Year: 2018
Type: conference-paper
Abstract: Following recent successes in exploiting both latent factor and word embedding models in recommendation, we propose a novel Regularized Multi-Embedding (RME) based recommendation model that simultaneously encapsulates the following ideas via decomposition: (1) which items a user likes, (2) which two users co-like the same items, (3) which two items users often co-liked, and (4) which two items users often co-disliked. In experimental validation, the RME outperforms competing state-of-the-art models in both explicit and implicit feedback datasets, significantly improving [email protected] by 5.9~7.0%, [email protected] by 4.3~5.6%, and [email protected] by 7.9~8.9%. In addition, under the cold-start scenario for users with the lowest number of interactions, against the competing models, the RME outperforms [email protected] by 20.2% and 29.4% in MovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source code are available at: https://github.com/thanhdtran/RME.git.
Authors: Thanh Tran, Kyumin Lee, Yiming Liao, Dongwon Lee
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funders: National Science Foundation, Nvidia
Awards: Penn State's CyberCorps; Scholarship for Service Program, Developing and Evaluating Fraud Informatics Curriculum among Institutions in the Appalachian Region, SBE TWC: Small: Collaborative: Privacy Protection in Social Networks: Bridging the Gap Between User Perception and Privacy Enforcement, EAGER: Training Computers and Humans to Detect Misinformation by Combining Computational and Theoretical Analysis, CAREER: Tracking, Revealing and Detecting Crowdsourced Manipulation
OpenAlex ID: W2890097133