We present our solution for the EvalRS DataChallenge. The EvalRS DataChallenge aims to build a more realistic recommender system considering accuracy, fairness, and diversity in evaluation. Our proposed system is based on an ensemble between an item-based variational auto-encoder (VAE) and a Bayesian personalized ranking matrix factorization (BPRMF). To mitigate the bias in popularity, we use an item-based VAE for each popularity group with an additional fairness regularization. To make a reasonable recommendation even the predictions are inaccurate, we combine the recommended list of BPRMF and that of item-based VAE. Through the experiments, we demonstrate that the item-based VAE with fairness regularization significantly reduces popularity bias compared to the user-based VAE. The ensemble between the item-based VAE and BPRMF makes the top-1 item similar to the ground truth even the predictions are inaccurate. Finally, we propose a `Coefficient Variance based Fairness' as a novel evaluation metric based on our reflections from the extensive experiments.

Recommender systems are widely used in industry to improve user experience. Despite great success, they have recently been criticized for collecting private user data. Federated Learning (FL) is a new paradigm for learning on distributed data without direct data sharing. Therefore, Federated Recommender (FedRec) systems are proposed to mitigate privacy concerns to non-distributed recommender systems. However, FedRec systems have a performance gap to its non-distributed counterpart. The main reason is that local clients have an incomplete user-item interaction graph, thus FedRec systems cannot utilize indirect user-item interactions well. In this paper, we propose the Federated Graph Recommender System (FedGRec) to mitigate this gap. Our FedGRec system can effectively exploit the indirect user-item interactions. More precisely, in our system, users and the server explicitly store latent embeddings for users and items, where the latent embeddings summarize different orders of indirect user-item interactions and are used as a proxy of missing interaction graph during local training. We perform extensive empirical evaluations to verify the efficacy of using latent embeddings as a proxy of missing interaction graph; the experimental results show superior performance of our system compared to various baselines. A short version of the paper is presented in \href{https://federated-learning.org/fl-neurips-2022/}{the FL-NeurIPS'22 workshop}.

If you missed the chance to pick up an Echo during Amazon's recent Prime Day sales event, the retailer has discounted the smart speaker to its lowest price ever. This weekend, you can buy the Echo for $50, or half off its usual $100 price. We gave Amazon's spherical smart speaker a score of 89 when it came out in 2020. Since then, it has remained one of our favorites in the category. The Echo sounds great for its small size, outperforming similarly priced smart speakers like the Nest Audio and HomePod mini.

Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to alleviate the range anxiety of private EV drivers. Previous studies focus on centralized strategies to achieve optimized resource allocation, particularly useful for privacy-indifferent taxi fleets and fixed-route public transits. However, private EV driver seeks a more personalized and resource-aware charging recommendation that is tailor-made to accommodate the user preference (when and where to charge) yet sufficiently adaptive to the spatiotemporal mismatch between charging supply and demand. Here we propose a novel Regularized Actor-Critic (RAC) charging recommendation approach that would allow each EV driver to strike an optimal balance between the user preference (historical charging pattern) and the external reward (driving distance and wait time). Experimental results on two real-world datasets demonstrate the unique features and superior performance of our approach to the competing methods.

Investors will soon be able to hook up with the world's most-popular gay-dating platform. A merger with the special-purpose acquisition company Tiga Acquisition, announced in May, values Grindr at $2.1 billion and is expected to close by the end of the year. As with any SPAC merger, historical details on the business are slim. In online dating, though, a snapshot often says all you need to know. Grindr's popularity relative to its total market size is impressive.

