Expenditure Aware Rating Prediction for Recommendation

TL;DR

This paper introduces an expenditure-aware rating prediction model for recommender systems, leveraging expenditure data to improve accuracy and uncover latent user and business traits, outperforming existing methods.

Contribution

It is the first to systematically study the correlation between expenditures and ratings and incorporate this information into a low-rank matrix factorization model for improved predictions.

Findings

Higher expenditures correlate with higher ratings.

Expenditures above normal spending lead to higher scores.

The proposed EARP method outperforms state-of-the-art baselines.

Abstract

The rating score prediction is widely studied in recommender system, which predicts the rating scores of users on items through making use of the user-item interaction information. Besides the rating information between users and items, lots of additional information have been employed to promote recommendations, such as social relation and geographic location. Expenditure information on each transaction between users and items is widely available on e-commerce websites, often appearing next to the rating information, while there is seldom study on the correlation between expenditures and rating scores. In this paper, we first study their correlations in real data sets and propose the expenditure aware rating prediction problem. From the data sets crawled from a well-known social media platform Dianping in China, we find some insightful correlations between expenditures and rating scores: 1) transactions or experiences with higher expenditures usually lead to higher rating scores; 2) when the real expenditures are higher than users' normal spending behavior, the users usually give higher scores; and 3) there are multiple grades of expenditure behaviors. Based on these three observations, we propose an Expenditure ware RatingPrediction method (EARP), based on low-rank matrix factorization, to effectively incorporate the expenditure information. Extensive experiments on five real data sets show that EARP not only always outperforms other state-of-the-art baselines but also discovers the latent characteristics of users and businesses.