Attention-based transactional context embedding for next-item recommendation

Shoujin Wang, Liang Hu, Longbing Cao, Xiaoshui Huang, Defu Lian, Wei Liu

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

174 Citations (Scopus)


To recommend the next item to a user in a transactional context is practical yet challenging in applications such as marketing campaigns. Transactional context refers to the items that are observable in a transaction. Most existing transaction based recommender systems (TBRSs) make recommendations by mainly considering recently occurring items instead of all the ones observed in the current context. Moreover, they often assume a rigid order between items within a transaction, which is not always practical. More importantly, a long transaction often contains many items irreverent to the next choice, which tends to overwhelm the influence of a few truely relevant ones. Therefore, we posit that a good TBRS should not only consider all the observed items in the current transaction but also weight them with different relevance to build an attentive context that outputs the proper next item with a high probability. To this end, we design an effective attention-based transaction embedding model (ATEM) for context embedding to weight each observed item in a transaction without assuming order. The empirical study on real-world transaction datasets proves that ATEM significantly outperforms the state-of-the-art methods in terms of both accuracy and novelty.
Original languageEnglish
Title of host publicationThirty-Second AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAssociation for the Advancement of Artificial Intelligence
Number of pages8
ISBN (Electronic)9781577358008
Publication statusPublished - 2018
Externally publishedYes
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: 2 Feb 20187 Feb 2018


Conference32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Country/TerritoryUnited States
CityNew Orleans


Dive into the research topics of 'Attention-based transactional context embedding for next-item recommendation'. Together they form a unique fingerprint.

Cite this