冷启动(汽车)
计算机科学
偏爱
生成语法
推荐系统
代表(政治)
协同过滤
考试(生物学)
情报检索
生成模型
训练集
人工智能
人机交互
机器学习
多媒体
工程类
古生物学
政治
生物
法学
政治学
经济
微观经济学
航空航天工程
作者
Haixia Bai,Min Hou,Le Wu,Yonghui Yang,Kun Zhang,Richang Hong,Meng Wang
标识
DOI:10.1145/3581783.3612238
摘要
Multimedia-based recommendation models learn user and item preference representation by fusing both the user-item collaborative signals and the multimedia content signals. In real scenarios, cold items appear in the test stage without any user interaction record. How to perform cold item recommendation is challenging as the training items and test items have different data distributions. These hybrid preference representations contained auxiliary collaborative signals, so current solutions designed alignment functions to transfer learned hybrid preference representations to cold items. Despite the effectiveness, we argue that they are still limited as these models relied heavily on the manually carefully designed alignment functions, which are easily influenced by the limited item records and noises in the training data.
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