User-Curated Image Collections: Modeling and Recommendation

TL;DR

This paper introduces a novel recommendation system for socially curated image collections, focusing on modeling entire collections and measuring similarity to user preferences, validated through large-scale Pinterest data.

Contribution

It proposes a new approach to model image collections as a whole and employs a metric learning method for personalized collection recommendation.

Findings

Effective collection modeling via group sparse reconstruction

Improved recommendation accuracy demonstrated on Pinterest data

Outperforms several baseline methods

Abstract

Most state-of-the-art image retrieval and recommendation systems predominantly focus on individual images. In contrast, socially curated image collections, condensing distinctive yet coherent images into one set, are largely overlooked by the research communities. In this paper, we aim to design a novel recommendation system that can provide users with image collections relevant to individual personal preferences and interests. To this end, two key issues need to be addressed, i.e., image collection modeling and similarity measurement. For image collection modeling, we consider each image collection as a whole in a group sparse reconstruction framework and extract concise collection descriptors given the pretrained dictionaries. We then consider image collection recommendation as a dynamic similarity measurement problem in response to user's clicked image set, and employ a metric learner to measure the similarity between the image collection and the clicked image set. As there is no previous work directly comparable to this study, we implement several competitive baselines and related methods for comparison. The evaluations on a large scale Pinterest data set have validated the effectiveness of our proposed methods for modeling and recommending image collections.