推荐算法 - HongZhaoHua/jstarcraft-rns GitHub Wiki

推荐引擎教程

推荐引擎解决什么问题

旨在解决推荐领域的两个核心任务:排序预测(Ranking)和评分预测(Rating).

Ranking任务与Rating任务之间的区别是什么

根据解决基本问题的不同,将推荐算法与评估指标划分为排序(Ranking)与评分(Rating).

两者之间的根本区别在于目标函数的不同.
通俗点的解释:
Ranking算法基于隐式反馈数据,趋向于拟合用户的排序.(关注度)
Rating算法基于显示反馈数据,趋向于拟合用户的评分.(满意度)

Rating算法能不能用于Ranking问题

关键在于具体场景中,关注度与满意度是否保持一致.
通俗点的解释:
人们关注的东西,并不一定是满意的东西.(例如:个人所得税)

推荐的分类

按照信息的利用分类

基于协同和基于内容

按照解决的问题分类

排序预测与评分预测

按照算法的模型分类

机器学习中的模型划分

推荐的过程

推荐的算法

基准算法

名称 问题 说明/论文
RandomGuess Ranking Rating 随机猜测
MostPopular Ranking 最受欢迎
ConstantGuess Rating 常量猜测
GlobalAverage Rating 全局平均
ItemAverage Rating 物品平均
ItemCluster Rating 物品聚类
UserAverage Rating 用户平均
UserCluster Rating 用户聚类

协同算法

名称 问题 说明/论文
AspectModel Ranking Rating Latent class models for collaborative filtering
BHFree Ranking Rating Balancing Prediction and Recommendation Accuracy: Hierarchical Latent Factors for Preference Data
BUCM Ranking Rating Modeling Item Selection and Relevance for Accurate Recommendations
ItemKNN Ranking Rating 基于物品的协同过滤
UserKNN Ranking Rating 基于用户的协同过滤
AoBPR Ranking Improving pairwise learning for item recommendation from implicit feedback
BPR Ranking BPR: Bayesian Personalized Ranking from Implicit Feedback
CLiMF Ranking CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
EALS Ranking Collaborative filtering for implicit feedback dataset
FISM Ranking FISM: Factored Item Similarity Models for Top-N Recommender Systems
GBPR Ranking GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering
HMMForCF Ranking A Hidden Markov Model Purpose: A class for the model, including parameters
ItemBigram Ranking Topic Modeling: Beyond Bag-of-Words
LambdaFM Ranking LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates
LDA Ranking Latent Dirichlet Allocation for implicit feedback
ListwiseMF Ranking List-wise learning to rank with matrix factorization for collaborative filtering
PLSA Ranking Latent semantic models for collaborative filtering
RankALS Ranking Alternating Least Squares for Personalized Ranking
RankSGD Ranking Collaborative Filtering Ensemble for Ranking
SLIM Ranking SLIM: Sparse Linear Methods for Top-N Recommender Systems
WBPR Ranking Bayesian Personalized Ranking for Non-Uniformly Sampled Items
WRMF Ranking Collaborative filtering for implicit feedback datasets
Rank-GeoFM Ranking Rank-GeoFM: A ranking based geographical factorization method for point of interest recommendation
SBPR Ranking Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering
AssociationRule Ranking A Recommendation Algorithm Using Multi-Level Association Rules
PRankD Ranking Personalised ranking with diversity
AsymmetricSVD++ Rating Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model
AutoRec Rating AutoRec: Autoencoders Meet Collaborative Filtering
BPMF Rating Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo
CCD Rating Large-Scale Parallel Collaborative Filtering for the Netflix Prize
FFM Rating Field Aware Factorization Machines for CTR Prediction
GPLSA Rating Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis
IRRG Rating Exploiting Implicit Item Relationships for Recommender Systems
MFALS Rating Large-Scale Parallel Collaborative Filtering for the Netflix Prize
NMF Rating Algorithms for Non-negative Matrix Factorization
PMF Rating PMF: Probabilistic Matrix Factorization
RBM Rating Restricted Boltzman Machines for Collaborative Filtering
RF-Rec Rating RF-Rec: Fast and Accurate Computation of Recommendations based on Rating Frequencies
SVD++ Rating Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model
URP Rating User Rating Profile: a LDA model for rating prediction
RSTE Rating Learning to Recommend with Social Trust Ensemble
SocialMF Rating A matrix factorization technique with trust propagation for recommendation in social networks
SoRec Rating SoRec: Social recommendation using probabilistic matrix factorization
SoReg Rating Recommender systems with social regularization
TimeSVD++ Rating Collaborative Filtering with Temporal Dynamics
TrustMF Rating Social Collaborative Filtering by Trust
TrustSVD Rating TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings
PersonalityDiagnosis Rating A brief introduction to Personality Diagnosis
SlopeOne Rating Slope One Predictors for Online Rating-Based Collaborative Filtering

内容算法

名称 问题 说明/论文
EFM Ranking Rating Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
TF-IDF Ranking 词频-逆文档频率
HFT Rating Hidden factors and hidden topics: understanding rating dimensions with review text
TopicMF Rating TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation