Recommendation Systems

536 papers with code • 42 benchmarks • 31 datasets

The recommendation systems task is to produce a list of recommendations for a user.

( Image credit: CuMF_SGD )

Greatest papers with code

DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning

google-research/google-research 7 Jun 2021

State-of-the-art MoE models use a trainable sparse gate to select a subset of the experts for each input example.

Multi-Task Learning Recommendation Systems

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

microsoft/recommenders 6 Feb 2020

We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.

Graph Classification Recommendation Systems

A Simple Convolutional Generative Network for Next Item Recommendation

microsoft/recommenders 15 Aug 2018

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation.

Recommendation Systems

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

microsoft/recommenders 14 Mar 2018

On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly.

Click-Through Rate Prediction Recommendation Systems

DKN: Deep Knowledge-Aware Network for News Recommendation

microsoft/recommenders 25 Jan 2018

To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation.

Click-Through Rate Prediction Common Sense Reasoning +2

Neural Collaborative Filtering

microsoft/recommenders WWW 2017

When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.

Recommendation Systems Speech Recognition

Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue

facebookresearch/ParlAI IJCNLP 2019

These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone's preferences, react to their requests, and recommend more appropriate items.

Recommendation Systems

Field-Embedded Factorization Machines for Click-through rate prediction

shenweichen/DeepCTR 13 Sep 2020

Field-Aware Factorization Machine (FFM) and Field-weighted Factorization Machine (FwFM) are state-of-the-art among the shallow models for CTR prediction.

Click-Through Rate Prediction Recommendation Systems

FLEN: Leveraging Field for Scalable CTR Prediction

shenweichen/DeepCTR 12 Nov 2019

By suitably exploiting field information, the field-wise bi-interaction pooling captures both inter-field and intra-field feature conjunctions with a small number of model parameters and an acceptable time complexity for industrial applications.

Click-Through Rate Prediction Recommendation Systems