Research on Mobile Marketing Recommendation Method Incorporating Layout Aesthetic Preference for Sustainable m-Commerce
Abstract
:1. Introduction
2. Literature Review
2.1. Mobile Marketing Recommendation Methods
2.2. Layout Design and Quantification
2.3. Aesthetic Preference and Its Affecting Factors
3. Modeling
- Phase I, Recommendation list generation based on traditional user interests. Use existing content-based and collaborative-filtering methods, to generate a recommendation list. The process requires information about traditional user preferences. Traditional user preferences do not contain the information of user aesthetic preferences for interface layouts. Therefore, in this phase, the same data used by existing recommendation methods are used to generate a recommendation list.
- Phase II, Recommendation list generation based on aesthetic preference for layout. User aesthetic preferences for layout are used to generate a list of marketing recommendations. By using the layout aesthetic quantification method and aesthetic preference similarity method, we calculate similarities between layout designs and the layout aesthetic preferences between users, respectively. Modified content-based and collaborative-filtering methods are combined to generate a recommendation list based on layout aesthetic preference.
- Phase III, Recommendation list rearranging. The recommendation lists generated in the previous two stages are merged and sorted. The sorted recommendation list is output as the recommendation result. The recommended model framework of the method is shown in Figure 1.
3.1. Recommendation List Generation Based on Traditional User Interests
- Initialize the recommendation list with contents to satisfy traditional user interests and set it to ∅.
- If a preference records exist for , a content-based method is adopted. Let hold the preference records of , as , is the attribute vector of , for a marketing content , is the attribute vector of . If , then put into , is the similarity of and , is the threshold marketing content similarity.
- If there are no existing preference records for , a collaborative-filtering method is adopted. Pick a user from U, whose preference records exist in the system. For users and , and are the profile attribute vectors of and , respectively, represents the similarity of and . If , then put the items in ’s preferred list to , is the user similarity threshold.
- Returns
3.2. Recommendation List Generation Based on Aesthetic Preference For Layout
3.2.1. The Quantitative Method for Layout Design of Mobile Marketing Content Interface
3.2.2. User Similarity Measurement Based on Influencing Factors of Layout Aesthetic Preference
3.2.3. Comprehensive Similarity Model of Attributes Based on Independence Weight
3.2.4. The Process of Recommendation List Generation Based Aesthetic Preference for Layout
- Initialize the recommendation list with contents that satisfy aesthetic preference for the layout of () and set it to ∅.
- If the aesthetic preference for layout records exist for , a content-based method is adopted. Let hold the preference records of , as , is the layout quantification attributes vector of , for a marketing content , is the layout quantification attributes vector of . If , then put into , is the layout similarity of and , is the threshold of marketing content layout similarity.
- If there are no existing aesthetic preferences for layout records for , a collaborative-filtering method is adopted. Pick a user from U whose aesthetic preference for layout records exist in the system. For users and , and are the attribute vectors of factors influencing aesthetic preference of and respectively, represents the aesthetic preference similarity of and . If , then put the items in ’s preferred list to , is the user similarity threshold.
- Returns .
3.3. Recommendation List Rearranging
- If , go to step (2), otherwise go to step (3);
- Append the element with the order of to , set , if , go to step (1), otherwise return and stop;
- Get the auxiliary order of the elements with the orders of and , append the element with a smaller auxiliary order to , the main order of the remaining element is set to , set , if , go to step (1), else return and stop.
4. Experiment
4.1. Experiment Design
- Subject preparation. We decide the number of our subjects to be 500, according to the size of subjects in Horowitz’s research on an event-related recommendation system (no public data set support). The subjects differ in gender, age, and location.
- Experimental materials preparation. For a single content task, 30 images of interfaces of the same content with different layouts (differ in layout attributes) are designed, the images are grayed out to eliminate the impact of colors. A sample of the materials is shown in Figure 4. For conventional tasks, we collect data for both LAPR and the four classical methods. The data for different attributes used in these methods are collected from the subjects. Existing mobile marketing contents (together with their metadata extracted from the text content of the mobile marketing content) [93,94] are collected from the mobile Internet and store with their interfaces (as images) by the means of a web crawler. The images of the collected marketing contents are grayed out, and an image object detection algorithm is run to extract data used for layout quantification [95,96].
- Training. A total of 200 subjects are selected to conduct several rounds of feedback operations. Use the prepared materials for the conventional task, feedback data (including users’ selections, clicks, and interactions to the materials) are collected. The data are then used in the training process to obtain the values of parameters.
- Phase 1. The marketing content of the single content task is an activity in which users can get a data traffic coupon from the ISP. A total of 200 subjects from the remaining 300 subjects attended this phase of our experiment. We chose them because they are the users of the same ISP (China Mobile) and have all been familiar with these kinds of activities recommended by the ISP’s marketing department. This ensures that the contents of the experimental materials have a neutral impact on user preferences. Feedback data of the subjects on the TOP-10 recommendation list generated by each method are collected.
