Sequential Recommendation

198 papers with code • 8 benchmarks • 8 datasets

Sequential recommendation is a sophisticated approach to providing personalized suggestions by analyzing users' historical interactions in a sequential manner. Unlike traditional recommendation systems, which consider items in isolation, sequential recommendation takes into account the temporal order of user actions. This method is particularly valuable in domains where the sequence of events matters, such as streaming services, e-commerce platforms, and social media.

Libraries

Use these libraries to find Sequential Recommendation models and implementations
3 papers
319
2 papers
280

Most implemented papers

Self-Attentive Sequential Recommendation

microsoft/recommenders 20 Aug 2018

Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the `context' of users' activities on the basis of actions they have performed recently.

BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer

FeiSun/BERT4Rec 14 Apr 2019

To address this problem, we train the bidirectional model using the Cloze task, predicting the masked items in the sequence by jointly conditioning on their left and right context.

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

microsoft/recommenders 19 Sep 2018

Top-$N$ sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-$N$ ranked items that a user will likely interact in a `near future'.

TiSASRec: Time Interval Aware Self-Attention for Sequential Recommendation

PaddlePaddle/PaddleRec 1 Jan 2020

Sequential recommender systems seek to exploit the order of users' interactions, in order to predict their next action based on the context of what they have done recently.

DeepRec: An Open-source Toolkit for Deep Learning based Recommendation

cheungdaven/DeepRec 25 May 2019

In this toolkit, we have implemented a number of deep learning based recommendation algorithms using Python and the widely used deep learning package - Tensorflow.

OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems

agiresearch/openp5 19 Jun 2023

In recent years, the integration of Large Language Models (LLMs) into recommender systems has garnered interest among both practitioners and researchers.

Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation

XiaoZHOUCAM/TEMN 19 May 2019

Point-of-Interest (POI) recommender systems play a vital role in people's lives by recommending unexplored POIs to users and have drawn extensive attention from both academia and industry.

Hierarchical Gating Networks for Sequential Recommendation

allenjack/HGN 21 Jun 2019

However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the short-term user interests given several items the user just accessed.

CosRec: 2D Convolutional Neural Networks for Sequential Recommendation

zzxslp/CosRec 27 Aug 2019

Sequential patterns play an important role in building modern recommender systems.