DATASET for Evaluating Recommender System (Collaborative Filtering)
Neural Model for Recommendation
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua. Neural Collaborative Filtering. https://arxiv.org/abs/1708.05031
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua. Neural Graph Collaborative Filtering. https://arxiv.org/abs/1905.08108
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, Bin Cui. Graph Neural Networks in Recommender Systems: A Survey, ACM Computing SurveysVolume 55Issue 5Article No.: 97pp 1–37https://doi.org/10.1145/3535101
Xiangnan He , Kuan Deng , Xiang Wang , Yan Li , Yongdong Zhang , Meng Wang. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation LINK
Xuheng Cai, Chao Huang, Lianghao Xia, Xubin Ren. LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation, ICLR 2023. LINK
Zhu Sun, Hongyang Liu, Xinghua Qu, Kaidong Feng, Yan Wang, Yew-Soon Ong. Large Language Models for Intent-Driven Session Recommendations , SIGIR 2024
Online RS:
Daniel Lemire and Anna Maclachlan, Slope One Predictors for Online Rating-Based Collaborative Filtering, In SIAM Data Mining (SDM’05), Newport Beach, California, April 21-23, 2005. (2005) Link
FIDEL CACHEDA, VICTOR CARNEIRO, DIEGO FERNANDEZ and VREIXO FORMOSO, Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposals for Scalable, High-Performance Recommender Systems, ACM Transactions on the Web, Vol. 5, No. 1, Article 2, Publication date: February 2011. (pdf) (2011)
Subbian, K., Aggarwal, C., Hegde, K.: Recommendations for streaming data. In: Proceedings of the International on Conference on Information and Knowledge Management, pp. 2185–2190. ACM (2016)
Huang, Y., Cui, B., Zhang, W., Jiang, J., Xu, Y.: Tencentrec: real-time stream recommendation in practice. In: Proceedings of the SIGMOD International Conference on Management of Data, pp. 227–238. ACM (2015)
Rama Syamala Sreepada and Bidyut Kr. Patra. An Incremental Approach for Collaborative Filtering in Streaming Scenarios, The 40th European Conference in Information Retrieval (ECIR 2018), pp 632-637. (2018)
Workshop on Online Recommender Systems and User Modeling
Diversity Issue:
Gediminas Adomavicius and YoungOk Kwon, Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 5, MAY 2012.
Session Based Recommendation (ECIR Tutorial)
B. Hidasi, A.Karatzoglou, L. Baltrunas and D. Tikk. Session-Based Recommendation with Recurrent Neural Networks, ICLR 2016.
Yong Tan, X.Xu and Y.Liu. Improved Recurrent Neural Networks for Session-based Recommendations, DLRS, 2016.
B.Twardowski. Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks, RecSys 2016.
B. Hidasi, M. Quadrana, A. Karatzoglou and D. Tikk. Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations, RecSys 2016.
M. Quadrana, A. Karatzoglou, B. Hidasi and P. Cremonesi. Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks, RecSys 2017.
Dietmar Jannach. and M. Ludewig. When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation, RecSys 2017.
C.Wu, M.Yan and L.Si. Session-Aware Information Embedding for E-commerce Product Recommendation, CIKM 2017.
J.Li, P Ren, Z.Cheb, Z, Ren, T. Lian and J.Ma. Neural Attentive Session-based Recommendation, CIKM 2017.
P.Loyola, C. Liu and Y. Hirate. Modeling User Session and Intent with Attention-based Encoder-Decoder Architecture, RecSys 2017.
Liu,Zeng,Mokhosi and Zhang. STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation, KDD 2018.
M. Zhou, Zhuoye Ding, Jiliang Tang and Dawei Yin. Micro Behaviors: ANewPerspective in E-Commerce Recommender System, WSDM 2018.
Recommender System in Educational Technology
N.Manouselis, H. Drachsler, R. Vuorikari, H. Hummel and R. Koper. Recommender systems in technology enhanced learning. Recommender systems handbook, 387–415, 2011.
M. K. Khribi, M. Jemni and O. Nasraoui Automatic Recommendations for ELearning Personalization Based on Web Usage Mining Techniques and Information Retrieval. Education Technology and Society, 12(4):30–42, 2009.
G. Balakrishnan. Predicting student retention in massive open online courses using hidden markov models. Master’s thesis, EECS Department, University of California, Berkeley, May 2013.
C. Piech, J. Huang, Z. Chen, C. Do, A. Ng, and D. Koller. Tuned models of peer assessment in MOOCs. In Proceedings of The 6th International Conference on Educational Data Mining (EDM 2013), 2013.
D. Szafir and B. Mutlu. Artful: Adaptive review technology for flipped learning. In Proc. CHI 2013.
F. Shi, J. Marini, and E. Audry. "Towards a psycho-cognitive recommender system." Proceedings of the International Workshop on Emotion Representations and Modelling for Companion Technologies. ACM, 2015.
X. Xiao, and J. Wang. "Towards attentive, bi-directional mooc learning on mobile devices." In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 163-170. ACM, 2015.
F. Shi, J. Marini, and E. Audry. "Towards a psycho-cognitive recommender system." Proceedings of the International Workshop on Emotion Representations and Modelling for Companion Technologies. ACM, 2015.
Recommendations in Social Relationship
Yi Tay, Anh Tuan Luu, Siu Cheung Hui . CoupleNet: Paying Attention to Couples with Coupled Attention for Relationship Recommendation, 12th International Conference on Web and Social Media (ICWSM 2018).
Recommendations in Location based Social Networks (Recommendations in LBSN)
Yu Zheng, Lizhu Zhang, Zhengxin Ma, Xing Xie and Wei-Ying Ma. Recommending friends and locations based on individual location history, ACM Transactions on the Web, Vol. 5, No. 1, Article 5, Publication date: February 2011.