- Phase 2. We perform TOP-5 recommendation tasks for each algorithm on the subject scale of 20%, 60%, and 100% of the remaining 300 subjects, using conventional task materials. Another set of tasks run TOP-10, TOP-15, and TOP-20 recommendation tasks on the subject scale of 100% of the remaining 300 subjects. Feedback data of the subjects are collected for each of the methods under all the tasks in this phase.
4.2. Data Preprocessing
5. Results and Discussion
5.1. Results and Discussion for the Single Content Task
5.2. Results and Discussion for Conventional Tasks
5.2.1. The Qualities of the TOP-5 Recommendation on Different Subject Scales
5.2.2. The Qualities of TOP-N Recommendations for All Subjects
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Method | LAPR | UB-CF | TAGB | TAGIB | ADR-CFT |
---|---|---|---|---|---|
Precision | 0.3893 | 0.1814 | 0.1765 | 0.1861 | 0.1881 |
Subject Scale | LAPR | UB-CF | TAGB | TAGIB | ADR-CFT |
---|---|---|---|---|---|
20% | 0.0765 | 0.0605 | 0.0745 | 0.0752 | 0.0755 |
60% | 0.0745 | 0.0595 | 0.0695 | 0.0715 | 0.0725 |
100% | 0.0723 | 0.0581 | 0.0696 | 0.0705 | 0.0712 |
Subject Scale | LAPR | UB-CF | TAGB | TAGIB | ADR-CFT |
---|---|---|---|---|---|
20% | 0.0393 | 0.0254 | 0.0315 | 0.0331 | 0.0372 |
60% | 0.0381 | 0.0215 | 0.0285 | 0.0313 | 0.0353 |
100% | 0.0355 | 0.0195 | 0.0284 | 0.0291 | 0.0325 |
Subject Scale | LAPR | UB-CF | TAGB | TAGIB | ADR-CFT |
---|---|---|---|---|---|
20% | 0.0517 | 0.0358 | 0.0442 | 0.0458 | 0.0497 |
60% | 0.0503 | 0.0316 | 0.0404 | 0.0432 | 0.0472 |
100% | 0.0476 | 0.0292 | 0.0398 | 0.0411 | 0.0446 |
TOP-N | LAPR | UB-CF | TAGB | TAGIB | ADR-CFT |
---|---|---|---|---|---|
TOP-5 | 0.0726 | 0.0583 | 0.0695 | 0.0705 | 0.0713 |
TOP-10 | 0.0695 | 0.0455 | 0.0605 | 0.0605 | 0.0615 |
TOP-15 | 0.0605 | 0.0369 | 0.052 | 0.0525 | 0.0538 |
TOP-20 | 0.0475 | 0.0255 | 0.0427 | 0.0405 | 0.0415 |
TOP-N | LAPR | UB-CF | TAGB | TAGIB | ADR-CFT |
---|---|---|---|---|---|
TOP-5 | 0.0355 | 0.0195 | 0.0282 | 0.0293 | 0.0325 |
TOP-10 | 0.0345 | 0.0153 | 0.0224 | 0.0245 | 0.0271 |
TOP-15 | 0.0294 | 0.0126 | 0.0155 | 0.0174 | 0.0235 |
TOP-20 | 0.0247 | 0.0073 | 0.0131 | 0.0152 | 0.0197 |
TOP-N | LAPR | UB-CF | TAGB | TAGIB | ADR-CFT |
---|---|---|---|---|---|
TOP-5 | 0.0476 | 0.0292 | 0.0398 | 0.0411 | 0.0446 |
TOP-10 | 0.0461 | 0.0226 | 0.0323 | 0.0344 | 0.0375 |
TOP-15 | 0.0392 | 0.0180 | 0.0239 | 0.0257 | 0.0321 |
TOP-20 | 0.0319 | 0.0110 | 0.0196 | 0.0219 | 0.0261 |
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Xiao, L.; Mao, H.; Wang, S. Research on Mobile Marketing Recommendation Method Incorporating Layout Aesthetic Preference for Sustainable m-Commerce. Sustainability 2020, 12, 2496. https://doi.org/10.3390/su12062496
Xiao L, Mao H, Wang S. Research on Mobile Marketing Recommendation Method Incorporating Layout Aesthetic Preference for Sustainable m-Commerce. Sustainability. 2020; 12(6):2496. https://doi.org/10.3390/su12062496
Chicago/Turabian StyleXiao, Liang, Hangxiao Mao, and Shu Wang. 2020. "Research on Mobile Marketing Recommendation Method Incorporating Layout Aesthetic Preference for Sustainable m-Commerce" Sustainability 12, no. 6: 2496. https://doi.org/10.3390/su12062496