沒有看明白的論文---Recommendation with Temporal Dynamics Based on Sequence Similarity Search
貢獻： 解決推薦時間問題+人偏好的變化 聚類+相似性度量+推薦算法 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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相關文章
- 1. 論文筆記Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
- 2. 論文筆記：Collaborative Filtering with Temporal Dynamics
- 3. Spatio-Temporal Fusion based Convolutional Sequence Learning for Lip Reading論文閱讀
- 4. 【論文閱讀】PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks
- 5. Efficient Multi-Attribute Similarity Learning Towards Attribute-based Fashion Search
- 6. 論文筆記：Connectionist Temporal Classification: Labelling Unsegmented Sequence
- 7. VSR論文筆記二|Robust Video Super-Resolution with Learned Temporal Dynamics
- 8. 論文學習6-（M2DNE）Temporal Network Embedding with Micro- and Macro-dynamics
- 9. 論文解讀：Feature trees: A new molecular similarity measure based on tree matching
- 10. 論文閱讀：《Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neu》