A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?
Abstract
:1. Introduction
- Giving the prediction of the consumer product preference behavior model based on artificial intelligence architecture and paying attention to the details of different consumer product preference behaviors.
- Demonstrating the advantages and characteristics of ML that can be applied to predict sustainable patterns in consumer product preferences.
- Demonstrating ML’s ability to predict consumer product preference characteristics in multiple product attribute dimensions.
- Providing product designers with an efficient and convenient consumer product preference prediction technology. This allows product designers to more easily, quickly, and accurately predict consumer preference products.
- Providing a new and reliable analysis and prediction tool for consumer preference behavior that is not easy to grasp and accurately predict.
2. Theory Literature Review of Prediction Methods
3. Methodology
3.1. ML Predicts Sustainable Pattern in Consumer Product Preferences
3.1.1. Sample Collection and Processing
3.1.2. Analysis and Predicting Work
- Step 2. Mark photos. Ask a consumer who has experience in buying a coffee machine to use a score from 1 to 5 to rate each of the 40 photos (Figure 1) and the 20 photos (Figure 2) in the ML system. Score notation is based on the consumer’s personal preference for the coffee machine in the photo. The 1–5 points represent consumers’ preference scores for coffee machines (1 point: do not like it very much, 2 point: do not like it, 3 point: normal, 4 point: like it, 5 point: like it very much).
- Step 3. ML training: First set of ML parameters. (A) Set network model to deep learning; (B) set image information to image width 300 dpi, image height 300 dpi, image depth 3 dpi; (C) set learning parameters iteration to 100, batch quantity 1, learning rate 1 × 10−1. After the parameters were set, this study began to perform ML training on the 40 photos in Figure 1 in the ML system. When the accuracy rate of ML reaches 82.8%, this research will store the training model obtained by ML in the ML system.
- Step 4. Training model verification. Input the 20 photos (Figure 2). After the pictures are inputted, this study will then input the training model stored in the ML system and use it to judge consumers’ coffee machine preferences for the 20 photos (Figure 2). The results of consumer preferences predicted by the ML system for 20 coffee machines are presented in Table 1.
- Step 5. Then, ML predicts consumers’ coffee machine shape preferences. Using the same steps of Step 2–Step 4, this study invites the same consumer to rate each of the 40 photos (Figure 1) and the 20 photos (Figure 2) with 1–5 points according to his personal styling preference. Then, we make the ML algorithm train, and the output is verified again. Table 1 shows the results of the ML system predicting consumers’ preferences for 20 coffee machine shapes.
- Step 6. ML predicts consumers’ coffee machine color preferences. Using the same steps of Step 2–Step 4, this study invites the same consumer to rate each of the 40 photos (Figure 1) and the 20 photos (Figure 2) with 1–5 points according to his personal color preference. Then, we make the ML algorithm train, and the output is verified again. Table 1 shows the results of the ML system predicting consumers’ preferences for 20 coffee machine colors.
3.2. KJ Method Predicts Sustainable Pattern in Consumer Product Preferences
3.2.1. Sample Collection and Processing
3.2.2. Analysis and Predicting Work
- Step 1. Decide on the topic of prediction. This study first decides on the prediction topics as follows: using the KJ method to predict the overall preference of consumers’ coffee machines, to predict the preferences of consumers’ coffee machines shape, and to predict the preferences of consumers’ coffee machines color.
- Step 2. Watch samples. Product designers who have experience in designing coffee machines are invited to carefully look at the 20 photos of coffee machines in Figure 2. When the product designer watches, the product designer is required to pay special attention to the shape of the coffee machine, the color of the coffee machine, and the overall feeling of the coffee machine in each photo.
- Step 3. Make a preference scoring word card. Product designers are asked to write 1 point, 2 points, 3 points, 4 points, and 5 points on five 5 cm × 2 cm white cards. The scores on these five white cards represent the degree to which the designer predicts consumer product preference. Among them, 1 point represents “don’t like it very much”, 2 points represents “don’t like it”, 3 points represents “normal”, 4 points represents “like it”, and 5 points represents “like it very much”.
- Step 4. Make groups. Ask the product designer to spread out all the 20 coffee machine photos (Figure 2) and 5 preference scoring word cards, and then ask the product designer to predict the degree (score) of consumer’s overall product preference for each coffee machine photo. After the prediction work is completed, the product designer is asked to put the coffee machine photos with the same overall product preference scores in the same group. If there is a single photo of a coffee machine that cannot be classified, it will be regarded as a group as it is, and finally the product designer will be asked to classify it according to the similarity between the groups.
