based recommender systems. Forspringer International Journal on Information Retrieval,2(1): 2018, 369-409.

U. Hanani, B. Shapira, and P. Shoval. Information filtering: Overview of issues, research, and systems. User Modeling and User-Adapted Interaction, 11(3), 2001, 203–259.

P. Lops, M. Gemmis, and G. Semeraro. Content-based recommender systems: State of the art and trends. In information Retrieval.2011, 73–105, Springer.

S. Gong and J. Softw. A Collaborative Filtering Recommendation Algorithm Based On User Clustering and Item Clustering.Conference on Innovative Applications of Artificial Intelligence. 2010, 745-752.

J. Sarun and K. Paween. Automatic non-personalized book recommender algorithm for Bookstore shelf management: The 3rd International Conference on Digital Arts, Media, and Technology (ICDAMT IEEE): 2018, 1-5.

R. Chhavirana and K. J. Sanjay. Building a Book Recommender system using time-based Content Filtering. WSEAS Transactions on Computers Issue 2(11): 2012, 49-78.

M. Suthathip and M. Songrit. A recommendation model for personalized book lists. In Communications and Information Technologies (ISCIT) International Symposium on IEEE,2010, 389-394.

P. Jomsri. Book recommendation system for digital library based on user profiles by using association rule. In Innovative Computing Technology (INTECH), Fourth International Conference on IEEE, Luton., England: 2014, 130-134.

J. Chen, C.U. Zhao, C. Chen. Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering” Complex & Intelligent Systems published online by Springer 30 January, 2019, 147-156.

H. Xia, Y. Luo, and Y. Liu. Attention Neural Collaboration Filtering Base on GRU for Recommender Systems.” Complex and Intelligent Systems. Published @ Springer. 2020

X. Liao, X. Li, Q. Xu, H. Wu, and Y. Wang. Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction”Applied Science. 2021, 72-45 http://www.mdpi.com/journal/applsci

J. McAuley, and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. In Proceedings of the 7 th ACM conference on recommender systems. 7(13): 2013, 165-172.

P. Priyanga, and A. R. N. B. Kamal. Methods of Mining the Data from Big Data and Social Networks Based on Recommender System. International Journal of Advanced Networking & Applications. 8(5): 2017, 55-60.

Username
Password
Remember me