A Multi-Source Information Learning Framework for Airbnb Price Prediction

TL;DR

This paper introduces a multi-source information embedding framework combining statistical, textual, and spatial data to improve Airbnb rental price prediction, addressing limitations of previous models.

Contribution

The study proposes a novel multi-source embedding model integrating diverse data types for more accurate Airbnb price prediction.

Findings

The model outperforms baseline methods in prediction accuracy.

Textual and spatial features significantly enhance prediction performance.

The multi-source approach effectively captures complex rental price determinants.

Abstract

With the development of technology and sharing economy, Airbnb as a famous short-term rental platform, has become the first choice for many young people to select. The issue of Airbnb's pricing has always been a problem worth studying. While the previous studies achieve promising results, there are exists deficiencies to solve. Such as, (1) the feature attributes of rental are not rich enough; (2) the research on rental text information is not deep enough; (3) there are few studies on predicting the rental price combined with the point of interest(POI) around the house. To address the above challenges, we proposes a multi-source information embedding(MSIE) model to predict the rental price of Airbnb. Specifically, we first selects the statistical feature to embed the original rental data. Secondly, we generates the word feature vector and emotional score combination of three different text information to form the text feature embedding. Thirdly, we uses the points of interest(POI) around the rental house information generates a variety of spatial network graphs, and learns the embedding of the network to obtain the spatial feature embedding. Finally, this paper combines the three modules into multi source rental representations, and uses the constructed fully connected neural network to predict the price. The analysis of the experimental results shows the effectiveness of our proposed model.