A hybrid collaborative filtering model with context and folksonomy for social recommendation

A hybrid collaborative filtering model with context and folksonomy for social recommendation

Xiaoyi Deng Cheng Wang 

Business School, Huaqiao University, Quanzhou 362021, China

Research Center for Applied Statistics and Big Data, Huaqiao University, Xiamen 361021, China

Corresponding Author Email: 
londonbell.deng@gmail.com
Page: 
139-157
|
DOI: 
https://doi.org/10.3166/ISI.23.5.139-157
Received: 
| |
Accepted: 
| | Citation

OPEN ACCESS

Abstract: 

To address data sparsity problem and lack of context in neighbourhood-based collaborative filtering (CF), this paper proposes a hybrid CF model combining context and tag information. Firstly, all users were divided into different groups by their profile and contextual information using clustering, aiming to reduce the sparsity and dimension of ratings data. Then, a folksonomy network model (FNM) was developed based on tag information to analyze the relevance between different items. Then, the FNM was incorporated into the similarity measuring process of neighbourhood-based CF for the improvement of recommendation accuracy. Through the experiments on three real-world datasets, it is clear that our method outperforms other methods in recommendation quality, which means our model is more applicable in situations where context and folksonomy are critical to the success of the application, just like in social commerce and virtual community websites.

Keywords: 

collaborative filtering, hybrid recommendation, context, folksonomy, social tag

1. Introduction
2. Related work
3. Proposed model
4. Experimental results
5. Conclusion
Acknowledgment

The authors gratefully acknowledge the supports from the National Natural Science Foundation of China (No.71401058), the Project of Science and Technology Plan of Fujian Province of China (No.2017H01010065), and the Program for New Century Excellent Talents in Fujian Province University, NCETFJ (No.Z1625110).

  References

Abbas A., Zhang L., Khan S. U. (2015). A survey on context-aware recommender systems based on computational intelligence techniques. Computing, Vol. 97, No. 7, pp. 667-690. https://doi.org/10.1007/s00607-015-0448-7

Adomavicius G., Tuzhilin A. (2010). Context-aware recommender systems. AI Magazine, Vol. 16, No. 3, pp. 2175-2178. https://doi.org/10.1016/j.eswa.2013.09.005

Anand D., Bharadwaj K. K. (2011). Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities. Expert Systems with Applications, Vol. 38, No. 5, pp. 5101-5109. https://doi.org/10.1016/j.eswa. 2010.09.141

Belem F. M., Martins E. F., Almeida J. M., Gonçalves M. A. (2014). Personalized and object-centered tag recommendation methods for Web 2.0 applications. Information Processing & Management, Vol. 50, No. 4, pp. 524-553. https://doi.org/10.1016/j.ipm.2014.03.002

Biancalana C., Gasparetti F., Micarelli A., Sansonetti G. (2013). An approach to social recommendation for context-aware mobile services. ACM Transactions on Intelligent Systems & Technology, Vol. 4, No. 1, pp. 1-31. https://doi.org/10.1145/2414425.2414435

Bobadilla J., Ortega F., Hernando A., Gutierrez A. (2013). Recommender systems survey. Knowledge-Based Systems, Vol. 46, No. 1, pp. 109-132. https://doi.org/10.1016/j.knosys.2013.03.012

Champiri Z. D., Shahamiri S. R., Salim S. S. B. (2015). A systematic review of scholar context-aware recommender systems. Expert Systems with Applications, Vol. 42, No. 3, pp. 1743-1758. https://doi.org/10.1016/j.eswa.2014.09.017

Chen C. C., Wan Y. H., Chung M. C., Sun Y. C. (2013). An effective recommendation method for cold start new users using trust and distrust networks. Information Sciences, Vol. 224, No. 2, pp. 19-36. https://doi.org/10.1016/j.ins.2012.10.037

Deng X. (2016). Combining neighborhood based collaborative filtering with tag information for personalized recommendation. International Journal of Multimedia and Ubiquitous Engineering, Vol. 11, No. 9, pp. 277-288. http://dx.doi.org/10.14257/ijmue.2016.11.9.28

Hariri N., Mobasher B., Burke R. (2012). Context-aware music recommendation based on latent topic sequential patterns. The 6th ACM Conference on Recommender Systems, pp. 131-138. https://doi.org/10.1145/2365952.2365979

Kim H. N., El-Saddik A., Jo G. S. (2011). Collaborative error-reflected models for cold-start recommender systems. Decision Support Systems, Vol. 51, No. 3, pp. 519-531. https://doi.org/10.1016/j.dss.2011.02.015

Lika B., Kolomvatsos K., Hadjiefthymiades S. (2014). Facing the cold start problem in recommender systems. Expert Systems with Applications, Vol. 41, No. 4, pp. 2065-2073.

