Overshoot: Taking advantage of future gradients in momentum-based stochastic optimization

Overshoot is a novel, momentum-based stochastic gradient descent optimization method designed to enhance performance beyond standard and Nesterov's momentum. In conventional momentum methods, gradients from previous steps are aggregated with the gradient at current model weights before taking a step and updating the model. Rather than calculating gradient at the current model weights, Overshoot calculates the gradient at model weights shifted in the direction of the current momentum. This sacrifices the immediate benefit of using the gradient w.r.t. the exact model weights now, in favor of evaluating at a point, which will likely be more relevant for future updates. We show that incorporating this principle into momentum-based optimizers (SGD with momentum and Adam) results in faster convergence (saving on average at least 15% of steps). Overshoot consistently outperforms both standard and Nesterov's momentum across a wide range of tasks and integrates into popular momentum-based optimizers with zero memory and small computational overhead.

Understanding User Behavior in Carousel Recommendation Systems for Click Modeling and Learning to Rank

Carousels (also-known as multilists) have become the standard user interface for e-commerce platforms replacing the ranked list, the previous standard for recommender systems. While the research community has begun to focus on carousels, there are many unanswered questions and undeveloped areas when compared to the literature for ranked lists, which includes information retrieval research on the presentation of web search results. This work is an extended abstract for the RecSys 2023 Doctoral Symposium outlining a PhD project, with the main contribution of addressing the undeveloped areas in carousel recommenders: 1) the formulation of new click models and 2) learning to rank with click data. We present two significant barriers for this contribution and the field: lack of public datasets and lack of eye tracking user studies of browsing behavior. Clicks, the standard feedback collected by recommender systems, are insufficient to understand the whole interaction process of a user with a recommender requiring system designers to make assumptions, especially on browsing behavior. Eye tracking provides a means to elucidate the process and test these assumptions. Thus, to address these barriers and encourage future work, we will conduct an eye tracking user study within a carousel movie recommendation setting and make the dataset publicly available. Moreover, the insights learned on browsing behavior will help motivate the formulation of new click models and learning to rank.

Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis

Eye tracking in recommender systems can provide an additional source of implicit feedback, while helping to evaluate other sources of feedback. In this study, we use eye tracking data to inform a collaborative filtering model for movie recommendation providing an improvement over the click-based implementations and additionally analyze the area of interest (AOI) duration as related to the known information of click data and movies seen previously, showing AOI information consistently coincides with these items of interest.