没有看明白的论文---Recommendation with Temporal Dynamics Based on Sequence Similarity Search
贡献： 解决推荐时间问题+人偏好的变化 聚类+相似性度量+推荐算法 In SeqSim, in order to improve the efficiency of similarity search, we design a new temporal clustering algorithm to transform item sequence into cluster sequence. In
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相关文章
- 1. 论文笔记Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
- 2. 论文笔记：Collaborative Filtering with Temporal Dynamics
- 3. Spatio-Temporal Fusion based Convolutional Sequence Learning for Lip Reading论文阅读
- 4. 【论文阅读】PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks
- 5. Efficient Multi-Attribute Similarity Learning Towards Attribute-based Fashion Search
- 6. 论文笔记：Connectionist Temporal Classification: Labelling Unsegmented Sequence
- 7. VSR论文笔记二|Robust Video Super-Resolution with Learned Temporal Dynamics
- 8. 论文学习6-（M2DNE）Temporal Network Embedding with Micro- and Macro-dynamics
- 9. 论文解读：Feature trees: A new molecular similarity measure based on tree matching
- 10. 论文阅读：《Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neu》