Session-aware Item-combination Recommendation with Transformer Network

@article{Lin2021SessionawareIR,
  title={Session-aware Item-combination Recommendation with Transformer Network},
  author={Tzu-Heng Lin and Chen Gao},
  journal={2021 IEEE International Conference on Big Data (Big Data)},
  year={2021},
  pages={5708-5713}
}
  • Tzu-Heng LinChen Gao
  • Published 13 November 2021
  • Computer Science
  • 2021 IEEE International Conference on Big Data (Big Data)
In this paper, we detailedly describe our solution for the IEEE BigData Cup 2021: RL-based RecSys (Track 1: Item Combination Prediction) 1. We first conduct an exploratory data analysis on the dataset and then utilize the findings to design our framework. Specifically, we use a two-headed transformer-based network to predict user feedback and unlocked sessions, along with the proposed session-aware reweighted loss, multitasking with click behavior prediction, and randomness-in-session… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 17 REFERENCES

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

This work proposes a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering, and is much easier to implement and train, exhibiting substantial improvements over Neural Graph Collaborative Filtering (NGCF) under exactly the same experimental setting.

A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation

A systematic review on neural recommender models is conducted, aiming to summarize the field from the perspective of recommendation modeling, and discusses some promising directions in this field, including benchmarking recommender systems, graph reasoning based recommendation models, and explainable and fair recommendations for social good.

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

This paper shows that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions, and combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.

Bundle Recommendation with Graph Convolutional Networks

A graph neural network model named BGCN (short for Bundle Graph Convolutional Network) is proposed for bundle recommendation, which unifies user-item interaction, user-bundle interaction and bundle-item affiliation into a heterogeneous graph.

Deep Interest Network for Click-Through Rate Prediction

A novel model: Deep Interest Network (DIN) is proposed which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad.

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

A novel Compressed Interaction Network (CIN), which aims to generate feature interactions in an explicit fashion and at the vector-wise level and is named eXtreme Deep Factorization Machine (xDeepFM), which is able to learn certain bounded-degree feature interactions explicitly and can learn arbitrary low- and high-order feature interactions implicitly.

A Survey of Collaborative Filtering Techniques

From basic techniques to the state-of-the-art, this paper attempts to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

Neural Collaborative Filtering

This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering.

Deep Learning based Recommender System: A Survey and New Perspectives

A taxonomy of deep learning based recommendation models is provided and a comprehensive summary of the state-of-the-art is provided, along with providing new perspectives pertaining to this new exciting development of the deep learning in recommender system.

Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

A comprehensive review of the literature on graph neural network-based recommender systems, following the taxonomy above, and systematically analyze the challenges in graph construction, embedding propagation/aggregation, model optimization, and computation efficiency.