AdaCML: Adaptive collaborative metric learning for recommendation

Tingting Zhang, Pengpeng Zhao, Yanchi Liu, Jiajie Xu, Junhua Fang, Lei Zhao, Victor S. Sheng, Zhiming Cui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


User preferences are dynamic and diverse in real world, while historical preference of a user may not be equally important as current preference when predicting future interests. As a result, learning the evolving user representation effectively becomes a critical problem in personalized recommendation. However, existing recommendation solutions often use a fixed user representation, which is not capable of modeling the complex interests of users. To this end, we propose a novel metric learning approach named Adaptive Collaborative Metric Learning (AdaCML) for recommendation. AdaCML employs a memory component and an attention mechanism to learn an adaptive user representation, which dynamically adapts to locally activated items. In this way, implicit relationships of user-item pairs can be better determined in the metric space and users’ interests can be modeled more accurately. Comprehensive experimental results demonstrate the effectiveness of AdaCML on two datasets, and show that AdaCML outperforms competitive baselines in terms of Precision, Recall, and Normalized Discounted Cumulative Gain (NDCG).

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings
EditorsJuggapong Natwichai, Joao Gama, Jun Yang, Guoliang Li, Yongxin Tong
Number of pages16
ISBN (Print)9783030185787
StatePublished - 2019
Event24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 - Chiang Mai, Thailand
Duration: Apr 22 2019Apr 25 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11447 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
CityChiang Mai


  • Attention mechanism
  • Metric learning
  • Recommender systems


Dive into the research topics of 'AdaCML: Adaptive collaborative metric learning for recommendation'. Together they form a unique fingerprint.

Cite this