Approach for Personalized Recommendations to Enhance Customer Service Process in Peruvian Restaurants using OpenAI Contextual Chatbot
Average rating
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Star rating
Your vote was cast
Thank you for your feedback
Thank you for your feedback
Issue Date
2023-01-01
Metadata
Show full item recordJournal
Proceedings of the 2023 IEEE 30th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2023DOI
10.1109/INTERCON59652.2023.10326059Abstract
In the current digital era, efficiently accessing relevant information is crucial for various applications such as restaurant recommendation systems, website searches, book recommendations, among others. This study presents an approach for personalized recommendations in improving the customer service process in restaurants using OpenAI's Contextual Chatbot. The approach consists of six phases: (1) Data preprocessing, (2) Embedding and storage, (3) Scheduled updating, (4) Document retrieval, (5) Context adaptation and request creation, and (6) Response generation. OpenAI's text embeddings are used to convert application data into vectors and store them in a vector database. These vectors are used to retrieve similar records and generate contextualized responses using the GPT-3.5 model. The chatbot's performance is evaluated in terms of accuracy and user satisfaction. Two scenarios were used in the experimentation: (a) with the proposed solution and (b) without the solution. The results demonstrated an operational efficiency of 86.67% with the proposed solution and the versatility of the proposed methodology, showcasing its potential for application in a wide range of domains, including websites, books, PDFs, and other forms of documentation.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/embargoedAccessLanguage
engae974a485f413a2113503eed53cd6c53
10.1109/INTERCON59652.2023.10326059
Scopus Count
Collections