Liu J., Wu C., Liu W. (2013). Bayesian probabilistic matrix factorization with social relations and item contents for recommendation. Decision Support Systems, Vol. 55, No. 3, pp. 838-850. https://doi.org/10.1016/j.dss.2013.04.002

Lv L., Medo M., Chi H. Y., Zhang Y. C., Zhang Z. K., Zhou T. (2012). Recommender systems, Physics Reports, Vol. 519, No. 1, pp. 1-49. https://doi.org/10.1016/j.physrep.2012.02.006

Najafabadi M. K., Mahrin M. N. (2016). A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback. Artificial Intelligence Review, Vol. 45, No. 2, pp. 1-35. https://doi.org/10.1007/s10462-015-9443-9

Naseri S., Bahrehmand A., Ding C., Chi C. H. (2013). Enhancing tag-based collaborative filtering via integrated social networking information. IEEE International Conference on Advances in Social Networks Analysis and Mining, pp. 760-764. https://doi.org/10.1145/2492517.2492658

Ozsoy M. G., Polat F. (2013). Trust based recommendation systems. Advances in Social Networks Analysis and Mining, pp. 1267-1274. https://doi.org/10.1145/2492517.2500276

Pereira A. L. V., Hruschka E. R. (2015). Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowledge-Based Systems, Vol. 82, pp. 11-19. https://doi.org/10.1016/j.knosys.2015.02.016

Rodriguez J., Bravo M., Guzman R. (2013). Multidimensional ontology model to support context-aware systems. The 27th AAAI Conference on Artificial Intelligence, pp. 53-60.

Said A., Bellogin A. (2014). Comparative recommender system evaluation: benchmarking recommendation frameworks. The 8th ACM Conference on Recommender Systems, pp. 129-136. https://doi.org/10.1145/2645710.2645746

Sarwar B., Karypis G., Konstan J., Riedl J. (2001). Item based collaborative filtering recommendation algorithms. The 10th International Conference on World Wide Web, pp. 285-295. https://doi.org/10.1145/371920.372071

Sharma M., Zhou J., Hu J., Karypis G. (2015). Feature-based factorized bilinear similarity model for cold-start top-n item recommendation. 2015 SIAM International Conference on Data Mining, pp. 190-198. https://doi.org/10.1137/1.9781611974010.22

Shi Y., Larson M., Hanjalic A. (2014). Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Computing Surveys, Vol. 47, No. 1, pp. 1-45. https://doi.org/10.1145/2556270

Wang H., Li W. J. (2015). Relational collaborative topic regression for recommender systems. IEEE Transactions on Knowledge & Data Engineering, Vol. 27, No. 5, pp. 1343-1355. https://doi.org/10.1109/TKDE.2014.2365789

Wang X., Lu W., Ester M., Wang C., Chen C. (2016). Social recommendation with strong and weak ties. Conference on Information and Knowledge Management, pp. 5-14. https://doi.org/10.1145/2983323.2983701

Wang Z., Liang J., Li R., Qian Y. (2016). An approach to cold-start link prediction: establishing connections between non-topological and topological information. IEEE Transactions on Knowledge & Data Engineering, Vol. 28, No. 11, pp. 2857-2870. https://doi.org/10.1109/TKDE.2016.2597823

Wu C. C., Shih M. J. (2015). A context-aware recommender system based on social media. Computer Science, International Conference on Data Mining & Mechanical Engineering, pp. 15-19. http://dx.doi.org/10.15242/IIE.E04 15007

Yang X., Guo Y., Liu Y., Steck H. (2014). A survey of collaborative filtering based social recommender systems. Computer Communications, Vol. 41, No. 5, pp. 1-10. https://doi.org/10.1016/j.comcom.2013.06.009