- Step 5. Match preference scoring word card for each group. After all the coffee machine photos are grouped, the product designer can be asked to match the preference scoring word cards with the coffee machine photo group according to the degree of deviation of the predicted consumer coffee machine. When the five preference scoring word cards are completely macheted, the KJ method prediction for the overall coffee machine preference of consumers is completed.
- Step 6. Check after classification. Check the classified groups to see if there are any duplications, errors, or omissions in the classifications, and then make corrections.
- Step 7. Record classification results. Record the number of photos in the classified group and the group’s consumer product preference prediction score (Table 2).
- Step 8. Predicting consumer coffee machine shape preferences. Invite product designers to complete the prediction of consumers’ coffee machine shape preference according to Step 4–Step 6 again and make records (Table 2).
- Step 9. Predicting consumer coffee machine color preferences. Invite product designers to complete the prediction of consumers’ coffee machine color preference according to Step 4–Step 6 again and make records (Table 2).
3.3. AHP Method Predicts Sustainable Pattern in Consumer Product Preferences
3.3.1. Sample Collection and Processing
3.3.2. Analysis and Predicting Work
- Step 1. Confirm the problem. In the research on the effectiveness of the AHP method in predicting consumers’ product preferences, the research topics are as follows: the effectiveness of the AHP method in predicting the overall preferences of consumers for coffee machines, the effectiveness of the AHP method in predicting consumers’ coffee machine shape preferences, and the effectiveness of the AHP method in predicting consumers’ coffee machine color preferences.
- Step 2. List the evaluation factors. In the study, we take the 20 coffee machine photos in Figure 2 as evaluation factors.
- Step 3. Establish the evaluation matrix. In this step, this study establishes three evaluation matrices, as follows: the consumer coffee machine overall preference prediction evaluation matrix, consumer coffee machine shape preference prediction evaluation matrix, and consumer coffee machine color preference prediction evaluation matrix.
- Step 4. Paired comparison evaluation. In this study, the implementation method of the paired comparison evaluation of consumers’ overall preference for coffee machines is as follows: two coffee machine photos are randomly selected in the prediction evaluation matrix of consumers’ overall coffee machine preferences, and without considering the influence of other factors, these two evaluation coffee machine photos are compared for predictive consumer product overall preference. This study uses 1 point, 2 points, 3 points, 4 points, and 5 points (1 point represents “don’t like it very much”, 2 points represents “don’t like it”, 3 points represents “normal”, 4 points represents “like it”, and 5 point represents “like it very much”) to present the result of the paired comparison evaluation. After a set of paired evaluation coffee machine photos completes the comparative evaluation, the subjects will conduct a comparison of another set of random two coffee machine photos to compare the overall predictive consumer product preference again. After completing the comparative evaluation of a pair of evaluation factors, the subjects will conduct another group of random selections of two coffee machines photos to carry out the overall preference prediction and comparison of consumer products again. When all the coffee machine photos in the matrix have completed the paired evaluation, the paired comparison evaluation of the overall consumer coffee machine preference is completed. Next, this study uses the same pairwise comparison evaluation method to let the subjects conduct pairwise comparison evaluation of consumer coffee machine shape preference and consumer coffee machine color preference.
- Step 5. Create a pairwise comparison matrix. The pairwise comparison matrix is built as follows: (A) Put the paired comparison evaluation of the consumer’s overall coffee machine preference in the consumer coffee machine overall preference prediction evaluation matrix (Figure 3); (B) put the paired comparison evaluation of the consumer coffee machine shape preference in the consumer coffee machine shape preference prediction evaluation matrix (Figure 4); (C) put the paired comparison evaluation of the consumer coffee machine color preference in the consumer coffee machine color preference prediction evaluation matrix (Figure 5).
- Step 6. Calculate the pairwise comparison matrix eigenvalues and eigenvectors. This step is mainly to calculate and obtain the eigenvalues and eigenvectors of the pairwise comparison matrix with the paired comparison evaluation of the consumer overall coffee machine preference, the pairwise comparison matrix with the paired comparison evaluation of the consumer coffee machine shape preference, and the pairwise comparison matrix with the paired comparison evaluation of the consumer coffee machine color preference. In this study, Formula (1) is used to obtain the eigenvalues of each pairwise comparison matrix, and Formula (2) is used to obtain the eigenvectors of each pairwise comparison matrix.
