Work

Sequential Recommender via Time-aware Attentive Memory Network

Year: 2020

Type: conference-paper

Abstract: Recommendation systems aim to assist users to discover most preferred contents from an ever-growing corpus of items. Although recommenders have been greatly improved by deep learning, they still face several challenges: (1) Behaviors are much more com- plex than words in sentences, so traditional attentive and recurrent models have limitations capturing the temporal dynamics of user preferences. (2) The preferences of users are multiple and evolving, so it is difficult to integrate long-term memory and short-term intent.

Source: ACM International Conference on Information and Knowledge Management (CIKM)

DOI: 10.1145/3340531.3411869

Authors: Wendi Ji, Keqiang Wang, Xiaoling Wang, Tingwei Chen, Alexandra Cristea

Institutions: East China Normal University, Tongji University, Liaoning University, Durham University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G1395209947, G2278760843, G3704501742, G5661849975