I declare that this report titled Chatbot Beauty Skin Care Product Recommendations is my own work, except as cited in the references. Therefore, the project proposes a context-aware chatbot for skin care product recommendations based on skin types. Next, we built a sentiment analyzer based on DistilBERT to rate beauty products based on positive and negative results from product reviews.
Next, we train a skin type model to detect four skin types: dry, oily, combination, and natural using CNN. We then trained a recommendation system using a factorization engine to automatically recommend skin care products to users based on skin types. Most recently, we built a chatbot at Telegram that allows users to input a picture of their face for skin detection and product recommendations.
INTRODUCTION
Using the features learned from the CNN, a linear classifier (softmax) is trained for skin type classification. The user is recommended the product that is suitable for the particular skin type. Based on product reviews from real users, the product that is suitable for the skin type can be determined and recommended to the user using this recommendation.
To purchase the most suitable skin care product, the user's skin type must first be determined. Nowadays, different ways are used to determine the skin type of users. For example, by asking them to answer the questions to determine the skin type.
LITERATURE REVIEW
Users filled out a survey on their skin and sent a selfie to the chatbot. Bringing augmented reality innovation to the business of excellence, Sephora's beauty app uses facial recognition for users to experiment with skin makeup products anywhere. Let them virtually try on the makeup to see exactly if that eyeliner or that lipstick is like the wearer.
By trying on the skin makeup products, the users can virtually observe their entire appearance by following the step-by-step tutorials. It also offers coupons for cosmetic products that are promoted to the users before purchasing the products. The chatbot asks the users a series of questions such as the price range of the gift and the age of the person who will receive the gift.
SYSTEM METHODOLOGY
Only one component of the target vector t that is not equal to zero ti = tp. After the loss is defined, the gradient is calculated with respect to the output neurons of the CNN model to create it through the net and optimize the defined loss function that sets the net parameters. The gradient of CE loss respected for each of the CNN class counts in s needs to be evaluated.
Because the Softmax for positive class depends on the score of the negative classes, the loss gradient towards the negative classes does not cancel. The gradient expression is the same for all C except for the ground truth class Cp because the score for Cp (sp) is in the nominator. It is Sigmoid activation with the Cross-Entrophy loss as opposed to the Softmax loss which is independent for each of the vector elements or called class.
This means that the loss calculated for each of the CNN output class is not affected by the other element values. The multi-label classification is also used which means that the element belonging to the class does not influence the decision of classification of the other element belonging to other classes. The C independent binary classification problems are set to ( The loss is summed over the characteristic binary problems.
These two are, in the case of the telegram bot, the user and the telegram bot itself. In the proposed project, the users have to send their own facial images to the chatbot to identify their skin type and skin care product recommendations. Telegrambot's security encrypts the user's facial images and protects the user's privacy.
It will be released in 2017 and has a huge impact on the world of machine learning and AI.
SYSTEM IMPLEMENTATION
Observing the distribution of tags in the data, it is shown that there are many more positive reviews than negative reviews. The added text is shown to mean essentially the same as the original text. After performing text zoom on the negatively rated texts, it is shown that the number of negative texts (with 0 tags) has doubled.
This will definitely help train the sentiment analysis model better and improve the accuracy of the model. Faculty of Information and Communication Technology (Kampar Campus), UTAR Figure 4-2-6: DistilBERT model for sentiment analysis. The data set can be increased and the value of epoch can be increased to increase the accuracy of the model.
For example, when product 2 is entered into the model, the percentage of positive and negative ratings for each skin type is shown. When a random image is fed into the recommendation system, it will first determine the skin type. For example, when the image with the dry skin type is fed into the recommendation system,
In the proposed project, the Telegram Chatbot connects the user and the skin care product recommendation system. The Telegram Chatbot will ask for the name of the user and greet the user after providing the name. There are no restrictions on the amount of face images that the users import, they can import as many face images as they want.
If the user's input image is not a face image, the system is unable to detect it.
SYSTEM EVALUATION AND DISCUSSION
For example, if a combination skin image is entered into the telegram chat bot, and the skin identification results of the chat bot are also a combination skin type (see Figure 5-1-3), then it is counted as TP (True Positive). TN, FP and FN values are counted by adding cells, but we don't need to count them manually with Confusion Matrix online calculator. Accuracy is also called positive predictive value, which is the proportion of positive cases that are correctly identified.
The recall is also called sensitivity, which is the proportion of true positive cases correctly identified. The F1 score is the harmonic mean of precision and recall values for the classification problem. The number of data is increased to 1000 with the number of 250 data per skin type data set.
The first objective is to train a skin type classification model using CNN for skin type based product recommendations. The accuracy of the CNN model is 85% using Epoch = 50. The second objective is to train a sentiment analysis model using product review data crawled from cosmetic website. The accuracy of the model is 95% using Epoch = 10. The final goal is to train a recommender system to suggest product types based on user skin types.
A/B Testing
The results show that there are more respondents (90%) who feel that the proposed work is more accurate in skin classification. This is because the proposed work can identify the skin type of the user when the facial image of the user is sent to the chatbot. Compared to the Sephora Live Chat (refer to Figure 6-2-4) which does not have the function to determine skin type online, the proposed chat bot can easily detect the skin type and recommend products to users based on their skin type.
The proposed work helps to identify the skin type of the users and give specific product recommendations, while Sephora recommends the best-selling skin care products (refer Figure 6-2-7). Sephora recommends the skin care products based on the popularity of the products but not according to the skin type of the users. Therefore, the respondents feel that the skin care product recommendations for Sephora are not as accurate as compared to the proposed work.
There are more respondents with 90% of them feeling that the proposed chatbot gives more personalized and personalized recommendations for skin care products according to the user, while Sephora offers different skin care products for users to choose by themselves (see Figure 6-2-9). Meanwhile, the proposed chatbot will provide personalized recommendations to users based on his/her skin type. All respondents have better user experience using the proposed work in terms of simplicity and efficiency.
The proposed work is simple to use and straightforward as it provides product recommendation after the user enters his/her face image. Sephora live chat recommends skin care products that are only rated, which is not accurate and efficient. There are more respondents with 95% of them finding access to the proposed work faster and easier.
In the proposed work, users only need to search the username of the chatbot via Telegram and do not need to download any applications. refer Figure 6-2-15) The proposed work provides easier accessibility and convenience for the users so that they can access the chatbot within a short time.
CONCLUSION
Asmat Nizam Abdul-Talib, "Brand Consciousness and Brand Loyalty: A Study on Beauty Foreign Brand Beauty and Skin Care Products," 2020. D az, "A Qualitative Analysis of Facebook Messenger's Most Popular Chatbots," Proceedings of the 33rd Annual ACM Symposium on Applied Computing, no. Available: https://venturebeat.com loreal-beauty-bot-learns-your-style-to-make-gift-giving-easier/.
The required originality parameters and limitations approved by UTAR are as follows:. i) the total similarity index is 20% or less, and. ii) Matching of individual cited sources must be less than 3% each and (iii) Matching of texts in a continuous block must not exceed 8 words. Note The supervisor/candidate(s) must provide the Faculty/Institute with an electronic copy of the complete originality report set. Based on the above results, I declare that I am satisfied with the originality of the final year project report submitted by my students as mentioned above.
All references in the bibliography are cited in the thesis, especially in the chapter on literature review.
