Recommendation Systems
1442 papers with code • 54 benchmarks • 54 datasets
The Recommendation Systems task is to produce a list of recommendations for a user. The most common methods used in recommender systems are factor models (Koren et al., 2009; Weimer et al., 2007; Hidasi & Tikk, 2012) and neighborhood methods (Sarwar et al., 2001; Koren, 2008). Factor models work by decomposing the sparse user-item interactions matrix to a set of d dimensional vectors one for each item and user in the dataset. Factor models are hard to apply in session-based recommendations due to the absence of a user profile. On the other hand, neighborhood methods, which rely on computing similarities between items (or users) are based on co-occurrences of items in sessions (or user profiles). Neighborhood methods have been used extensively in session-based recommendations.
( Image credit: CuMF_SGD )
Benchmarks
These leaderboards are used to track progress in Recommendation Systems
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Libraries
Use these libraries to find Recommendation Systems models and implementations
MovieLens
Netflix Prize
Gowalla
MIND
Yelp2018
ReDial
Polyvore
Subtasks
Most implemented papers
Graph Convolutional Matrix Completion
We consider matrix completion for recommender systems from the point of view of link prediction on graphs.
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
DeepGraphLearning/RecommenderSystems
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Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space.
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
We also turn the feed-forward layer in DNN model into a mixture of addictive and multiplicative feature interactions by proposing MaskBlock in this paper.
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
Although some CTR model such as Attentional Factorization Machine (AFM) has been proposed to model the weight of second order interaction features, we posit the evaluation of feature importance before explicit feature interaction procedure is also important for CTR prediction tasks because the model can learn to selectively highlight the informative features and suppress less useful ones if the task has many input features.
Product-based Neural Networks for User Response Prediction
Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
Learning effective feature crosses is the key behind building recommender systems.
Training Deep AutoEncoders for Collaborative Filtering
Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training.
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data.
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance.
Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
Deep learning based methods have been widely used in industrial recommendation systems (RSs).
