Abstract
In this paper, we propose a graph-based method for hybrid recommendation. Unlike a simple linear combination of several factors, our method integrates user-based, item-based and content-based techniques more fully. The interaction among different factors are not done once, but by iterative updates. The graph model is composed of target user’s similar-minded neighbors, candidate items, target user’s historical items and the topics extracted from items’ contents using topic modeling. By constructing the concise graph, we filter out irrelevant noise and only retain useful information which is highly related to the target user. Top-N recommendation list is finally generated by using personalized random walk. We conduct a series of experiments on two datasets: movielen and lastfm. Evaluation results show that our proposed approach achieves good quality and outperforms existing recommendation methods in terms of accuracy, coverage and novelty.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009, 4 (2009)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR 1999, pp. 230–237 (1999)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW 2001, pp. 285–295 (2001)
Hofmann, T.: Probabilistic latent semantic indexing. In: SIGIR 1999, vol. 24, pp. 50–57 (1999)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Lops, P., De Gemmis, M., Semeraro, G.: Content-based recommender systems: State of the art and trends. In: Recommender Systems Handbook, pp. 73–105 (2011)
Meng, F., Gao, D., Li, W., Sun, X., Hou, Y.: A unified graph model for personalized query-oriented reference paper recommendation. In: CIKM 2013, pp. 1509–1512 (2013)
Popescul, A., et al.: Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In: UAI 2001, pp. 437–444 (2001)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Communications of the ACM 40(3), 66–72 (1997)
Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: WWW 2007, pp. 271–280 (2007)
Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: SIGIR 2009, pp. 195–202 (2009)
Haveliwala, T.H.: Topic-sensitive pagerank. In: WWW 2002, pp. 517–526 (2002)
Craswell, N., Szummer, M.: Random walks on the click graph. In: SIGIR 2007, pp. 239–246 (2007)
Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: CIKM 2001, pp. 247–254 (2001)
Yildirim, H., Krishnamoorthy, M.S.: A random walk method for alleviating the sparsity problem in collaborative filtering. In: RecSys 2008, pp. 131–138 (2008)
Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Recommender Systems Handbook, pp. 257–297 (2011)
Porteous, I., Newman, D., Ihler, A., Asuncion, A., Smyth, P., Welling, M.: Fast collapsed gibbs sampling for latent dirichlet allocation. In: KDD 2008, pp. 569–577 (2008)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys 2010, pp. 39–46 (2010)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD 2008, pp. 426–434 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zheng, HT., Yan, YH., Zhou, YM. (2015). Graph-Based Hybrid Recommendation Using Random Walk and Topic Modeling. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-25255-1_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25254-4
Online ISBN: 978-3-319-25255-1
eBook Packages: Computer ScienceComputer Science (R0)