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Sequential Recommendation Using Graph Neuron Networks
2309 (2024) Trang: 66-79
Tác giả: Mai Thị Cẩm Nhung, Nguyễn Thúy Anh, Nguyễn Thái Nghe
Tạp chí: International Conference on Future Data and Security Engineering (FDSE)
Liên kết: https://link.springer.com/chapter/10.1007/978-981-96-0434-0_5
Tóm tắt
Recommendation systems (RS) are becoming increasingly
popular in e-commerce, spanning areas like books, movies, music, news,
and especially shopping and consumer goods. In recent years, sequential
recommendation systems have gained attention as a new approach to
capturing user preferences, either by analyzing the user’s entire behavior
history over time or by focusing on interactions within a single session.
This paper focuses on studying sequential recommender systems based
on user behavior sequences with a session. Specifically, customer session
data representing product selection through click behavior (number of
times choosing to buy or view a product) can support effective for the e
commerce systems. Session-based recommendation system endeavors to
predict the interests of users on the items they may likely click/buy next
in the sequence of items which they have viewed. This article explores
sequential recommender systems for analyzing user behavior within a ses
sion, with a particular focus on session-based product recommendations
using graph neural networks. Experiments are conducted using RSC15
and Diginetica datasets via two metrics: accuracy measured by precision
at 20 (P@20) and average reciprocal rating indicated by MRR@20. On
the RSC15 dataset, the method achieves an accuracy of 71.09 (P@20)
and an MRR@20 score of 30.69. Meanwhile, on the Diginetica dataset,
the P@20 is 52.41 and a MRR@20 value of 18.35. These results show
that the proposed approach could be used for session-based product rec
ommendation.
 


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