CMoAN

Source code for paper "A Selection-pattern-aware Recommendation Model with Colored-Motif Attention Network"

Introduction

Heterogeneous information networks (HIN) based models are now widely used to fuse auxiliary information for personalized recommendation. However, existing works mainly focus on capturing high-order connections between heterogeneous nodes through predefined pattern (such as meta-path, etc.), which depends on relevant domain knowledge and ignores the statistical characteristics of the substructures in HIN. How to capture meaningful personalized behavior patterns, especially the selection patterns (i.e. how a user select an item), and incorporate them into the preference model is still a challenge in the research of HIN-based recommendation. Therefore, in this paper, a specifically-designed Colored-Motif Attention Network (CMoAN) is proposed to deal with this problem. In the proposed CMoAN, colored motif are used as the elemental building blocks to represent context-based selection patterns, and then by constructing a motif-based adjacency matrix to capture higher-order semantic association between nodes in HIN. Besides, an attentive graph neural network is designed to efficiently model the semantics and high-order relations information. Extensive experiments on three real-world datasets demonstrate that CMoAN consistently outperforms state-of-the-art methods. Furthermore, experimental results also verify the effectiveness of using colored motif for capturing users’selection patterns.

Environment Requirement

The code has been tested running under Python 3.6.5. The required packages are as follows:

• tensorflow == 1.11.0
• numpy == 1.14.3
• scipy == 1.1.0
• sklearn == 0.19.1
• cython == 0.29.15

C++ evaluator

We have implemented C++ code to output metrics during and after training, which is much more efficient than python evaluator. It needs to be compiled first using the following command.

python setup.py build_ext --inplace

After compilation, the C++ code will run by default instead of Python code.

DataSet

movie_0.8_alldata_5

• train.txt

  • Train file.
  • Each line is a user with her/his positive interactions with movies: userID\t a list of movieID\n.

• test.txt

  • Test file (positive instances).
  • Each line is a user with her/his positive interactions with movis: userID\t a list of movieID\n.
  • Note that here we treat all unobserved interactions as the negative instances when reporting performance.

• node_info.txt

  • node type file.
  • Each line is a nodeID with node Type. 0-user 1-movie 2-actor 3-director 4-type

• original_graph_weight.txt

  • Describe the connection relationship between users and each movie in the training set.

• uma_motif_graph_weight.txt

• Describe the connection relationship between users, movies, and actors in the semantic graph.

• umd_motif_graph_weight.txt

• Describe the connection relationship between users, movies, and director in the semantic graph.

• um1t_motif_graph_weight.txt

• Describe the connection relationship between users, movies, and type in the semantic graph.

Examples

• Command

python CMoAN_movie.py --dataset movie_0.8_alldata_5 --regs [1e-3] --embed_size 64 --layer_size [64,64] --lr 0.001 --batch_size 1024 --epoch 500

Other parameters can be adjusted as needed, refer to utility/parser.py