Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
Abstract
:1. Introduction
- We propose a novel course recommendation method based on heterogeneous graph embedding, and our experiments prove that the performance of this method is better than existing methods.
- We propose a novel solution for enhancing the embedding of the meta-path in HG.
- Extensive experiments on four real-world datasets demonstrate the effectiveness of the proposed approach. In addition, we show that the proposed approach can maintain good performance even in the absence of meta-path data.
2. Related Work
2.1. Course Recommendation System
2.2. Heterogeneous Information Network Embedding-Based Recommendation
3. Preliminary
4. Proposed Approach for Course Recommendation
4.1. Meta-Path Embedding Layer
4.1.1. Original Meta-Path Embedding
4.1.2. Simulated Meta-Path Embedding
4.2. Embedding Weight Aggregation Layer
4.2.1. Aggregation of Meta-Path Embedding Based on Attention Mechanism
4.2.2. Fusion of Meta-Path Embedding
Algorithm 1: Algorithm for enhancing meta-path embedding |
Input: the heterogeneous graph G; the adjacency matrix ; the meta-path sets for users and for items. Output: the enhanced meta-path embedding set of users and items: ,.
|
4.3. Matrix Factorization-Based Prediction Layer
Algorithm 2: HGE-CRec training algorithm |
Input: the heterogeneous graph G; the adjacency matrix ; the rating matrix ; the adjustable parameters , ; the regularization parameter ; the learning rate coefficients for integrating embedding features; the enhanced meta-path embedding sets for users and for items. Output: , the users and items feature matrices U and V; the weights of users and items HG embedding; the weights of feature interaction matrix and; the parameters in the fusion function of embedding
|
5. Experiments
5.1. Datasets
- ScholatThis dataset is from a real academic social course platform (scholat.com) which provides courses offered by Chinese universities, including undergraduate and graduate courses. The courses involve computer science, economics, pedagogy, and other disciplines. The student profiles include the school, grade, major, courses learned, etc. The dataset used in our experiment contains 3168 courses, 150,563 users, and 1,237,485 course visit records for the 2020–2021 academic year. The frequency of students’ attendance of courses represents information about student interest in the course. In this experiment, we scaled the attendance frequency to an interval.
- CNPC (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/26147, accessed on 28 September 2022)This dataset consists of the Canvas Network Open Course (canvas.net), which hosts open online courses, including Massive Open Online Courses (MOOCs) that are freely available to participants around the world. The dataset used in our experiments is from January 2014 to September 2015, including 224,914 users and 238 courses as well as various attribute information on users’ social relations, forums, users, and courses. The courses include ten disciplines, e.g., mathematics, statistics, and education.
- Yelp (https://www.yelp.com/dataset/documentation/main, accessed on 30 September 2022)This dataset comes from the largest merchant rating website in the United States, yelp.com. The dataset records user ratings of merchants, the users’ social relationship, and attribute information on users and merchants, including 16,239 users, 14,282 merchants, and 198,397 ratings.
- Movielens (http://files.grouplens.org/datasets/movielens/ml-100k-README.txt, accessed on 30 September 2022)The Movielens dataset is a classic movie recommendation dataset from movielens.org. Movielens-100k was selected for this experiment. This dataset has 943 users, 1682 movies, and 100,000 scores, and contains social relationship and attribute information between users and movies.
5.2. Experimental Setup
5.2.1. Baselines
- PMF [30]: This is a recommended algorithm for classical probability matrix factorization models which decomposes the scoring matrix into two low-dimensional matrices.
- SoMF [31]: In this algorithm, social relations have the characteristics of social regularization items, helping to integrate social relations into basic recommendations in the matrix factorization model.
- HERec [6]: This classical recommendation algorithm based on heterogeneous information network embedding adopts the random walk strategy based on the meta-path to generate the embedding, then integrates the embedded fusion into the matrix factorization model for recommendation.
