> For the complete documentation index, see [llms.txt](https://davidadeola.gitbook.io/influx-ai-whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://davidadeola.gitbook.io/influx-ai-whitepaper/5.0-influx-ai-use-cases-and-applications/5.2-multimodal-technology-framework/5.2.5-travel-assistant-technology-stack.md).

# 5.2.5 Travel Assistant Technology Stack

The core module is a recommendation system based on collaborative filtering and content filtering. We will use Python's data science ecosystem, including Pandas, Numpy, Scikit-Learn and other libraries for feature engineering and model training.

Specifically, we first need to extract useful features from the user's historical hotel booking records, such as the hotel's star rating, type, price range, location, facilities, etc. This can be achieved through data cleaning and preprocessing, and finally form a user-hotel rating matrix.

Next, we can train a collaborative filtering model on this matrix to find neighboring users with similar tastes to the target user and generate personalized recommendations for the target user based on their preferences. Popular collaborative filtering algorithms such as neighbor-based methods and matrix decomposition can be quickly implemented using libraries such as Scikit-Learn.

At the same time, we will also train a content filtering-based model to capture users' preferences for hotel attribute features and integrate them with collaborative filtering results to form a hybrid recommendation system.

After the model training is completed, we will use Python Web frameworks such as Flask/Django to encapsulate it into a RESTful API to provide personalized hotel recommendation services. The front end can be developed using modern frameworks such as React/Vue.

In order to obtain the user's geographic location information, we can use HTML5's Geolocation API or third-party IP geolocation services for city-level positioning.

The system also requires a data layer to store user preferences and hotel metadata. You can use a relational database such as MySQL or PostgreSQL, or consider a more flexible NoSQL solution such as MongoDB.