推荐系统2020炼丹指南
- PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media.
- Where to Go Next: Modeling Long-and Short‐Term User Preferences for Point-of‐Interest Recommendation.
- A Knowledge-Aware Attentional Reasoning Network for Recommendation.
- Enhancing Personalized Trip Recommendation with Attractive Routes.
- Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation.
- An Attentional Recurrent Neural Network for Personalized Next Location Recommendation.
- Memory Augmented Graph Neural Networks for Sequential Recommendation.
- Leveraging Title-Abstract Attentive Semantics for Paper Recommendation.
- Diversified Interactive Recommendation with Implicit Feedback.
- Question-driven Purchasing Propensity Analysis for Recommendation.
- Sequential Recommendation with Relation-Aware Kernelized Self-Attention.
- Incremental Fairness in Two‐Sided Market Platforms: On Smoothly Updating Recommendations.
- Attention‐guide Walk Model in Heterogeneous Information Network for Multi-style Recommendation.
- Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data.
- Symmetric Metric Learning with Adaptive Margin for Recommendation.
- Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation.
- Towards Comprehensive Recommender Systems: Time-Aware Unified Recommendations Based on Listwise Ranking of Implicit Cross-Network Data.
- Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback.
- Towards Hands‐free Visual Dialog Interactive Recommendation.
- Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests.
- Stochastically Robust Personalized Ranking for LSH Recommendation Retrieval.
- Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach.
- Multi-Component Graph Convolutional Collaborative Filtering.
- Deep Match to Rank Model for Personalized Click-Through Rate Prediction.
- Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution.
- Improved Algorithms for Conservative Exploration in Bandits.
- Linear Bandits with Feature Feedback.
算法小屋 文章被收录于专栏
不定期分享各类算法以及面经。同时也正在学习相关分布式技术。欢迎一起交流。
投递卓越教育等公司10个岗位 >
字节跳动公司氛围 548人发布