QuintoAndar Group is Latin America's largest housing platform, revolutionizing property rentals and sales. Headquartered in Brazil, it simplifies the housing process by eliminating paperwork and enhancing accessibility for tenants, buyers, and landlords. With thousands of houses available for each city, users struggle to find the ideal home. In this context, location plays a pivotal role, as it significantly influences property value, access to amenities, and life quality. A great location can make even a modest home highly desirable. Therefore, incorporating location into recommendations is essential for their effectiveness. We propose a geo-aware embedding framework to address sparsity and spatial nuances in housing recommendations on digital rental platforms. Our approach integrates an hierarchical H3 grid at multiple levels into a two-tower neural architecture. We compare our method with a traditional matrix factorization baseline and a single-resolution variant using interaction data from our platform. Embedding specific evaluation reveals richer and more balanced embedding representations, while offline ranking simulations demonstrate a substantial uplift in recommendation quality.

Neural Information Processing Systems http://nips.cc/

Significance: This paper belongs to an important class of algorithms that allow one to choose between privacy and accuracy. If data privacy continues to be in the public spotlight, this paper could be a nice addition to that field. Originality: To this reader's knowledge, their approach is novel, borrowing from common techniques in privacy aware learning. Q2: Please summarize your review in 1-2 sentences The paper illustrates a nice application of privacy aware learning to recommendation systems. Further experiments would strengthen the reader's understanding of how the algorithm performs, whether it meets its privacy goals, and how it compares to previous methods.

Neural Information Processing Systems http://nips.cc/

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. SUMMARY This paper proposes a nuclear norm penalized estimator for matrix completion problem, where the observations take a finite (discrete) number of values. Both with theoretical analysis and with numerical experiment, the authors verify the proposed approach is effective. I understand that there are cases where the observations are discrete and that we may need a distinguished algorithm for them, the recommendation systems may not be a good example. Although most recommender system datasets allow finite number of possible ratings (usually 1 to 5 stars), the output does not need to be finite.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Matrix factorization (MF) collaborative filtering is an effective and widely used method in recommendation systems. However, the problem of finding an optimal trade-off between exploration and exploitation (otherwise known as the bandit problem), a crucial problem in collaborative filtering from cold-start, has not been previously addressed. In this paper, we present a novel algorithm for online MF recommendation that automatically combines finding the most relevant items with exploring new or less-recommended items. Our approach, called Particle Thompson sampling for MF (PTS), is based on the general Thompson sampling framework, but augmented with a novel efficient online Bayesian probabilistic matrix factorization method based on the Rao-Blackwellized particle filter. Extensive experiments in collaborative filtering using several real-world datasets demonstrate that PTS significantly outperforms the current state-of-the-arts.