Work

Neural Collaborative Reasoning

Year: 2021

Type: conference-paper

Abstract: Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i.e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so that a user embedding can be matched with relevant item embeddings using designed or learned similarity functions. However, as a cognition rather than a perception intelligent task, recommendation requires not only the ability of pattern recognition and matching from data, but also the ability of cognitive reasoning in data.

DOI: 10.1145/3442381.3449973

Authors: Hanxiong Chen, Shaoyun Shi, Yunqi Li, Yongfeng Zhang

Institutions: Rutgers, The State University of New Jersey, Tsinghua University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W3024683329

FWCI: 14.3

Cites: 80

Cited by: 88

Related to: 10