A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numerous nodes. A non-negative latent factor (NLF) model performs efficient representation learning to an HDI matrix, whose learning process mostly relies on a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm. However, an SLF-NMU algorithm updates a latent factor based on the current update increment only without appropriate considerations of past learning information, resulting in slow convergence. Inspired by the prominent success of a proportional-integral (PI) controller in various applications, this paper proposes a Proportional-Integral-incorporated Non-negative Latent Factor (PI-NLF) model with two-fold ideas: a) establishing an Increment Refinement (IR) mechanism via considering the past update increments following the principle of a PI controller; and b) designing an IR-based SLF-NMU (ISN) algorithm to accelerate the convergence rate of a resultant model. Empirical studies on four HDI datasets demonstrate that a PI-NLF model outperforms the state-of-the-art models in both computational efficiency and estimation accuracy for missing data of an HDI matrix. Hence, this study unveils the feasibility of boosting the performance of a non-negative learning algorithm through an error feedback controller.

The matrix completion problem seeks to recover a $d\times d$ ground truth matrix of low rank $r\ll d$ from observations of its individual elements. Real-world matrix completion is often a huge-scale optimization problem, with $d$ so large that even the simplest full-dimension vector operations with $O(d)$ time complexity become prohibitively expensive. Stochastic gradient descent (SGD) is one of the few algorithms capable of solving matrix completion on a huge scale, and can also naturally handle streaming data over an evolving ground truth. Unfortunately, SGD experiences a dramatic slow-down when the underlying ground truth is ill-conditioned; it requires at least $O(\kappa\log(1/\epsilon))$ iterations to get $\epsilon$-close to ground truth matrix with condition number $\kappa$. In this paper, we propose a preconditioned version of SGD that preserves all the favorable practical qualities of SGD for huge-scale online optimization while also making it agnostic to $\kappa$. For a symmetric ground truth and the Root Mean Square Error (RMSE) loss, we prove that the preconditioned SGD converges to $\epsilon$-accuracy in $O(\log(1/\epsilon))$ iterations, with a rapid linear convergence rate as if the ground truth were perfectly conditioned with $\kappa=1$. In our experiments, we observe a similar acceleration for item-item collaborative filtering on the MovieLens25M dataset via a pair-wise ranking loss, with 100 million training pairs and 10 million testing pairs. [See supporting code at https://github.com/Hong-Ming/ScaledSGD.]

A new study from University of Washington and Johns Hopkins shows that robots trained on artificial intelligence make decisions imbued with racism and sexism. Of course, robots are only the latest in a long line of new technologies found to perpetuate harmful stereotypes--so do search engines, social media, and video games, as well as other popular tech products trained on huge sets of data and driven by algorithms. That devices feed racist and sexist misinformation to adults is terrible enough. But, as a psychologist and advocate for kids, I worry even more about what's being fed to children, including the very young, who are also exposed to--and influenced by--tech-delivered misinformation about race. The study comes out at a time when, across the U.S., a wave of new legislation is censoring what educators can discuss in the classroom, including topics of race, slavery, gender identity, and politics.

Artificial Intelligence and Romance are an inevitable match. Love is the basis of human experience, yet it remains one of the most challenging emotions to understand and define. Since the dawn of time, we have been searching for that special someone to share our lives with, and in recent years, technology has begun to play an increasingly important role in this quest. The advent of online dating has transformed the way we connect with potential partners, and the growth of social media has created new opportunities for building relationships. But as our interactions with technology become more and more intimate, what role will artificial intelligence (AI) play in our search for love? AI is disrupting virtually every other area of our lives, from how we work and communicate to how we shop and consume.

A new study from University of Washington and Johns Hopkins shows that robots trained on artificial intelligence make decisions imbued with racism and sexism. Of course, robots are only the latest in a long line of new technologies found to perpetuate harmful stereotypes--so do search engines, social media, and video games, as well as other popular tech products trained on huge sets of data and driven by algorithms. That devices feed racist and sexist misinformation to adults is terrible enough. But, as a psychologist and advocate for kids, I worry even more about what's being fed to children, including the very young, who are also exposed to--and influenced by--tech-delivered misinformation about race. The study comes out at a time when, across the U.S., a wave of new legislation is censoring what educators can discuss in the classroom, including topics of race, slavery, gender identity, and politics.