Novel Recommendation-Based Approach for Multidisciplinary Development of Future Universities
Abstract
:1. Introduction
- Rather than directly wading through massive high-dimensional data, we judiciously formulate the cumbersome configurations of disciplines as a discipline recommendation problem.
- An efficient non-negative matrix factorization-based algorithm is developed to solve the resultant recommendation problem with a low (polynomial-time) complexity.
- A promising off-the-shelf subject list can be readily given by the proposed recommendation engine and used as a cost-effective, powerful and practical tool in the decision process of discipline configuration for a future university.
2. Problem Formulation
2.1. Problem Mapping
- In the classical recommendation-based problem of retail business, there are two types of roles: users and items. As shown in the left subfigure of Figure 1, the solid blue line refers to an actual purchase of an item by a user, and the green dashed line refers to a recommended item by the recommendation engine. In such a scenario, the users freely purchase the preferred ones from thousands of items. Typically, it can be inefficient for the user to fetch the mostly matched targets via brute-force search in many high-dimensional candidates. This induces the so-called information overload problem. To solve the problem, the recommendation engine is utilized to transform the cumbersome high-dimensional searching space into the low-dimensional space, and based on neighborhood or historical data similarities and correlations in the low-dimensional space, further recommend a Top-K (K is usually a small number) number of most needed items to users [29,30].As for the configuration of disciplines for universities, there are also two types of roles: universities and disciplines. As shown in the right sub-figure of Figure 1, the solid blue line refers to a preponderant discipline included in published data by a third party for a university, and the green dashed line refers to a recommended discipline by the recommendation engine to be the promising discipline for a university. In such a case, the objective of a university is to determine a series of disciplines as the focus of further developments. Correspondingly, we resort to the recommendation engine to transform the original high-dimensional features of both universities and disciplines into the low-dimensional ones. Based on neighborhood or historical data similarities and correlations in the low-dimensional space, we subsequently recommend an adequate configuration of disciplines to direct the decision process on the development of disciplines.
- In the recommendation case of retail business, the recommendation engine often relies on the explicit feedback of users, e.g., the rating for a specific item given by users. Based on a specific item’s neighborhood or historical usage rating data, the recommendation engine can readily provide the recommendation list of items with a neighborhood-based collaborative filtering approach or model-based approach [31]. As for the problem of discipline configuration for universities, the score on a specific discipline by a third party can be seen as a preferred configuration of the discipline for a university. Based on the scores on disciplines by the third party, the recommendation engine can be readily used to handle the recommendation task as in retail business scenarios and provide an appropriate list of disciplines for universities.
2.2. Discipline Recommendation Problem
Algorithm 1 The proposed iterative-based algorithm to solve problem (3) |
Initialize: Randomly initialize and with each entry in (0, 1), fill the unknown entries of as 0, an accuracy level , select a step size , a regularization parameter , and set . |
Repeat:
|
3. Numerical Results
3.1. Evaluation on a Local Authoritative Data Set
3.2. Further Evaluation of Well-Known International Data Set
4. Discussions and Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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University | Discipline | Scores |
---|---|---|
University 1 | mathematics | 89 |
University 2 | mathematics | 82 |
University 3 | physics | 85 |
University 4 | physics | 75 |
University 2 | chemistry | 82 |
University 3 | chemistry | 81 |
Discipline | Mathematics | Physics | Chemistry |
---|---|---|---|
University | |||
University 1 | 89 | ? | ? |
University 2 | 82 | ? | 82 |
University 3 | ? | 85 | 81 |
University 4 | ? | 75 | ? |
Rank | University | Rating |
---|---|---|
1 | Peking University | 90 |
2 | Renmin University of China | 89 |
3 | Sun Yat-sen University | 84 |
4 | Fudan University | 83 |
5 | Nanjing University | 81 |
6 | Wuhan University | 79 |
7 | Beijing Normal University | 77 |
8 | Nankai University | 76 |
9 | Jilin University | 75 |
Zhejiang University | ||
10 | Tsinghua University | 74 |
11 | East China Normal University Xiamen University | 72 |
Rank | University | Rating |
---|---|---|
1 | Peking University | 95 |
2 | Renmin University of China | 92 |
3 | Fudan University Sun Yat-sen University | 87 |
4 | Nanjing University Wuhan University | 85 |
5 | Beijing Normal University | 83 |
6 | Nankai University Jilin University | 79 |
7 | Tsinghua University Heilongjiang University Zhejiang University | 78 |
8 | Shanxi University East China Normal University | 76 |
The Scale of Universities | The Scale of Disciplines | Sparsity |
---|---|---|
201 | 78 | 86.8% |
University | Disciplines Hit in the Top-5 Recommendation |
---|---|
Northwestern Polytechnical University | business administration biomedical engineering electrical engineering public administration software engineering biology design |
China University of Mining and Technology | mechanics material science and engineering information and communication engineering mechanical engineering physical education physics safety science and engineering Marxist theory |
Dalian Jiaotong University | management science and engineering mechanics computer science and technology control science and engineering business administration mathematics software engineering transportation engineering environmental science and engineering Marxist theory |
Shandong Agricultural University | agricultural engineering horticulture biology agricultural and forestry economic management ecology landscape architecture agricultural resources and environment foreign language and literature |
The Scale of Universities | The Scale of Disciplines | Sparsity |
---|---|---|
78 | 49 | 81.9% |
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Bi, S.; Ni, W.; Jiang, Y.; Wang, X. Novel Recommendation-Based Approach for Multidisciplinary Development of Future Universities. Sustainability 2022, 14, 5881. https://doi.org/10.3390/su14105881
Bi S, Ni W, Jiang Y, Wang X. Novel Recommendation-Based Approach for Multidisciplinary Development of Future Universities. Sustainability. 2022; 14(10):5881. https://doi.org/10.3390/su14105881
Chicago/Turabian StyleBi, Siguo, Wei Ni, Yi Jiang, and Xin Wang. 2022. "Novel Recommendation-Based Approach for Multidisciplinary Development of Future Universities" Sustainability 14, no. 10: 5881. https://doi.org/10.3390/su14105881