Autosoft Journal

Online Manuscript Access

The Effect of Neighborhood Selection on Collaborative Filtering and a Novel Hybrid Algorithm



Recommender systems are widely used in industry and are still active research areas in academia. For many businesses, they have become indispensable business tools. Producing accurate results for such systems is important for the operations of the businesses. For this reason, various algorithms and approaches have been developed for recommender systems to increase the prediction accuracy. Collaborative filtering is one of the most successful approaches. In collaborative filtering, in order to predict more accurately, it is recommended to determine user2019s active neighbors. k-nearest neighbor (k-NN) algorithm is one of the most widely used neighbor selection algorithms. However, k-NN algorithm uses a fixed k value that reduces the accuracy of the prediction. In this paper, we present two novel approaches to increase the prediction accuracy of recommender systems; k%-nearest neighbor (k%-NN) algorithm to determine the appropriate k value for a user and a hybrid algorithm that combines a collaborative filtering technique and content-based approach. Our test results demonstrate that k%-NN algorithm increases the average prediction accuracy compared to the traditional k-NN algorithm. Additionally, when the proposed hybrid algorithm is used with k%-NN, it produces more accurate results than the conventional collaborative filtering technique and content-based approach.



Total Pages: 9
Pages: 261-269


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways

Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Volume: 23
Issue: 2
Year: 2016

Cite this document


Adomavicius, G., 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 (2005): 734-749. Crossref. Web.

Anand S.S. Proceedings of the 2003 international conference on intelligent techniques for web personalization

Barranco, Manuel J., and Luis Martínez. "A Method for Weighting Multi-Valued Features in Content-Based Filtering." Lecture Notes in Computer Science (2010): 409-418. Crossref. Web.

Breese J.S. Proceedings of the fourteenth conference on uncertainty in artificial intelligence

Chen, Zhi-Sheng, Jyh-Shing Roger Jang, and Chin-Hui Lee. "A Kernel Framework for Content-Based Artist Recommendation System in Music." IEEE Transactions on Multimedia 13.6 (2011): 1371-1380. Crossref. Web.

"Journal of the American Society for Information Science." n. pag. Crossref. Web.

Ding Y. Proceedings of the 17th australasian database conference-volume 49

Foltz, Peter W., and Susan T. Dumais. "Personalized Information Delivery: An Analysis of Information Filtering Methods." Communications of the ACM 35.12 (1992): 51-60. Crossref. Web.

Formoso, Vreixo et al. "Distributed Architecture for k-Nearest Neighbors Recommender Systems." World Wide Web 18.4 (2014): 997-1017. Crossref. Web.

Goldberg, David et al. "Using Collaborative Filtering to Weave an Information Tapestry." Communications of the ACM 35.12 (1992): 61-70. Crossref. Web.

Gong, Songjie. "A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering." Journal of Software 5.7 (2010): n. pag. Crossref. Web.

Herlocker J.L. Proceedings of the 22nd annual international acm sigir conference on research and development in information retrieval

Herlocker, Jon, Joseph A. Konstan, and John Riedl. Information Retrieval 5.4 (2002): 287-310. Crossref. Web.

Herlocker, Jonathan L. et al. "Evaluating Collaborative Filtering Recommender Systems." ACM Transactions on Information Systems 22.1 (2004): 5-53. Crossref. Web.

Jannach, D., Zanker, M., Felfernig, A. & Friedrich, G. (2011). Recommender system An introduction Cambridge. New York: University Press.

Malone, Thomas W et al. "Intelligent Information-Sharing Systems." Communications of the ACM 30.5 (1987): 390-402. Crossref. Web.

Melville P. Encyclopedia of machine learning

Mooney, Raymond J., and Loriene Roy. "Content-Based Book Recommending Using Learning for Text Categorization." Proceedings of the fifth ACM conference on Digital libraries - DL ”00 (2000): n. pag. Crossref. Web.

uddin, Mohammed Nazim, Jenu Shrestha, and Geun-Sik Jo. "Enhanced Content-Based Filtering Using Diverse Collaborative Prediction for Movie Recommendation." 2009 First Asian Conference on Intelligent Information and Database Systems (2009): n. pag. Crossref. Web.

Pazzani, Michael J., and Daniel Billsus. "Content-Based Recommendation Systems." Lecture Notes in Computer Science 325-341. Crossref. Web.

Pu, Pearl, Li Chen, and Pratyush Kumar. "Evaluating Product Search and Recommender Systems for E-Commerce Environments." Electronic Commerce Research 8.1-2 (2008): 1-27. Crossref. Web.

Rashid A.M WebKDD 2006: KDD Workshop on Web Mining and Web Usage Analysis

Resnick, Paul et al. "GroupLens." Proceedings of the 1994 ACM conference on Computer supported cooperative work - CSCW ”94 (1994): n. pag. Crossref. Web.

Salter, J., and N. Antonopoulos. "CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering." IEEE Intelligent Systems 21.1 (2006): 35-41. Crossref. Web.

Sarwar, Badrul et al. "Item-Based Collaborative Filtering Recommendation Algorithms." Proceedings of the tenth international conference on World Wide Web - WWW ”01 (2001): n. pag. Crossref. Web.

Vozalis E. The 6th Hellenic European conference on computer mathematics & its applications

Zou, Tengfei et al. "An Effective Collaborative Filtering Via Enhanced Similarity and Probability Interval Prediction." Intelligent Automation & Soft Computing 20.4 (2014): 555-566. Crossref. Web.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048