Improving Rating and Relevance with Point-of-Interest Recommender System

TL;DR

This paper introduces a deep neural network approach that integrates multiple feedback sources and contextual information to enhance POI recommendations in location-based social networks, achieving significant improvements on large-scale data.

Contribution

It presents a novel deep learning model that combines collaborative and content data with auxiliary feedback to improve POI relevance estimation.

Findings

Significant performance improvements on large-scale datasets.

Effective integration of diverse feedback sources.

Enhanced representation learning for queries and items.

Abstract

The recommendation of points of interest (POIs) is essential in location-based social networks. It makes it easier for users and locations to share information. Recently, researchers tend to recommend POIs by treating them as large-scale retrieval systems that require a large amount of training data representing query-item relevance. However, gathering user feedback in retrieval systems is an expensive task. Existing POI recommender systems make recommendations based on user and item (location) interactions solely. However, there are numerous sources of feedback to consider. For example, when the user visits a POI, what is the POI is about and such. Integrating all these different types of feedback is essential when developing a POI recommender. In this paper, we propose using user and item information and auxiliary information to improve the recommendation modelling in a retrieval system. We develop a deep neural network architecture to model query-item relevance in the presence of both collaborative and content information. We also improve the quality of the learned representations of queries and items by including the contextual information from the user feedback data. The application of these learned representations to a large-scale dataset resulted in significant improvements.