5.2.2. Evaluation Metrics
5.3. Results of the Comparative Experiment
5.4. Ablation Study
5.4.1. Component Adjustment
- HGE-CRecOnly the first part of the HGE-CRec model’s meta-path embedding is improved, that is, while GCN is used to generate analog meta-path embedding, GAT is not used to aggregate various kinds of meta-path embedding based on neighbor weight.
- HGE-CRecOnly the second part of the HGE-CRec model’s meta-path embedding is improved, that is, while GAT is used to aggregate the original meta-path embedding based on the neighbor weights, GCN is not used to generate simulated meta-path embeddings.
5.4.2. Meta-Path Embedding Adjustment
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Notation | Description |
---|---|
The heterogeneous graph | |
A | The set of the types of node |
P | A meta-path |
The set of the meta-path | |
The type of node t | |
The number of nodes of type in the neighbor of node v. | |
An adjacency matrix of HG. | |
A node embedding on the l meta-path. | |
The finally embedding of user u and item i. | |
The rating predicted by user u on item i. | |
The potential factors for user u and item i |
Datasets | Relations (A-B) | Number (A) | Number (B) | Number (A-B) |
---|---|---|---|---|
Student-Course | 25,293 | 1670 | 53,988 | |
Student-Unit_of_study | 150,563 | 5753 | 150,563 | |
Scholat | Student-Research_field | 150,563 | 6458 | 150,563 |
Course-School | 3168 | 344 | 3168 | |
Course-Type | 3168 | 13 | 3168 | |
Course-Teacher | 3168 | 1060 | 7846 | |
User-Course | 224,914 | 238 | 325,199 | |
User-Learner_type | 32,719 | 7 | 32,719 | |
CNPC | User-Age | 224,914 | 4 | 224,914 |
Course-Discipline | 238 | 10 | 238 | |
Course-Course_length | 238 | 79 | 238 | |
User-Business | 16,239 | 14,284 | 198,397 | |
User-User | 10,580 | 10,580 | 158,590 | |
Yelp | User-Compliment | 14,411 | 11 | 76,875 |
Business-City | 14,267 | 47 | 14,267 | |
Business-Category | 14,180 | 511 | 40,009 | |
User-Movie | 943 | 1682 | 100,000 | |
User-User | 943 | 943 | 47,150 | |
Movielens | User-Occupation | 943 | 21 | 943 |
User-Age | 943 | 8 | 943 | |
Movie-Movie | 1682 | 1682 | 82,798 | |
Movie-Genre | 1682 | 18 | 2891 |
Scholat | CNPC | Yelp | Movielens |
---|---|---|---|
S-C-S, C-S-C, S-C-Te-C-S, C-Te-C, C-Ty-C, S-C-Ty-C-S, S-C-Sc-C-S, C-Sc-C | U-C-U, C-U-C, U-C-D-C-U, C-D-C, U-C-Co-C-U, C-Co-C | U-B-U, B-U-B, U-B-Ci-B-U, B-Ci-B, U-B-Ca-B-U, B-Ca-B | U-M-U, M-U-M, U-M-G-M-U, M-G-M, M-M, U-M-M-U |
Training Rate | Metrics | PMF | SoMF | HERec | HGE-CRec |
---|---|---|---|---|---|
80% | MAE | 0.4732 | 0.4685 | 0.4529 | |
RMSE | 0.7199 | 0.7087 | 0.6596 | ||
60% | MAE | 0.5023 | 0.4832 | 0.4627 | |
RMSE | 0.7693 | 0.7303 | 0.6908 | ||
40% | MAE | 0.5758 | 0.5090 | 0.4801 | |
