Summary: 18 papers/algorithms were tested 11 weren't reproducible 7 were repoducible with considerable effort but only 1 clearly outperformed the baseline bu

Summary: 18 papers/algorithms were tested 11 weren't reproducible 7 were repoducible with considerable effort but only 1 clearly outperformed the baseline but still didn't outperform a well-tuned linear ranking method -- Travis Ebesu et al. 2018. Collaborative Memory Network for Recommendation Systems. In Proceedings SIGIR ’18. 515–524. was the only paper out 18 that was both reproducible and outperformed the baseline but did not consistently outperform a well-tuned non-neural linear ranking method Reproducible with considerable effort but outperformed by comparably simple heuristic methods, e.g., based on nearest-neighbor or graph-based techniques. ==================== They are: kDD: [17] Binbin Hu, Chuan Shi, Wayne Xin Zhao, and Philip S Yu. 2018. Leveraging meta- path based context for top-n recommendation with a neural co-attention model. In Proceedings KDD ’18. 1531–1540. [SIGIR] [23] Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In Proceedings KDD ’17. 305–314. [48] Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings KDD ’15. 1235–1244. RecSys: [53] LeiZheng,Chun-TaLu,FeiJiang,JiaweiZhang,andPhilipS.Yu.2018.Spectral Collaborative Filtering. In Proceedings RecSys ’18. 311–319. WWW: [14] Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings WWW ’17. 173–182. [24] Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. In Proceedings WWW ’18. 689–698. Non-reproducible: ==================== KDD: [43] Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018. Multi-Pointer Co-Attention Networks for Recommendation. In Proceedings SIGKDD ’18. 2309–2318. RecSys: [41] Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long-Kai Huang, and Chi Xu. 2018. Recurrent Knowledge Graph Embedding for Effective Recommendation. In Proceedings RecSys ’18. 297–305. [6] Homanga Bharadhwaj, Homin Park, and Brian Y. Lim. 2018. RecGAN: Recurrent Generative Adversarial Networks for Recommendation Systems. In Proceedings RecSys ’18. 372–376. [38] Noveen Sachdeva, Kartik Gupta, and Vikram Pudi. 2018. Attentive Neural Archi- tecture Incorporating Song Features for Music Recommendation. In Proceedings RecSys ’18. 417–421. [44] Trinh Xuan Tuan and Tu Minh Phuong. 2017. 3D Convolutional Networks for Session-based Recommendation with Content Features. In Proceedings RecSys ’17. 138–146. [21] Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. 2016. Convolutional Matrix Factorization for Document Context-Aware Recom- mendation. In Proceedings RecSys ’16. 233–240. [45] Flavian Vasile, Elena Smirnova, and Alexis Conneau. 2016. Meta-Prod2Vec: Prod- uct Embeddings Using Side-Information for Recommendation. In Proceedings RecSys ’16. 225–232. SIGIR: [32] Jarana Manotumruksa, Craig Macdonald, and Iadh Ounis. 2018. A Contextual Attention Recurrent Architecture for Context-Aware Venue Recommendation. In Proceedings SIGIR ’18. 555–564. [7] Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat- Seng Chua. 2017. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In Proceedings SIGIR ’17. 335–344. WWW: [42] Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018. Latent relational metric learn- ing via memory-based attention for collaborative ranking. In Proceedings WWW ’18. 729–739. [11] Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. 2015. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings WWW ’15. 278–288.