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推荐算法
Birdy edited this page Aug 9, 2019
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14 revisions
- 1.推荐引擎解决什么问题
- 2.推荐的分类
- 3.推荐的过程
- 4.推荐的算法
- 5.推荐的评估
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).
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)
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)
基于协同和基于内容
排序预测与评分预测
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 |
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 |
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 |