Citation: | YUAN Hanning, CHEN Zhengyu, YANG Jingting, et al., “A Hybrid Aspect Based Latent Factor Model for Recommendation,” Chinese Journal of Electronics, vol. 29, no. 3, pp. 482-490, 2020, doi: 10.1049/cje.2020.01.004 |
V. Subramaniyaswamy, G. Manogaran, R. Logesh, V. Vijayakumar, N. Chilamkurti, D. Malathi and N. Senthilselvan, “An ontology-driven personalized food recommendation in Iot-based healthcare system”, Journal of Supercomputing, Vol.75, No.6, pp.3184-3216,2018.
|
Z. Huang, G. Shan, J. Cheng and J. Sun, “Trec: An efficient recommendation system for hunting passengers with deep neural networks”, Neural Computing and Applications, Vol.31, No.1, pp.209-222, 2018.
|
M. Grbovic, V. Radosavljevic, N. Djuric, N. Bhamidipati, J. Savla, V. Bhagwan and D. Sharp, “E-commerce in your inbox: Product recommendations at scale”, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, pp.1809-1818, 2015.
|
Z.-H. T.-W. Chien, Tzu-Chao and Chen, “Exploring longterm behavior patterns in a book recommendation system for reading”, Journal of Educational Technology & Society, Vol.20, No.2, pp.27-36, 2017.
|
S. Zhang, L. Yao and X. Xu, “Autosvd++: An efficient hybrid collaborative filtering model via contractive auto-encoders”, Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, pp.957-960, 2017.
|
H. Lee, Y. Ahn, H. Lee, S. Ha and S.-g. Lee, “Quote recommendation in dialogue using deep neural network”, Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy, pp.957-960, 2016.
|
F. Zhang, N. J. Yuan, D. Lian, X. Xie and W.-Y. Ma, “Collaborative knowledge base embedding for recommender systems”, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, San Francisco, CA, USA, pp.353-362, 2016.
|
Z. Li, J. Tang and M. Tao, “Deep collaborative embedding for social image understanding”, IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol.41, No.9, pp.2070-2083, 2018.
|
Q. Wang, H. Yin, Z. Hu, D. Lian, H. Wang and Z. Huang, “Neural memory streaming recommender networks with adversarial training”, In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, pp.2467-2475, 2018.
|
A. Beutel, E. H. Chi, Z. Cheng, H. Pham and J. Anderson, “Beyond globally optimal: Focused learning for improved recommendations”, Proceedings of the 26th International Conference on World Wide Web, Perth, Australia, pp.203-212, 2017.
|
Y. Koren, “Factorization meets the neighborhood: A multifaceted collaborative filtering model”, Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, pp.426-434, 2008.
|
S. Moghaddam and M. Ester, “The FLDA model for aspectbased opinion mining: Addressing the cold start problem”, Proceedings of the International Conference on World Wide Web, Rio de Janeiro, Brazil, pp.909-918, 2013.
|
B. Xia, Y. Li, Q. Li and T. Li, “Attention-based recurrent neural network for location recommendation”, 12th International Conference on Intelligent Systems and Knowledge Engineering, Nanjing, China, pp.1-6, 2017.
|
Y. Wu and M. Ester, “Flame: A probabilistic model combining aspect based opinion mining and collaborative filtering”, In Proceedings of 8th ACM International Conference on Web Search and Data Mining, Shanghai, China, pp.199-208, 2015.
|
Z. J. Zha, J. Yu, J. Tang, M. Wang and T. S. Chua, “Product aspect ranking and its applications”, IEEE Transactions on Knowledge & Data Engineering, Vol.26, No.5, pp.1211-1224, 2014.
|
W. Zhang, G. Ding, L. Chen, C. Li and C. Zhang, “Generating virtual ratings from chinese reviews to augment online recommendations”, ACM Transactions on Intelligent Systems & Technology, Vol.4, No.1, pp.1-17, 2013.
|
Q. Diao, M. Qiu, C.Y.Wu, A. J. Smola, J. Jiang and C. Wang, “Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)”, ACM Press the 20th ACM SIGKDD International Conference, New York, USA, pp.193-202, 2014.
|
H. Wang and M. Ester, “A sentiment-aligned topic model for product aspect rating prediction”, Proceedings of the Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, pp.1192-1202, 2014.
|
K. Bauman, B. Liu and A. Tuzhilin, “Aspect based recommendations: Recommending items with the most valuable aspects based on user reviews”, Proceedings of the ACM SIGKDD International Conference, Halifax, NS, Canada, pp.717-725, 2017.
|
X. He, T. Chen, M. Y. Kan and X. Chen, “TriRank: Reviewaware explainable recommendation by modeling aspects”, Proceedings of the ACM International Conference on Information and Knowledge Management, Melbourne, VIC, Australia, pp.1661-1670, 2015.
|
L. Zheng, V. Noroozi and P. S. Yu, “Joint deep modeling of users and items using reviews for recommendation”, Proceedings of the 10th ACM International Conference on Web Search and Data Mining, Cambridge, United Kingdom, pp.425-434, 2017.
|
H.Wang, Y. Lu and C. Zhai, “Latent aspect rating analysis on review text data: A rating regression approach”, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, pp.783-792, 2010.
|
J. Mcauley and J. Leskovec, “Hidden factors and hidden topics: Understanding rating dimensions with review text”, Proceedings of the ACM Conference on Recommender Systems, Hong Kong, China, pp.165-172, 2013.
|
Y. Bao, H. Fang and J. Zhang, “Topicmf: Simultaneously exploiting ratings and reviews for recommendation”, Proceedings of the 28th AAAI Conference on Artificial Intelligence, Québec City, Québec, Canada, pp.2-8, 2014.
|
L. Qiu, S. Gao, W. Cheng and J. Guo, “Aspect-based latent factor model by integrating ratings and reviews for recommender system”, Knowledge-Based Systems, Vol.110, No.1, pp.233-243, 2016.
|
G. Ling, M. R. Lyu and I. King, “Ratings meet reviews, a combined approach to recommend”, Proceedings of the ACM Conference on Recommender Systems, Foster City, Silicon Valley, CA, USA, pp.105-112, 2014.
|
S. Seo, J. Huang, H. Yang and Y. Liu, “Interpretable convolutional neural networks with dual local and global attention for review rating prediction”, Proceedings of the 11th ACM Conference on Recommender Systems, Como, Italy, pp.297-305, 2017.
|
X. Yu, H. Ma, B. J. Hsu and J. Han, “On building entity recommender systems using user click log and freebase knowledge”, Proceedings of the ACM International Conference on Web Search and Data Mining, New York, NY, USA, pp.263-272, 2014.
|
A. Beutel, K. Murray, C. Faloutsos and A. J. Smola, “CoBaFi: collaborative bayesian filtering”, Seoul, Korea, pp.97-108, 2014.
|
Y. J. Park and A. Tuzhilin, “The long tail of recommender systems and how to leverage it”, Proceedings of the ACM Conference on Recommender Systems, Lausanne, Switzerland, pp.11-18, 2008.
|
J. Lee, S. Kim, G. Lebanon and Y. Singer, “Local low-rank matrix approximation”, International Conference on Machine Learning, Atlanta, GA, USA, pp.82-90, 2013.
|
B. Xu, J. Bu, C. Chen and D. Cai, “An exploration of improving collaborative recommender systems via user-item subgroups”, Proceedings of the International Conference on World Wide Web, Lyon, France, pp.21-30, 2012.
|
A. Beutel, A. Ahmed and A. J. Smola, “ACCAMS: Additive co-clustering to approximate matrices succinctly”, Proceedings of the 24th International Conference on World Wide Web, Florence, Italy, pp.119-129, 2015.
|
E. Christakopoulou and G. Karypis, “Local itemitem models for top-n recommendation”, Proceedings of the ACM Conference on Recommender Systems, Boston, MA, USA pp.67-74, 2016.
|