- Step 7. Calculate the weights of each level and factor. This study uses the geometric mean method to calculate the preference weight for each coffee machine. The weight of each product attribute of the 20 coffee machines in this study is shown in Table 3.
- Step 8. Calculate C.R. The C. R. refers to the consistency ratio. Its calculation formula is given in Formula (3). The C.I. refers to the consistency index. Its calculation formula is given in Formula (4). The R. I. refers to the random index, and its values are shown in Table 4. According to Formulas (3)–(5), the C.R. value of the coffee machine overall preference pairwise comparison matrix is 0.063 (Table 5), the C.R. value of the consumer coffee machine shape preference pairwise comparison matrix is 0.077 (Table 5), and the C.R. value of the coffee machine color preference pairwise comparison matrix is 0.028 (Table 5).
4. Results
4.1. Differences in Designer’s Prediction Results Caused by Different Product Attributes Used to Evaluate Cousumers’ Product Preferences
4.2. Differences in Designer’s Prediction Results Caused by Different Evaluation Methods Used to Evaluate Consumer Product Preferences
4.3. A High Gap between the Prediction Results of Traditional Prediction Methods and Actual Consumer Preferences
4.4. A High Consistency between the Prediction Results of ML Predictions Methods and Actual Consumer Preferences
4.5. ML Has a Better Ability to Predict Consumer Product Preferences Than Traditional Tools
5. Discussion
5.1. Implications of Differences in Designer’s Prediction Results Caused by Different Product Attributes
5.2. Implications of Differences in Designer’s Prediction Results Caused by Different Traditional Forecasting Methods
5.3. The Meaning of the High Gap between the Prediction Results of Traditional Forecasting Methods and Consumers’ Actual Preferences
6. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Photo No. | No.1 | No.2 | No.3 | No.4 | No.5 | No.6 | No.7 | No.8 | No.9 | No.10 | No.11 | No.12 | No.13 | No.14 | No.15 | No.16 | No.17 | No.18 | No.19 | No.20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Predict consumer overall product preference | 3 | 4 | 5 | 5 | 4 | 3 | 2 | 4 | 2 | 3 | 4 | 2 | 4 | 5 | 3 | 2 | 4 | 3 | 3 | 1 |
Predict consumer product shape preference | 3 | 3 | 5 | 5 | 4 | 4 | 4 | 4 | 2 | 3 | 5 | 1 | 5 | 5 | 2 | 1 | 5 | 4 | 3 | 2 |
Predict consumer product color preference | 2 | 2 | 3 | 2 | 1 | 4 | 1 | 5 | 3 | 2 | 3 | 1 | 4 | 5 | 1 | 3 | 4 | 5 | 3 | 1 |
Consumer Product Preference | 1 Point | 2 Points | 3 Points | 4 Points | 5 Points |
---|---|---|---|---|---|
Predict consumer overall product preference | (16) | (2, 12) | (6, 7, 10, 17, 20) | (1, 4, 8, 11, 14, 15, 19) | (2, 3, 5, 13, 18) |
Predict consumer product shape preference | (16,) | (7, 10, 12, 17, 20) | (3, 6, 19) | (4, 8, 11, 13) | (1, 2, 5, 9, 14, 15, 18) |
Predict consumer product color preference | (7, 12, 16) | (3, 4, 9, 10, 20) | (6, 19) | (8, 11, 13, 14, 17) | (1, 2, 5, 15, 18) |
