Scalable Content-Aware Collaborative Filtering for Location Recommendation

doi:10.1109/TKDE.2018.2789445

Scalable Content-Aware Collaborative Filtering for Location Recommendation

2018-06-01

70

PRE

AI

DOI：10.1109/TKDE.2018.2789445

Original

Original request for help

Save

Abstract

En 中文

Location recommendation plays an essential role in helping people find attractive places. Though recent research has studied how to recommend locations with social and geographical information, few of them addressed the cold-start problem of new users. Because mobility records are often shared on social networks, semantic information can be leveraged to tackle this challenge. A typical method is to feed them into explicit-feedback-based content-aware collaborative filtering, but they require drawing negative samples for better learning performance, as users' negative preference is not observable in human mobility. However, prior studies have empirically shown sampling-based methods do not perform well. To this end, we propose a scalable Implicit-feedback-based Content-aware Collaborative Filtering (ICCF) framework to incorporate semantic content and to steer clear of negative sampling. We then develop an efficient optimization algorithm, scaling linearly with data size and feature size, and quadratically with the dimension of latent space. We further establish its relationship with graph Laplacian regularized matrix factorization. Finally, we evaluate ICCF with a large-scale LBSN dataset in which users have profiles and textual content. The results show that ICCF outperforms several competing baselines, and that user information is not only effective for improving recommendations but also coping with cold-start scenarios.

Keywords:

Implicit feedback

content-aware

location recommendation

weighted matrix factorization

Scalable Content-Aware Collaborative Filtering for Location Recommendation

Abstract

AI Summary

Journal

Organization