Accurate News Recommendation Coalescing Personal and Global Temporal Preferences

被引：4

作者：

Koo, Bonhun ^{[1
]}

Jeon, Hyunsik ^{[1
]}

Kang, U. ^{[1
]}

机构：

[1] Seoul Natl Univ, Seoul, South Korea

来源：

ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2020, PT I | 2020年 / 12084卷

关键词：

News recommender systems; Personal and global temporal preferences; Attention; Recurrent neural network;

D O I：

10.1007/978-3-030-47426-3_7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Given session-based news watch history of users, how can we precisely recommend news articles? Unlike other items for recommendation, the worth of news articles decays quickly and various news sources publish fresh ones every second. Moreover, people frequently select news articles regardless of their personal preferences to understand popular topics at a specific time. Conventional recommendation methods, designed for other recommendation domains, give low performance because of these peculiarities of news articles. In this paper, we propose PGT (News Recommendation Coalescing Personal and Global Temporal Preferences), an accurate news recommendation method designed with consideration of the above characteristics of news articles. PGT extracts latent features from both personal and global temporal preferences to sufficiently reflect users' behaviors. Furthermore, we propose an attention based architecture to extract adequate coalesced features from both of the preferences. Experimental results show that PGT provides the most accurate news recommendation, giving the state-of-the-art accuracy.

引用

页码：78 / 90

页数：13

共 43 条

[31] Capturing Temporal Dynamics of Users' Preferences from Purchase History Big Data for Recommendation System
Li, Chen
He, Minjia
Qaosar, Mahboob
Ahmed, Saleh
Morimoto, Yasuhiko
[J]. 2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 5372 - 5374
[32] Temporal Interaction Embedding for Link Prediction in Global News Event Graph
Yang, Jing
Yang, Laurence T.
Wang, Hao
Gao, Yuan
[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2024, 11 (04) : 5327 - 5336
[33] PGT: Measuring Mobility Relationship using Personal, Global and Temporal Factors
Wang, Hongjian
Li, Zhenhui
Lee, Wang-Chien
[J]. 2014 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), 2014, : 570 - 579
[34] Bridge the Gap Between Network-Based Inference Method and Global Ranking Method in Personal Recommendation
Liu, Xiwei
[J]. PROCEEDINGS OF THE 2016 IEEE 11TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), 2016, : 1802 - 1806
[35] Exploiting bi-directional global transition patterns and personal preferences for missing POI category identification
Xi, Dongbo
Zhuang, Fuzhen
Liu, Yanchi
Zhu, Hengshu
Zhao, Pengpeng
Tan, Chang
He, Qing
[J]. NEURAL NETWORKS, 2020, 132 : 75 - 83
[36] Global spatio-temporal aware graph neural network for next point-of-interest recommendation
Jingkuan Wang
Bo Yang
Haodong Liu
Dongsheng Li
[J]. Applied Intelligence, 2023, 53 : 16762 - 16775
[37] Global spatio-temporal aware graph neural network for next point-of-interest recommendation
Wang, Jingkuan
Yang, Bo
Liu, Haodong
Li, Dongsheng
[J]. APPLIED INTELLIGENCE, 2023, 53 (13) : 16762 - 16775
[38] TeSP-TMF: A temporal-aware personalized POI recommendation approach based on potential preferences and grey relational analysis
Chen, Jiangmei
Zhang, Wende
[J]. ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS, 2023, 58
[39] PGST: Using Personal and Global Factors in the Spatio-Temporal Domain for Mobility Relationship Measurement
Guan, Tao
Wang, Ke-ren
[J]. 2015 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATIONS (CSA), 2015, : 204 - 207
[40] Decoding Global Preferences: Temporal and Cooperative Dependency Modeling in Multi-Agent Preference-Based Reinforcement Learning
Zhu, Tianchen
Qiu, Yue
Zhou, Haoyi
Li, Jianxin
[J]. THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 15, 2024, : 17202 - 17210

← 1 2 3 4 5 →