Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2018 | THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Embedding,Information retrieval,Computer science,Artificial intelligence,Transactional leadership,Machine learning |
DocType | Citations | PageRank |
Conference | 8 | 0.40 |
References | Authors | |
23 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shoujin Wang | 1 | 65 | 13.10 |
| Liang Hu | 2 | 166 | 15.64 |
| Longbing Cao | 3 | 2212 | 185.04 |
| Huang Xiaoshui | 4 | 33 | 5.72 |
| Defu Lian | 5 | 759 | 46.15 |
| Wei Liu | 6 | 468 | 37.36 |