Geodesy

Marketing

World Wide Web

Benchmark (surveying)

Materials science

Theoretical computer science

Benchmarking

Computer science

Business

Graph

Geography

Composite material

Information retrieval

Bundle

Bundle recommendation aims to recommend the user a bundle of items as a whole. Previous models capture user’s preferences on both items and the association of items. Nevertheless, they usually neglect the diversity of user’s intents on adopting items and fail to disentangle user’s intents in representations. In the real scenario of bundle recommendation, a user’s intent may be naturally distributed in the different bundles of that user (Global view). And a bundle may contain multiple intents of a user (Local view). Each view has its advantages for intent disentangling: 1) In the global view, more items are involved to present each intent, which can demonstrate the user’s preference under each intent more clearly. 2) The local view can reveal the association between items under each intent since the items within the same bundle are highly correlated to each other. To this end, in this paper we propose a novel model named Multi-view Intent Disentangle Graph Networks (MIDGN), which is capable of precisely and comprehensively capturing the diversity of user intent and items’ associations at the finer granularity. Specifically, MIDGN disentangles user’s intents from two different perspectives, respectively: 1) taking the Global view, MIDGN disentangles the user’s intent coupled with inter-bundle items; 2) taking the Local view, MIDGN disentangles the user’s intent coupled with items within each bundle. Meanwhile, we compare user’s intents disentangled from different views by a contrast method to improve the learned intents. Extensive experiments are conducted on two benchmark datasets and MIDGN outperforms the state-of-the-art methods by over 10.7% and 26.8%, respectively.

Multi-View Intent Disentangle Graph Networks for Bundle Recommendation