Context-aware Ensemble of Multifaceted Factorization Models for Recommendation Prediction in Social Networks
Abstract
This paper describes the solution of Shanda Innovations team to Task 1 of KDD-Cup 2012. A novel approach called Multifaceted Factorization Models is proposed to incorporate a great variety of features in social networks. Social relationships and actions between users are integrated as implicit feedbacks to improve the recommendation accuracy. Keywords, tags, profiles, time and some other features are also utilized for modeling user interests. In addition, user behaviors are modeled from the durations of recommendation records. A context-aware ensemble framework is then applied to combine multiple predictors and produce final recommendation results. The proposed approach obtained 0.43959 (public score) / 0.41874 (private score) on the testing dataset, which achieved the 2nd place in the KDD-Cup competition.
Keywords
Cite
@article{arxiv.2105.00991,
title = {Context-aware Ensemble of Multifaceted Factorization Models for Recommendation Prediction in Social Networks},
author = {Yunwen Chen and Zuotao Liu and Daqi Ji and Yingwei Xin and Wenguang Wang and Lu Yao and Yi Zou},
journal= {arXiv preprint arXiv:2105.00991},
year = {2021}
}
Comments
KDD 2012