This repository provides an implementation and datasets of IFM which is a novel factorization machine model. In addition, some additional experiments not included in the paper are also presented here, including AUC performance test on Avazu dataset, and so on.

The code is a Python implementation of IFM.

The Frappe dataset has been used for context-aware mobile app recommendation, which contains 96,202 records containing 957 users and 4,082 apps.

The MovieLens dataset has been used for personalized tag recommendation, which contains 668,953 tag applications of 17,045 users on 23,743 items with 49,657 distinct tags.