Photo No. | No 1 | No 2 | No 3 | No 4 | No 5 | No 6 | No 7 | No 8 | No 9 | No 10 | No 11 | No 12 | No 13 | No 14 | No 15 | No 16 | No 17 | No 18 | No 19 | No 20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Predict consumer overall product preference | 0.19 (13) | 0.19 (13) | 0.20 (12) | 0.20 (12) | 0.20 (12) | 0.23 (11) | 0.26 (9) | 0.24 (10) | 0.48 (6) | 0.51 (5) | 0.37 (8) | 0.68 (2) | 0.44 (7) | 0.44 (7) | 0.57 (3) | 0.56 (4) | 0.10 (15) | 0.69 (1) | 0.10 (15) | 0.15 (14) |
Predict consumer product shape preference | 0.26 (16) | 0.26 (16) | 0.32 (13) | 0.51 (9) | 0.25 (17) | 0.19 (9) | 0.37 (11) | 0.27 (15) | 0.64 (5) | 0.55 (6) | 0.31 (14) | 0.87 (3) | 0.38 (10) | 0.33 (12) | 0.54 (7) | 0.92 (2) | 0.86 (4) | 0.53 (8) | 0.53 (8) | 0.93 (1) |
Predict consumer product color preference | 0.43 (12) | 0.41 (13) | 0.51 (6) | 0.48 (9) | 0.40 (14) | 0.45 (11) | 0.60 (3) | 0.46 (10) | 0.49 (8) | 0.56 (5) | 0.56 (5) | 0.48 (9) | 0.59 (4) | 0.46 (10) | 0.66 (2) | 0.71 (1) | 0.50 (6) | 0.51 (7) | 0.40 (14) | 0.35 (15) |
Order | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R.I. | 0 | 0 | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.51 | 1.48 | 1.58 | 1.57 | 1.58 |
The Coffee Machine Overall Preference Pairwise Comparison Matrix | The Consumer Coffee Machine Shape Preference Pairwise Comparison Matrix | The Coffee Machine Color Preference Pairwise Comparison Matrix | |
---|---|---|---|
C.R.value | 0.063 | 0.077 | 0.028 |
Photo No. | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | No. 6 | No. 7 | No. 8 | No. 9 | No. 10 | No. 11 | No. 12 | No. 13 | No. 14 | No. 15 | No. 16 | No. 17 | No. 18 | No. 19 | No. 20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Consumer product preferences | 4 | 5 | 5 | 4 | 5 | 3 | 3 | 4 | 2 | 3 | 4 | 2 | 5 | 4 | 4 | 1 | 3 | 5 | 4 | 3 |
Consumer product preferences | 5 | 5 | 2 | 2 | 5 | 2 | 1 | 4 | 2 | 2 | 4 | 1 | 4 | 4 | 5 | 1 | 4 | 5 | 3 | 2 |
Consumer product preferences | 5 | 5 | 3 | 4 | 5 | 3 | 2 | 4 | 5 | 2 | 4 | 2 | 4 | 5 | 5 | 1 | 2 | 5 | 3 | 2 |
Photo No. | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | No. 6 | No. 7 | No. 8 | No. 9 | No. 10 | No. 11 | No. 12 | No. 13 | No. 14 | No. 15 | No. 16 | No. 17 | No. 18 | No. 19 | No. 20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Consumer product preferences | 13 | 13 | 12 | 12 | 12 | 11 | 9 | 10 | 6 | 5 | 8 | 2 | 7 | 7 | 3 | 4 | 15 | 1 | 15 | 14 |
Consumer product shape preferences | 16 | 16 | 13 | 9 | 17 | 9 | 11 | 15 | 5 | 6 | 14 | 3 | 10 | 12 | 7 | 2 | 4 | 8 | 8 | 1 |
Consumer product color preferences | 12 | 13 | 6 | 9 | 14 | 11 | 3 | 10 | 8 | 5 | 5 | 9 | 4 | 10 | 2 | 1 | 6 | 7 | 14 | 15 |
Photo No. | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | No. 6 | No. 7 | No. 8 | No. 9 | No. 10 | No. 11 | No. 12 | No. 13 | No. 14 | No. 15 | No. 16 | No. 17 | No. 18 | No. 19 | No. 20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Consumer product preferences | 3 | 4 | 5 | 5 | 4 | 3 | 2 | 4 | 2 | 3 | 4 | 2 | 4 | 5 | 3 | 2 | 4 | 3 | 3 | 1 |
