Finance

Tree (set theory)

Combinatorics

Euclidean space

Pure mathematics

Collaborative filtering

Mathematics

Recommender system

Operating system

Euclidean distance

Euclidean geometry

Machine learning

Artificial intelligence

Economics

Generative model

Structuring

Geometry

Space (punctuation)

Computer science

Theoretical computer science

Generative grammar

Learning informative representations of users and items from the historical interactions is crucial to collaborative filtering (CF). Existing CF approaches usually model interactions solely within the Euclidean space. However, the sophisticated user-item interactions inherently present highly non-Euclidean anatomy with various types of geometric patterns (i.e., tree-likeness and cyclic structures). The Euclidean-based models may be inadequate to fully uncover the intent factors beneath such hybrid-geometry interactions. To remedy this deficiency, in this paper, we study the novel problem of Geometric Disentangled Collaborative Filtering (GDCF), which aims to reveal and disentangle the latent intent factors across multiple geometric spaces. A novel generative GDCF model is proposed to learn geometric disentangled representations by inferring the high-level concepts associated with user intentions and various geometries. Empirically, our proposal is extensively evaluated over five real-world datasets, and the experimental results demonstrate the superiority of GDCF.

Geometric Disentangled Collaborative Filtering