The following papers have been accepted at SIGIR eCom 2018:

Workshop Papers
  • 1. Did We Get It Right? Predicting Query Performance in E-commerce Search  [PDF]
    • Rohan Kumar, Mohit Kumar, Neil Shah and Christos Faloutsos
  • 2. Towards a simplified ontology for better e-commerce search  [PDF]
    • Aliasgar Kutiyanawala, Prateek Verma and Zheng Yan
  • 3. Carl: Sports Award Recommender  [PDF]
    • Martin Pichl, Bernward Pichl and Eva Zangerle
  • 4. Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks  [PDF]
    • Humphrey Sheil, Omer Rana and Ronan Reilly
  • 5. High Accuracy Recall Task  [PDF]
    • Andrew Trotman, Surya Kallumadi and Jon Degenhardt
  • 6. Dynamic Query Substitution in fast evolving fashion  [PDF]
    • Kritika Jain, Nilaksh Bajpai, Ankul Batra and Naveen Pajjuri Reddy
  • 7. Towards Practical Visual Search Engine within Elasticsearch  [PDF]
    • Cun Mu, Jun Zhao, Guang Yang, Jing Zhang and Zheng Yan
  • 8. Address Clustering for e-Commerce Applications  [PDF]
    • Vishal Kakkar and Ravindra Babu T
  • 9. Speeding up the Metabolism in E-commerce by Reinforcement Mechanism Design  [PDF]
    • Hua-Lin He, Chun-Xiang Pan, Qing Da and An-Xiang Zeng
  • 10. End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings  [PDF]
    • Eliot Brenner, Jun Zhao, Aliasgar Kutiyanawala and Zheng Yan
  • 11. A Multimodal Recommender System for Large-scale Assortment Generation in E-commerce  [PDF]
    • Murium Iqbal, Adair Kovac and Kamelia Aryafar
  • 12. Visualizing and Understanding Deep Neural Networks in CTR Prediction  [PDF]
    • Lin Guo, Hui Ye, Wenbo Su, Hehuan Liu, Kai Sun and Hang Xiang
  • 13. PReFacTO: Preference Relations Based Factor Model with Topic Awareness and Offset  [PDF]
    • Priyanka Choudhary and Maunendra Sankar Desarkar
  • 14. A Framework to Discover Significant Product Aspects from E-commerce Product Reviews  [PDF]
    • Saratchandra Indrakanti and Gyanit Singh
  • 15. An End-to-end Model of Predicting Diverse Ranking On Heterogeneous Feeds  [PDF]
    • Zizhe Gao, Zheng Gao, Heng Huang, Zhuoren Jiang and Yuliang Yan
  • 16. Towards Optimization of E-Commerce Search and Discovery  [PDF]
    • Anjan Goswami, Chengxiang Zhai and Prasant Mohapatra
  • 17. Realtime query completion via deep language models  [PDF]
    • Po-Wei Wang, Huan Zhang, Vijai Mohan, Inderjit S. Dhillon and J. Zico Kolter
  • 18. Leveraging Catalog to Resolve Conflicting Query Attributes in E-commerce Sites  [PDF]
    • Suhas Ranganath
  • 19. Noise-aware Missing Shipment Return Comment Classification in E-Commerce  [PDF]
    • Avijit Saha, Vishal Kakkar and Ravindra Babu
  • 20. Central Intention Identification for Natural Language Search Query in E-Commerce  [PDF]
    • Xusheng Luo, Yu Gong and Xi Chen

Data Challenge Papers
  • Overview of the SIGIR 2018 eCom Rakuten Data Challenge  [PDF]
    • Yiu-Chang Lin, Pradipto Das and Ankur Datta
  • Convolutional Neural Network and Bidirectional LSTM Based Taxonomy Classification Using External Dataset at SIGIR eCom Data Challenge  [PDF]
    • Shogo Suzuki, Yohei Iseki, Hiroaki Shiino, Hongwei Zhang, Aya Iwamoto and Fumihiko Takahashi
  • Multi-level Deep Learning based E-commerce Product Categorization  [PDF]
    • Wenhu Yu, Zhiqiang Sun, Haifeng Liu, Zhipeng Li and Zhitong Zheng
  • Encoder-Decoder neural networks for taxonomy classification  [PDF]
    • Makoto Hiramatsu and Kei Wakabayashi
  • Large-Scale Taxonomy Problem: a Mixed Machine Learning Approach  [PDF]
    • Quentin Labernia, Yashio Kabashima, Michimasa Irie, Toshiyuki Oike, Kohei Asano, Jinhee Chun and Takeshi Tokuyama
  • Ecommerce Product Title Classification  [PDF]
    • Sylvain Goumy and Mohamed-Amine Mejri
  • An Empirical Study of Using An Ensemble Model in E-commerce Taxonomy Classification Challenge  [PDF]
    • Yugang Jia, Xin Wang, Hanqing Cao, Boshu Ru and Tianzhong Yang
  • Unconstrained Production Categorization with Sequence-to-Sequence Models  [PDF]
    • Yundi Maggie Li, Liling Tan, Stanley Kok and Ewa Szymanska
  • A Best Match KNN-based Approach for Large-scale Product Categorization  [PDF]
    • Haohao Hu, Runjie Zhu, Yuqi Wang, Wenying Feng, Xing Tan and Jimmy Xiangji Huang
  • Product Categorization with LSTMs and Balanced Pooling Views  [PDF]
    • Michael Skinner
  • Large Scale Taxonomy Classification using BiLSTM with Self-Attention  [PDF]
    • Hang Gao and Tim Oates
  • TopSig at the SIGIR'eCom 2018 Rakuten Data Challenge  [PDF]
    • Timothy Chappell, Shlomo Geva and Lawrence Buckingham
  • Team Waterloo at the SIGIR E-Commerce Data Challenge  [PDF]
    • Angshuman Ghosh, Vineet John and Rahul Iyer