Consumer product shape preferences | 3 | 3 | 5 | 5 | 4 | 4 | 4 | 4 | 2 | 3 | 5 | 1 | 5 | 5 | 2 | 1 | 5 | 4 | 3 | 2 |
Consumer product color preferences | 2 | 2 | 3 | 2 | 1 | 4 | 1 | 5 | 3 | 2 | 3 | 1 | 4 | 5 | 1 | 3 | 4 | 5 | 3 | 1 |
Photo No. | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | No. 6 | No. 7 | No. 8 | No. 9 | No. 10 | No. 11 | No. 12 | No. 13 | No. 14 | No. 15 | No. 16 | No. 17 | No. 18 | No. 19 | No. 20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
KJ Prediction | 4 | 5 | 5 | 4 | 5 | 3 | 3 | 4 | 2 | 3 | 4 | 2 | 5 | 4 | 4 | 1 | 3 | 5 | 4 | 3 |
AHP Prediction | 13 | 13 | 12 | 12 | 12 | 11 | 9 | 10 | 6 | 5 | 8 | 2 | 7 | 7 | 3 | 4 | 15 | 1 | 15 | 14 |
ML Prediction | 3 | 4 | 5 | 5 | 4 | 3 | 2 | 4 | 2 | 3 | 4 | 2 | 4 | 5 | 3 | 2 | 4 | 3 | 3 | 1 |
Photo No. | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | No. 6 | No. 7 | No. 8 | No. 9 | No. 10 | No. 11 | No. 12 | No. 13 | No. 14 | No. 15 | No. 16 | No. 17 | No. 18 | No. 19 | No. 20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
KJ Prediction | 5 | 5 | 3 | 4 | 5 | 3 | 2 | 4 | 5 | 2 | 4 | 2 | 4 | 5 | 5 | 1 | 2 | 5 | 3 | 2 |
AHP Prediction | 16 | 16 | 13 | 9 | 17 | 9 | 11 | 15 | 5 | 6 | 14 | 3 | 10 | 12 | 7 | 2 | 4 | 8 | 8 | 1 |
ML Prediction | 3 | 3 | 5 | 5 | 4 | 4 | 4 | 4 | 2 | 3 | 5 | 1 | 5 | 5 | 2 | 1 | 5 | 4 | 3 | 2 |
Photo No. | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | No. 6 | No. 7 | No. 8 | No. 9 | No. 10 | No. 11 | No. 12 | No. 13 | No. 14 | No. 15 | No. 16 | No. 17 | No. 18 | No. 19 | No. 20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
KJ Prediction | 5 | 5 | 2 | 2 | 5 | 2 | 1 | 4 | 2 | 2 | 4 | 1 | 4 | 4 | 5 | 1 | 4 | 5 | 3 | 2 |
AHP Prediction | 12 | 13 | 6 | 9 | 14 | 11 | 3 | 10 | 8 | 5 | 5 | 9 | 4 | 10 | 2 | 1 | 6 | 7 | 14 | 15 |
ML Prediction | 2 | 2 | 3 | 2 | 1 | 4 | 1 | 5 | 3 | 2 | 3 | 1 | 4 | 5 | 1 | 3 | 4 | 5 | 3 | 1 |
Photo No. | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | No. 6 | No. 7 | No. 8 | No. 9 | No. 10 | No. 11 | No. 12 | No. 13 | No. 14 | No. 15 | No. 16 | No. 17 | No. 18 | No. 19 | No. 20 | Accuracy Rate |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Actual Preference | 3 | 3 | 5 | 5 | 4 | 2 | 2 | 5 | 2 | 3 | 4 | 1 | 4 | 5 | 3 | 2 | 4 | 4 | 3 | 1 | |
KJ Prediction | 4 | 5 | 5 (O) | 4 | 5 | 3 | 3 | 4 | 2 (O) | 3 (O) | 4 (O) | 2 | 5 | 4 | 4 | 1 | 3 | 5 | 4 | 3 | 20% |
AHP Predict | 13 | 13 | 12 | 12 | 12 | 11 | 9 | 10 | 6 | 5 | 8 | 2 | 7 | 7 | 3 (O) | 4 | 15 | 1 | 15 | 14 | 5% |
Photo No. | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | No. 6 | No. 7 | No. 8 | No. 9 | No. 10 | No. 11 | No. 12 | No. 13 | No. 14 | No. 15 | No. 16 | No. 17 | No. 18 | No. 19 | No. 20 | Accuracy Rate |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Actual Preference | 3 | 3 | 5 | 5 | 4 | 4 | 4 | 5 | 2 | 3 | 5 | 1 | 5 | 5 | 2 | 1 | 5 | 4 | 3 | 1 | |
