SCIE, EI, Scopus, INSPEC, DBLP, CSCD, etc.
Citation: | Yu-Yao Liu, Bo Yang, Hong-Bin Pei, Jing Huang. Neural Explainable Recommender Model Based on Attributes and Reviews[J]. Journal of Computer Science and Technology, 2020, 35(6): 1446-1460. DOI: 10.1007/s11390-020-0152-8 |
[1] |
Zheng L, Noroozi V, Yu P S. Joint deep modeling of users and items using reviews for recommendation. In Proc. the 10th ACM International Conference on Web Search and Data Mining, February 2017, pp.425-434.
|
[2] |
He X, Liao L, Zhang H, Nie L, Hu X, Chua T S. Neural collaborative filtering. In Proc. the 26th International Conference on World Wide Web, April 2017, pp.173-182.
|
[3] |
Koren Y, Bell R. Advances in collaborative filtering. In Recommender Systems Handbook (2nd edition), Ricci F, Rokach L, Shapira B (eds.), Springer-Verlag, 2015, pp.77-118.
|
[4] |
Chen C, Zhang M, Liu Y, Ma S. Neural attentional rating regression with review-level explanations. In Proc. the 2018 International Conference on World Wide Web, April 2018, pp.1583-1592.
|
[5] |
Chen X, Zhang Y, Qin Z. Dynamic explainable recommendation based on neural attentive models. In Proc. the 33rd AAAI Conf. Artificial Intelligence, July 2019, pp.53-60.
|
[6] |
Zhao Q, Shi Y, Hong L. GB-CENT:Gradient boosted categorical embedding and numerical trees. In Proc. the 26th International Conference on World Wide Web, April 2017, pp.1311-1319.
|
[7] |
He X, Pan J, Jin O et al. Practical lessons from predicting clicks on Ads at Facebook. In Proc. the 8th International Workshop on Data Mining for Online Advertising, August 2014, Article No. 5.
|
[8] |
Wang X, He X, Feng F, Nie L, Chua T S. TEM:Treeenhanced embedding model for explainable recommendation. In Proc. the 2018 International Conference on World Wide Web, April 2018, pp.1543-1552.
|
[9] |
Chen T, Guestrin C. XGBoost:A scalable tree boosting system. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.785-794.
|
[10] |
Friedman J H. Greedy function approximation:A gradient boosting machine. The Annals of Statistics, 2001, 29(5):1189-1232.
|
[11] |
Breiman L. Random forests. Machine Learning, 2001, 45(1):5-32.
|
[12] |
Cho, K, van Merrienboer B, Gülçehre Ç et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078, 2014. https://arxiv.org/abs/1406.1078, March 2020.
|
[13] |
Kim Y. Convolutional neural networks for sentence classification. arXiv:1408.5882, 2014. https://arxiv.org/abs/1408.5882, March 2020.
|
[14] |
Mnih A, Salakhutdinov R R. Probabilistic matrix factorization. In Proc. the 21st Annual Conference on Neural Information Processing Systems, December 2007, pp.1257-1264.
|
[15] |
He X, Chen T, Kan M Y, Chen X. TriRank:Review-aware explainable recommendation by modeling aspects. In Proc. the 24th ACM International Conference on Information and Knowledge Management, October 2015, pp.1661-1670.
|
[16] |
Ling G, Lyu M R, King I. Ratings meet reviews, a combined approach to recommend. In Proc. the 8th ACM Conference on Recommender Systems, October 2014, pp.105-112.
|
[17] |
McAuley J, Leskovec J. Hidden factors and hidden topics:Understanding rating dimensions with review text. In Proc. the 7th ACM Conference on Recommender Systems, October 2013, pp.165-172.
|
[18] |
Tan Y, Zhang M, Liu Y, Ma S. Rating-boosted latent topics:Understanding users and items with ratings and reviews. In Proc. the 25th International Joint Conference on Artificial Intelligence, July 2016, pp.2640-2646.
|
[19] |
Zhang Y, Lai G, Zhang M, Zhang Y, Liu Y, Ma S. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proc. the 37th International ACM SIGIR Conference on Research Development in Information Retrieval, July 2014, pp.83-92.
|
[20] |
Zhang Y, Zhang M, Zhang Y et al. Daily-aware personalized recommendation based on feature-level time series analysis. In Proc. the 24th International Conference on World Wide Web, May 2015, pp.1373-1383.
|
[21] |
Zhang Y. Incorporating phrase-level sentiment analysis on textual reviews for personalized recommendation. In Proc. the 8th ACM International Conference on Web Search and Data Mining, February 2015, pp.435-440.
|
[22] |
Shi C, Kong X, Huang Y, Yu P S, Wu B. HeteSim:A general framework for relevance measure in heterogeneous networks. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(10):2479-2492.
|
[23] |
Shi C, Hu B, Zhao W X, Yu P S. Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering, 2018, 31(2):357-370.
|
[24] |
Han X, Shi C, Wang S, Yu P S, Song L. Aspect-level deep collaborative filtering via heterogeneous information networks. In Proc. the 27th International Joint Conference on Artificial Intelligence, July 2018, pp.3393-3399.
|
[25] |
Wang X, Wang D, Xu C, He X, Cao Y, Chua T S. Explainable reasoning over knowledge graphs for recommendation. In Proc. the 33rd AAAI Conference on Artificial Intelligence, July 2019, pp.5329-5336.
|
[26] |
Gan M X, Sun L, Jiang R. Trinity:Walking on a userobject-tag heterogeneous network for personalised recommendation. Journal of Computer Science and Technology, 2016, 31(3):577-594.
|
[27] |
Guo L, Ma J, Jiang H R, Chen Z M, Xing C M. Social trust aware item recommendation for implicit feedback. Journal of Computer Science and Technology, 2015, 30(5):1039-1053.
|
[28] |
Guo L, Wen Y F, Wang X H. Exploiting pre-trained network embeddings for recommendations in social networks. Journal of Computer Science and Technology, 2018, 33(4):682-696.
|
[29] |
Xin X, Lin C Y, Wei X C, Huang H Y. When factorization meets heterogeneous latent topics:An interpretable cross site recommendation framework. Journal of Computer Science and Technology, 2015, 30(4):917-932.
|
[30] |
Costa F, Ouyang S, Dolog P et al. Automatic generation of natural language explanations. In Proc. the 23rd International Conference on Intelligent User Interfaces Companion, March 2018, Article No. 57.
|
[31] |
Tao Y, Jia Y, Wang N, Wang H. The facT:Taming latent factor models for explainability with factorization trees. In Proc. the 42nd Int. ACM SIGIR Conference on Research and Development in Information Retrieval, July 2019, pp.295-304.
|
[32] |
Gao J Y, Wang X T, Wang Y S, Xie X. Explainable recommendation through attentive multi-view learning. In Proc. the 33rd AAAI Conference on Artificial Intelligence, January 2019, pp.3622-3629.
|
[33] |
Chen Z, Wang X, Xie X, Wu T, Bu G Q, Wang Y N, Chen E H. Co-attentive multi-task learning for explainable recommendation. In Proc. the 28th International Joint Conference on Artificial Intelligence, August 2019, pp.2137-2143.
|