Recommending Short-lived Dynamic Packages for Golf Booking Services

TL;DR

This paper presents a hybrid recommendation approach for short-lived golf packages, addressing cold start and attribute sparsity issues, and demonstrating improved precision over baseline methods.

Contribution

It introduces a novel hybrid recommender system tailored for ephemeral packages, combining user analysis, pricing, environmental data, and collaborative filtering.

Findings

Improved recommendation precision over baseline methods

Effective handling of short-lived, uninformative packages

Addresses cold start and attribute sparsity challenges

Abstract

We introduce an approach to recommending short-lived dynamic packages for golf booking services. Two challenges are addressed in this work. The first is the short life of the items, which puts the system in a state of a permanent cold start. The second is the uninformative nature of the package attributes, which makes clustering or figuring latent packages challenging. Although such settings are fairly pervasive, they have not been studied in traditional recommendation research, and there is thus a call for original approaches for recommender systems. In this paper, we introduce a hybrid method that leverages user analysis and its relation to the packages, as well as package pricing and environmental analysis, and traditional collaborative filtering. The proposed approach achieved appreciable improvement in precision compared with baselines.