Abstract
Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains. Existing CDCF models are either based on matrix factorization or deep neural networks. Independent use of either of the techniques in isolation may result in suboptimal performance for the prediction task. Also, most of the existing models face challenges particularly in handling diversity between domains and learning complex non-linear relationships that exist amongst entities (users/items) within and across domains. In this work, we propose an end-to-end neural network model – NeuCDCF, to address these challenges in a cross-domain setting. More importantly, NeuCDCF is based on a wide and deep framework and learns the representations jointly using both matrix factorization and deep neural networks. We perform experiments on four real-world datasets and demonstrate that our model performs better than state-of-the-art CDCF models.
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Notes
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This example is inspired from [18].
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http://jmcauley.ucsd.edu/data/amazon (we rename CDs-and-Vinyl as Music).
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References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: ICLR (2015)
Cantador, I., Fernández-Tobías, I., Berkovsky, S., Cremonesi, P.: Cross-domain recommender systems. In: Recommender Systems Handbook, pp. 919–959 (2015)
Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: RecSys, pp. 7–10. ACM (2016)
Elkahky, A.M., Song, Y., He, X.: A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: WWW, pp. 278–288 (2015)
Gao, S., Luo, H., Chen, D., Li, S., Gallinari, P., Guo, J.: Cross-domain recommendation via cluster-level latent factor model. In: ECML PKDD (2013)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep learning, vol. 1. MIT Press, Cambridge (2016)
Guo, G., Zhang, J., Yorke-Smith, N.: TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: AAAI (2015)
He, M., Zhang, J., Yang, P., Yao, K.: Robust transfer learning for cross-domain collaborative filtering using multiple rating patterns approximation. In: WSDM, pp. 225–233. ACM (2018)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)
Kanagawa, H., Kobayashi, H., Shimizu, N., Tagami, Y., Suzuki, T.: Cross-domain recommendation via deep domain adaptation. In: ECIR, pp. 20–29 (2019)
Kang, S., Hwang, J., Lee, D., Yu, H.: Semi-supervised learning for cross-domain recommendation to cold-start users. In: CIKM, pp. 1563–1572 (2019)
Kim, D., Park, C., Oh, J., Lee, S., Yu, H.: Convolutional matrix factorization for document context-aware recommendation. In: RecSys, pp. 233–240. ACM (2016)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)
Li, B., Yang, Q., Xue, X.: Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction. In: IJCAI, vol. 9, pp. 2052–2057 (2009)
Li, B., Yang, Q., Xue, X.: Transfer learning for collaborative filtering via a rating-matrix generative model. In: ICML, pp. 617–624 (2009)
Li, X., She, J.: Collaborative variational autoencoder for recommender systems. In: SIGKDD, pp. 305–314 (2017)
Lian, J., Zhang, F., Xie, X., Sun, G.: CCCFNet: a content-boosted collaborative filtering neural network for cross domain recommender systems. In: WWW (2017)
Liu, Y.F., Hsu, C.Y., Wu, S.H.: Non-linear cross-domain collaborative filtering via hyper-structure transfer. In: ICML, pp. 1190–1198 (2015)
Man, T., Shen, H., Jin, X., Cheng, X.: Cross-domain recommendation: an embedding and mapping approach. In: IJCAI, pp. 2464–2470. AAAI Press (2017)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: NeurIPS, pp. 1257–1264 (2008)
Rendle, S.: Factorization machines. In: ICDM, pp. 995–1000. IEEE (2010)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI (2009)
Sahebi, S., Brusilovsky, P.: It takes two to tango: an exploration of domain pairs for cross-domain collaborative filtering. In: RecSys, pp. 131–138. ACM (2015)
Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: SIGKD, pp. 650–658. ACM (2008)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in Artificial Intelligence (2009)
Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: SIGKDD, pp. 1235–1244. ACM (2015)
Wu, C.Y., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. In: WSDM, pp. 495–503. ACM (2017)
Wu, Y., DuBois, C., Zheng, A.X., Ester, M.: Collaborative denoising auto-encoders for top-n recommender systems. In: WSDM, pp. 153–162. ACM (2016)
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: SIGKDD, pp. 353–362. ACM (2016)
Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. CSUR 52(1), 5 (2019)
Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: WSDM, pp. 425–434. ACM (2017)
Zhong, E., Fan, W., Yang, Q.: User behavior learning and transfer in composite social networks. TKDD 8(1), 6 (2014)
Zhu, F., Chen, C., Wang, Y., Liu, G., Zheng, X.: DTCDR: a framework for dual-target cross-domain recommendation. In: CIKM, pp. 1533–1542 (2019)
Zhu, F., Wang, Y., Chen, C., Liu, G., Orgun, M.A., Wu, J.: A deep framework for cross-domain and cross-system recommendations. In: IJCAI, pp. 3711–3717 (2018)
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Vijaikumar, M., Shevade, S., Murty, M.N. (2021). Neural Cross-Domain Collaborative Filtering with Shared Entities. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12457. Springer, Cham. https://doi.org/10.1007/978-3-030-67658-2_42
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