AAAI2020推荐系统论文集锦

前言

最近整理了AAAI2020会议中关于推荐系统的论文，同时通过代码分析了下所接收论文的标题，发现了一些研究的热点以及趋势。

概述

通过对所接收的1590篇论文的标题进行分析，发现以下结论：

• 大部分的论文所用到的技术多为Neural Network(128)相关的；
• 大部分的文献聚焦在以下几个关键技术。比如Embedding(51), Attention(49), Adversarial(74), Reinforcement(49), Convolutional(42), Recurrent(16)等；
• 主要面向的研究任务有分类、回归、识别、追踪等，其中推荐的比重所占也不小。比如Classification(50), Regression(15), Prediction(39), Recognition(52), Tracking(20), Segmentation(28), Translation(32), Recommendation(21)等；
• 所研究的数据不仅关注准确性，关注指标更加多样化。比如Efficient(59), Robust(30), Dynamic(29), Adaptive(29), Hierarchical(26)，；
• 论文研究所用到的数据以图为主，视频、图像、文本比重相当。比如Graph(128), Video(35), Image(59), Heterogeneous(16), Text(35), Social(20)。

*其中括号里的数字表示出现次数。

推荐相关的文章

特此从1590篇论文中筛选出与推荐相关的27篇文章供大家提前阅读，提前领略牛人的最新想法。

• 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.

总结

随着推荐系统的重要性越来越大，研究推荐的人逐渐在增多；随着工业界所产生的用户数据越来越多，工业界研究推荐的优势也越来越大。此次会议上出现了许多推荐的应用，比如音乐推荐、兴趣点推荐、旅游推荐、论文推荐等；同时也有相关的研究放到冷启动、推荐效率等问题上。