计算机科学
偏爱
推荐系统
语义学(计算机科学)
消费(社会学)
期限(时间)
人机交互
序列(生物学)
情报检索
程序设计语言
微观经济学
美学
量子力学
生物
物理
哲学
经济
遗传学
作者
Chenyang Wang,Min Zhang,Weigang Ma,Yiqun Liu,Shaoping Ma
标识
DOI:10.1145/3397271.3401131
摘要
Traditional recommender systems mainly aim to model inherent and long-term user preference, while dynamic user demands are also of great importance. Typically, a historical consumption will have impacts on the user demands for its relational items. For instance, users tend to buy complementary items together (iPhone and Airpods) but not substitutive items (Powerbeats and Airpods), although substitutes of the bought one still cater to his/her preference. To better model the effects of history sequence, previous studies introduce the semantics of item relations to capture user demands for recommendation. However, we argue that the temporal evolution of the effects caused by different relations cannot be neglected. In the example above, user demands for headphones can be promoted after a long period when a new one is needed.
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