My personal highlights among the full paper presentations:
- Jiafeng Guo et al. – A Deep Relevance Matching Model for Ad-hoc Retrieval. To overcome the limitations of exact-match retrieval models, the authors propose a deep-learning architecture for ad-hoc retrieval.
- Leonid Boytsov et al. – Off the Beaten Path: Let’s Replace Term-Based Retrieval with k-NN Search. The authors discuss efficient k nearest neighbor search as an alternative to exact-match retrieval models for short documents.
- Min Xie et al. – Learning Graph-based POI Embedding for Location-based
Recommendation. The authors present a generic embedding technique for the task of location recommendation based on spatio-temporal properties of the represented locations.
- Liu Yang et al. – aNMM: Ranking Short Answer Texts with Attention-Based
Neural Matching Model. The paper describes a question answering pipeline based on recurrent neural networks with attention mechanisms.
- Bortik Bandyopadhyay et al. – Topological Graph Sketching for Incremental and Scalable Analytics. As a scalable alternative to exact representations of large graphs, the authors propose sketches based on the min-wise hashing of local graph neighborhoods
As well as some promising short papers:
- Ziwei Zheng et al. –
Graph-Based Multi-Modality Learning for Clinical Decision Support
- Sanguthevar Rajasekaran et al. –
Efficient Algorithms for the Two Locus Problem in Genome-Wide Association Study: Algorithms for the Two Locus Problem