User Preference Translation Model for Recommendation System with Item Influence Diffusion Embedding

Hao Shang Ma, Jen Wei Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Recommendation systems which are designed to understand and predict user interest based on user preferences play an important role in the era of information explosion. We propose the item influence embedding which adopts the social influence diffusion concept to model the item relations. We can learn the activation paths in items-item relation graph. In addition, for generating top-k items, most of recommendation systems calculate the similarity between user embedding and embedding of all items. The calculation costs too much time when number of users and items are huge. Therefore, we propose the User Preference Translation Model (UPTM) to recommend the Top-k items based on the language translation technology. UPTM directly generates the recommendation items based on translating the user preference. We can avoid to calculate the similarity of user embedding and item embedding. From the experimental results, UPTM not only outperforms the compared methods but also save the time in real large datasets.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
EditorsMartin Atzmuller, Michele Coscia, Rokia Missaoui
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages50-54
Number of pages5
ISBN (Electronic)9781728110561
DOIs
Publication statusPublished - 2020 Dec 7
Event12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands
Duration: 2020 Dec 72020 Dec 10

Publication series

NameProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Conference

Conference12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
CountryNetherlands
CityVirtual, Online
Period20-12-0720-12-10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management
  • Social Psychology
  • Communication

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