RMSE | 0.8988 | 0.7748 | 0.7185 | ||
20% | MAE | 0.8302 | 0.5856 | 0.5677 | |
RMSE | 1.4199 | 0.8048 | 0.7866 |
Training Rate | Metrics | PMF | SoMF | HERec | HGE-CRec |
---|---|---|---|---|---|
80% | MAE | 0.8998 | 0.9074 | 0.8775 | |
RMSE | 1.2254 | 1.2293 | 1.1666 | ||
60% | MAE | 0.9124 | 0.9248 | 0.8843 | |
RMSE | 1.2504 | 1.2563 | 1.1761 | ||
40% | MAE | 0.9335 | 0.9585 | 0.8955 | |
RMSE | 1.2934 | 1.3092 | 1.1928 | ||
20% | MAE | 1.0504 | 1.0236 | 0.9156 | |
RMSE | 1.4053 | 1.4465 | 1.2273 |
Training Rate | Metrics | PMF | SoMF | HERec | HGE-CRec |
---|---|---|---|---|---|
90% | MAE | 1.0412 | 1.0095 | 0.8395 | |
RMSE | 1.4268 | 1.3392 | 1.0907 | ||
80% | MAE | 1.0791 | 1.0373 | 0.8475 | |
RMSE | 1.4816 | 1.3782 | 1.1117 | ||
70% | MAE | 1.1170 | 1.0694 | 0.8580 | |
RMSE | 1.5387 | 1.4201 | 1.1256 | ||
60% | MAE | 1.1778 | 1.1135 | 0.8759 | |
RMSE | 1.6167 | 1.4748 | 1.1488 |
Training Rate | Metrics | PMF | SoMF | HERec | HGE-CRec |
---|---|---|---|---|---|
80% | MAE | 0.7324 | 0.7289 | 0.7103 | |
RMSE | 0.9862 | 0.9851 | 0.9274 | ||
60% | MAE | 0.7463 | 0.7450 | 0.7181 | |
RMSE | 1.0121 | 1.0112 | 0.9369 | ||
40% | MAE | 0.7661 | 0.7784 | 0.7293 | |
RMSE | 1.0542 | 1.0650 | 0.9536 | ||
20% | MAE | 0.8527 | 0.8451 | 0.7495 | |
RMSE | 1.1641 | 1.1423 | 0.9881 |
Dataset | Metrics | HGE-CRec | HGE-CRec | HGE-CRec |
---|---|---|---|---|
Scholat | MAE | 0.4528 | 0.4435 | 0.4435 |
RMSE | 0.6598 | 0.6295 | 0.6294 | |
CNPC | MAE | 0.8775 | 0.8658 | 0.8658 |
RMSE | 1.1667 | 1.1362 | 1.1361 | |
Yelp | MAE | 0.8479 | 0.7807 | 0.7804 |
RMSE | 1.1111 | 0.9880 | 0.9884 | |
Movielens | MAE | 0.7103 | 0.6992 | 0.6992 |
RMSE | 0.9272 | 0.8981 | 0.8980 |
Datasets | Metrics | HGE-CRec | HGE-CRec |
---|---|---|---|
Scholat | MAE | 0.4559 | 0.4533 |
RMSE | 0.6719 | 0.6604 | |
CNPC | MAE | 0.8808 | 0.8795 |
RMSE | 1.1784 | 1.1682 | |
Yelp | MAE | 0.8804 | 0.8529 |
RMSE | 1.1607 | 1.1283 | |
Movielens | MAE | 0.7136 | 0.7112 |
RMSE | 0.9360 | 0.9298 |
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Wu, Z.; Liang, Q.; Zhan, Z. Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph. Appl. Sci. 2023, 13, 2404. https://doi.org/10.3390/app13042404
Wu Z, Liang Q, Zhan Z. Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph. Applied Sciences. 2023; 13(4):2404. https://doi.org/10.3390/app13042404
Chicago/Turabian StyleWu, Zhengyang, Qingyu Liang, and Zehui Zhan. 2023. "Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph" Applied Sciences 13, no. 4: 2404. https://doi.org/10.3390/app13042404
APA StyleWu, Z., Liang, Q., & Zhan, Z. (2023). Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph. Applied Sciences, 13(4), 2404. https://doi.org/10.3390/app13042404