Results 21 to 30 of about 7,528,176 (292)

Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation

Connection Science, 2022
Personalised recommendation is a difficult problem that has received a lot of attention to academia and industry. Because of the sparse user–item interaction, cold-start recommendation has been a particularly difficult problem.
Tianyuan Li, Xin Su, Wei Liu, Wei Liang, Meng-Yen Hsieh, Zhuhui Chen, XuChong Liu, Hong Zhang   +7 more
doaj   +1 more source

HyperRS: Hypernetwork-Based Recommender System for the User Cold-Start Problem

IEEE Access, 2023
Meta-learning has been proven to be effective for the cold-start problem of recommender systems. Many meta-learning recommender systems that are designed for the user cold-start problem are gradient-based.
Yuxun Lu, Kosuke Nakamura, Ryutaro Ichise   +2 more
doaj   +1 more source

Survey on Solving Cold Start Problem in Recommendation Systems [PDF]

Jisuanji kexue yu tansuo
Recommender systems provide important functions in areas such as dealing with data overload, providing personalized consulting services, and assisting clients in investment decisions. However, the cold start problem in recommender systems has always been
MAO Qian, XIE Weicheng, QIAO Yitian, HUANG Xiaolong, DONG Gang
doaj   +1 more source

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

Applied Sciences, 2021
Recommender systems suggest items of interest to users based on their preferences. These preferences are typically generated from user ratings of the items. If there are no ratings for a certain user or item, it is said that there is a cold start problem,
Nor Aniza Abdullah, Rasheed Abubakar Rasheed, Mohd Hairul Nizam Md. Nasir, Md Mujibur Rahman   +3 more
doaj   +1 more source

Using Association Rules to Solve the Cold-Start Problem in Recommender Systems [PDF]

, 2010
Recommender systems are widely used online to help users\ud find other products, items etc that they may be interested in based on what is known about that user in their profile.
Geva, Shlomo, Yue Xu, Shaw, Gavin, Shlomo Geva, Gavin Shaw, Xu, Yue   +5 more
core   +2 more sources

Mobile App Start-Up Prediction Based on Federated Learning and Attributed Heterogeneous Network Embedding

Future Internet, 2021
At present, most mobile App start-up prediction algorithms are only trained and predicted based on single-user data. They cannot integrate the data of all users to mine the correlation between users, and cannot alleviate the cold start problem of new ...
Shaoyong Li, Liang Lv, Xiaoya Li, Zhaoyun Ding   +3 more
doaj   +1 more source

Improved Epsilon-greedy Algorithm for Cold-start Problem of New Users [PDF]

Jisuanji gongcheng, 2018
When solving the cold-start problem of new users,fixed and invariant Epsilon parameters will slow the convergence of traditional Epsilon-greedy algorithm.Therefore,an improved Epsilon-greedy algorithm is proposed.Immune feedback model is used to ...
WANG Suqin,ZHANG Yang,JIANG Hao,ZHU Dengming
doaj   +1 more source

Two-stage system using item features for next-item recommendation

Intelligent Systems with Applications, 2022
The next-item recommendation task aims to predict the item that a user is likely to interact with next, given the previous transactions of the user. In this paper, we explore the use of discrete item features to improve the performance of a next-item ...
Madiraju Srilakshmi, Gourab Chowdhury, Sudeshna Sarkar   +2 more
doaj   +1 more source

A Conscious Cross-Breed Recommendation Approach Confining Cold-Start in Electronic Commerce Systems

IEEE Access, 2023
When a new customer enters the spectrum of the E-Commerce system, the informative records and dataset, such as about the new user, purchasing history and other browsing data become insufficient, resulting in the emergence of one serious issue such as a ...
S. Gopal Krishna Patro, Brojo Kishore Mishra, Sanjaya Kumar Panda, Amrutashree Hota, Raghvendra Kumar, Shiyang Lyu, David Taniar   +6 more
doaj   +1 more source

A Hybrid POI Recommendation System Combining Link Analysis and Collaborative Filtering Based on Various Visiting Behaviors

ISPRS International Journal of Geo-Information, 2023
Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user’s geographic location and contextual factors such as time, personal preference, and location categories.
Sumet Darapisut, Komate Amphawan, Nutthanon Leelathakul, Sunisa Rimcharoen   +3 more
doaj   +1 more source