AsRep

Property	Data
Created	2023-02-21
Updated	2023-02-21
Author	@Aiden
Tags	#study

AsRep, Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer

Title	Venue	Year	Code
AsRep, Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer	SIGIR	'21	code

Abstract

• Sequential Recommendation characterizes the evolving patterns by modeling item sequences chronologically.
• The essential target of it is to capture the item transition correlations.
• The recent developments of transformer inspire the community to design effective sequence encoders
  • e.g., SASRec and BERT4Rec.
• However, we observe that these transformer-based models suffer from the cold-start issue
  • i.e., performing poorly for short sequences.
• Therefore, we propose to augment short sequences while still preserving original sequential correlations.
  • a new framework for Augmenting Sequential Recommendation with Pseudo-prior items (ASReP).
    1. We firstly pre-train a transformer with sequences in a reverse direction to predict prior items.
    2. Then, we use this transformer to generate fabricated(捏造的) historical items at the beginning of short sequences.
    3. Finally, we fine-tune the transformer using these augmented sequences from the time order to predict the next item.
• Experiments on two real-world datasets verify the effectiveness of ASReP.

Model Structure

Introduction

...wip