Abstract
Heterogeneous one-class collaborative filtering (HOCCF) is a recent and important recommendation problem which involves two different types of one-class feedback such as purchases and examinations. In this paper, we propose a generic asymmetric pairwise preference assumption and a novel like-minded user-group construction strategy for the HOCCF problem. Specifically, our generic assumption contains six different pairwise preference relations derived from the heterogeneous feedback, where we introduce a series of weighting strategies to make our assumption more reasonable. Our group construction strategy introduces richer interactions within user-groups, which is expected to learn the users’ preference more accurately. We then design a novel recommendation model called asymmetric pairwise preference learning (APPLE). Extensive empirical studies show that our APPLE can recommend items significantly more accurately than the closely related state-of-the-art methods on three real-world datasets.
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References
Chen, C., Zhang, M., Zhang, Y., Ma, W., Liu, Y., Ma, S.: Efficient heterogeneous collaborative filtering without negative sampling for recommendation. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp. 19–26 (2020)
Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)
Ding, J., et al.: Improving implicit recommender systems with view data. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. IJCAI 2018, pp. 3343–3349 (2018)
He, X., He, Z., Song, J., Liu, Z., Jiang, Y.G., Chua, T.S.: NAIS: neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30(12), 2354–2366 (2018)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web. WWW 2017, pp. 173–182 (2017)
Hu, Y., Yehuda, K., Chris, V.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining. ICDM 2008, pp. 263–272 (2008)
Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-N recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2013, pp. 659–667 (2013)
Ouyang, S., Li, L., Pan, W., Ming, Z.: Asymmetric Bayesian personalized ranking for one-class collaborative filtering. In: Proceedings of the 13th ACM Conference on Recommender Systems. RecSys 2019, pp. 373–377 (2019)
Pan, R., et al.: One-class collaborative filtering. In: Proceedings of the 8th IEEE International Conference on Data Mining. ICDM 2008, pp. 502–511 (2008)
Pan, W., Chen, L., Ming, Z.: Personalized recommendation with implicit feedback via learning pairwise preferences over item-sets. Knowl. Inf. Syst. 58(2), 295–318 (2019). https://doi.org/10.1007/s10115-018-1154-5
Pan, W., Liu, M., Ming, Z.: Transfer learning for heterogeneous one-class collaborative filtering. IEEE Intell. Syst. 31(4), 43–49 (2016)
Qiu, H., Liu, Y., Guo, G., Sun, Z., Zhang, J., Nguyen, H.T.: BPRH: Bayesian personalized ranking for heterogeneous implicit feedback. Inf. Sci. 453, 80–98 (2018)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. UAI 2009, pp. 452–461 (2009)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Annual Conference on Neural Information Processing Systems. NeurIPS 2007, pp. 1257–1264 (2007)
Acknowledgement
We thank the support of National Natural Science Foundation of China Nos. 61872249, 61836005 and 61672358. Weike Pan and Zhong Ming are the corresponding authors for this work.
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Ni, Y., Zhan, Z., Pan, W., Ming, Z. (2020). Asymmetric Pairwise Preference Learning for Heterogeneous One-Class Collaborative Filtering. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_34
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