AAAI2020推荐系统论文集锦

Original 张小磊机器学习与推荐算法 2022-04-27

收录于合集 #推荐系统顶会聚焦 13个

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.