Pair-wised-Personal-Ranking

Codes of pair-wised personal ranking algorithms, which are based on BPR(Bayesian Personalized Ranking) .

This project is including several implementation of algorithms, which are experimental codes for our publishes:

1. Guo W, Wu S, Wang L, et al. Personalized ranking with pairwise Factorization Machines[J]. Neurocomputing, 2016, 2214(C):191-200.

2. Guo W, Wu S, Wang L, et al. Multiple Attribute Aware Personalized Ranking[M]// Web Technologies and Applications. Springer International Publishing, 2015:244-255.

3. Guo W, Wu S, Wang L, et al. Adaptive Pairwise Learning for Personalized Ranking with Content and Implicit Feedback[C]// IEEE / Wic / ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2016:369-376.

In neuro, we provide the FM based methods:

1.RankPairFM(Personalized Ranking with Pairwise Factorization Machines),

2.PFM( Pairwise Factorization Machines),

3.trFM(Factorization Machines).

In wi_ait, we provide the BPR and svd based methods:

1. Model_WI_CABpr.py is the method described in Adaptive Pairwise Learning for Personalized Ranking with Content and Implicit Feedback.

2. Model_MapBPR.py is map BPR.

3. Model_BPR.py is BPR(Bayesian Personalized Ranking).

4. trMF.py is trainning code of Non-negtive Matrix Factorization.