Abstract
In this paper, we derive a probabilistic ranking framework for diversifying the recommendations of baseline methods. Unlike conventional approaches to balance relevance and diversity, we produce the diversified list by maximizing user’s current marginal aspect preference, thus avoiding the hyperparameters in making the tradeoff. Before diversification, we adopt clustering to generate a much smaller set of candidate items based on three requirements: efficiency, relevance and diversity. As a result, it helps us not only reduce the search space greatly but also promote a slight increase in performance. Our framework is flexible to incorporate new preference aspects and apply new marginal aspect preference algorithms. Evaluation results show that our method can get better diversity than others and maintain comparable accuracy to baseline methods, thus a better balance between relevance and diversity.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Adomavicious, G., Tuzhilin, A.: Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions. IEEE TKDE 17(6), 734–749 (2004)
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)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys 2010, pp. 39–46 (2010)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer (8), 30–37 (2009)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD 2008, pp. 426–434 (2008)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Vargas, S., Castells, P., Vallet, D.: Intent-oriented diversity in recommender systems. In: SIGIR 2011, pp. 1211–1212 (2011)
Su, R., Yin, L.A., Chen, K., et al.: Set-oriented personalized ranking for diversified top-n recommendation. In: ACM Conference on Recommender Systems, pp. 415–418 (2013)
Clarke, C.L.A., Kolla, M., Cormack, G.V., et al.: Novelty and diversity in information retrieval evaluation. In: SIGIR 2008, pp. 659–666 (2008)
Lathia, N., Hailes, S., Capra, L., et al.: Temporal diversity in recommender systems. In: SIGIR 2010, pp. 210–217 (2010)
Shi, Y., Zhao, X., Wang, J., Larson, M., Hanjalic, A.: Adaptive diversification of recommendation results via latent factor portfolio. In: SIGIR 2012, pp. 175–184 (2012)
Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: WWW 2005, pp. 22–32 (2005)
Ziegler, C., Lausen, G., Schmidt-Thieme, L.: Taxonomy-driven computation of product recommendations. In: CIKM 2004, pp. 406–415 (2004)
Dang, V., Croft, W.B.: Diversity by proportionality an election-based approach to search result diversification. In: SIGIR 2012, pp. 65–74 (2012)
Santos, R., Macdonald, C., Ounis, I.: Exploiting Query Reformulations for Web Search Result Diversification. In: WWW 2010, pp. 881–890 (2010)
Koren, Y.: Collaborative filtering with temporal dynamics. In: KDD 2009, 447–456 (2009)
Hurley, N., Zhang, M.: Novelty and diversity in top-N recommendation-analysis and evaluation. TOIT 10(4), 14, 1–30 (2011)
Carbonell, J.G., Goldstein, J.: The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. In: SIGIR 1998, pp. 335–336 (1998)
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)
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
Yan, YH., Zhou, YM., Zheng, HT. (2015). PDMA: A Probabilistic Framework for Diversifying Recommendation Lists. 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_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-25255-1_55
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)