Sequential Recommendation
191 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.
Benchmarks
These leaderboards are used to track progress in Sequential Recommendation
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Libraries
Use these libraries to find Sequential Recommendation models and implementations
MovieLens
Yelp
X-Wines
Most implemented papers
Self-Attentive Sequential Recommendation
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
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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
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
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
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
In recent years, the integration of Large Language Models (LLMs) into recommender systems has garnered interest among both practitioners and researchers.
Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation
microsoft/recommenders
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IJCAI 2019
User modeling is an essential task for online rec- ommender systems.
Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation
XiaoZHOUCAM/TEMN
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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
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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
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Sequential patterns play an important role in building modern recommender systems.