KJ Prediction | 5 | 5 | 3 | 4 | 5 | 3 | 2 | 4 | 5 | 2 | 4 | 2 | 4 | 5 (O) | 5 | 1 (O) | 2 | 5 | 3 (O) | 2 | 15% |
AHP Prediction | 16 | 16 | 13 | 9 | 17 | 9 | 11 | 15 | 5 | 6 | 14 | 3 | 10 | 12 | 7 | 2 | 4 | 8 | 8 | 1 | 0% |
Photo No. | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | No. 6 | No. 7 | No. 8 | No. 9 | No. 10 | No. 11 | No. 12 | No. 13 | No. 14 | No. 15 | No. 16 | No. 17 | No. 18 | No. 19 | No. 20 | Accuracy Rate |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Actual preference | 2 | 2 | 4 | 2 | 1 | 3 | 1 | 5 | 3 | 2 | 4 | 1 | 4 | 5 | 1 | 3 | 5 | 5 | 3 | 1 | |
KJ Prediction | 5 | 5 | 2 | 2 | 5 | 2 | 1 (O) | 4 | 2 | 2 (O) | 4 (O) | 1 (O) | 4 (O) | 4 | 5 | 1 | 4 | 5 | 3 | 2 | 20% |
AHP Prediction | 12 | 13 | 6 | 9 | 14 | 11 | 3 | 10 | 8 | 5 | 5 | 9 | 4 | 10 | 2 | 1 | 6 | 7 | 14 | 15 | 0% |
Photo No. | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | No. 6 | No. 7 | No. 8 | No. 9 | No. 10 | No. 11 | No. 12 | No. 13 | No. 14 | No. 15 | No. 16 | No. 17 | No. 18 | No. 19 | No. 20 | Accuracy Rate |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Actual Preference | 3 | 3 | 5 | 5 | 4 | 2 | 2 | 5 | 2 | 3 | 4 | 1 | 4 | 5 | 3 | 2 | 4 | 4 | 3 | 1 | |
ML Prediction | 3 (O) | 4 | 5 (O) | 5 (O) | 4 (O) | 3 | 2 (O) | 4 | 2 (O) | 3 (O) | 4 (O) | 2 | 4 (O) | 5 (O) | 3 (O) | 2 (O) | 4 (O) | 3 | 3 (O) | 1 (O) | 75% |
Photo No. | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | No. 6 | No. 7 | No. 8 | No. 9 | No. 10 | No. 11 | No. 12 | No. 13 | No. 14 | No. 15 | No. 16 | No. 17 | No. 18 | No. 19 | No. 20 | Accuracy Rate |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Actual Preference | 3 | 3 | 5 | 5 | 4 | 4 | 4 | 5 | 2 | 3 | 5 | 1 | 5 | 5 | 2 | 1 | 5 | 4 | 3 | 1 | |
ML Prediction | 3 (O) | 3 (O) | 5 (O) | 5 (O) | 4 (O) | 4 (O) | 4 (O) | 4 | 2 (O) | 3 (O) | 5 (O) | 1 (O) | 5 (O) | 5 (O) | 2 (O) | 1 (O) | 5 (O) | 4 (O) | 3 (O) | 2 | 90% |
Photo No. | No. 1 | No. 2 | No. 3 | No. 4 | No. 5 | No. 6 | No. 7 | No. 8 | No. 9 | No. 10 | No. 11 | No. 12 | No. 13 | No. 14 | No. 15 | No. 16 | No. 17 | No. 18 | No. 19 | No. 20 | Accuracy Rate |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Actual Preference | 2 | 2 | 4 | 2 | 1 | 3 | 1 | 5 | 3 | 2 | 4 | 1 | 4 | 5 | 1 | 3 | 5 | 5 | 3 | 1 | |
ML Prediction | 2 (O) | 2 (O) | 3 | 2 (O) | 1 (O) | 4 | 1 (O) | 5 (O) | 3 (O) | 2 (O) | 3 | 1 (O) | 4 (O) | 5 (O) | 1 (O) | 3 (O) | 4 | 5 (O) | 3 (O) | 1 (O) | 80% |
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Chen, C.-W. A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences? Sustainability 2023, 15, 3983. https://doi.org/10.3390/su15053983
Chen C-W. A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences? Sustainability. 2023; 15(5):3983. https://doi.org/10.3390/su15053983
Chicago/Turabian StyleChen, Chun-Wei. 2023. "A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?" Sustainability 15, no. 5: 3983. https://doi.org/10.3390/su15053983