Abstract
Conventional recommendation systems often use binary relations matrices, which could not represent raw data sets efficiently and uniformly. The graph model can represent multiple relationships and form a unified standard of the feature space to recommend the candidate items. Existing graph-based work is generally based on the path to establish the feature space of the data, only concerned about the impact of an item on the description of the user. Utilizing combination features to construct user profiles, this paper concentrates on the contribution maked by combination of items and designs a user-based collaborative filtering algorithm (CFC), and validates the validity of the algorithm in the prototype of the proposed system. The experimental results show that the recommendation algorithms can significantly improve accuracy of the recommendation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nguyen, H., Dinh, T.: A modified regularized non-negative matrix factorization for movielens. In: 2012 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), pp. 1–5. IEEE (2012)
Li, Q., Kim, B.: Constructing user profiles for collaborative recommender system. In: Advanced Web Technologies and Applications, pp. 100–110 (2004)
Huang, Z., Chung, W., Ong, T.H., et al.: A graph-based recommender system for digital library. In: Proceedings of the 2nd ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 65–73. ACM (2002)
Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81(1), 53–67 (2010)
Takács, G., Pilászy, I., Németh, B., et al.: Scalable collaborative filtering approaches for large recommender systems. J. Mach. Learn. Res. 10, 623–656 (2009)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 39–46. ACM (2010)
Neo Technology. Powering Recommendations with a Graph Database. https://neo4j.com/
Vicknair, C., Macias, M., Zhao, Z., et al.: A comparison of a graph database and a relational database: a data provenance perspective. In: Proceedings of the 48th Annual Southeast Regional Conference, p. 42. ACM (2010)
Linden, G.D., Jacobi, J.A., Benson, E.A.: Collaborative recommendations using item-to-item similarity mappings: U.S. Patent 6,266,649[P], 24 July 2001
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Beel, J., Gipp, B., Langer, S., et al.: Research-paper recommender systems: a literature survey. Int. J. Digit. Libr. 17(4), 305–338 (2016)
Schafer, J., Frankowski, D., Herlocker, J., et al.: Collaborative Filtering Recommender Systems. In: The Adaptive Web, pp. 291–324 (2007)
Wang, J., De Vries, A.P., Reinders, M.J.T.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 501–508. ACM (2006)
Lee, S., Park, S., Kahng, M., et al.: Pathrank: a novel node ranking measure on a heterogeneous graph for recommender systems. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 1637–1641. ACM (2012)
Yu, X., Ren, X., Sun, Y., et al.: Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 283–292. ACM (2014)
Islam, M.S., Liu, C., Li, J.: Efficient answering of why-not questions in similar graph matching. IEEE Trans. Knowl. Data Eng. 27(10), 2672–2686 (2015)
Ma, S., Li, J., Hu, C., et al.: Big graph search: challenges and techniques. Front. Comput. Sci. 10(3), 387–398 (2016)
Noia, T.D., Ostuni, V.C., Tomeo, P., et al.: Sprank: semantic path-based ranking for top-n recommendations using linked open data. ACM Trans. Intell. Syst. Technol. (TIST) 8(1), 9 (2016)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant No. 61572247).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Hu, X., Liu, Q., Li, L., Liu, P. (2018). A High Precision Recommendation Algorithm Based on Combination Features. In: Liu, C., Zou, L., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10829. Springer, Cham. https://doi.org/10.1007/978-3-319-91455-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-91455-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91454-1
Online ISBN: 978-3-319-91455-8
eBook Packages: Computer ScienceComputer Science (R0)