Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …

State-of-the-art sequential recommendation relies heavily on self-attention-based
recommender models. Yet such models are computationally expensive and often too slow …

Research on recommender systems algorithms, like other areas of applied machine
learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of …

The performance of a recommender system algorithm in terms of common offline accuracy
measures often strongly depends on the chosen hyperparameters. Therefore, when …

Collaborative filtering methods for recommender systems tend to represent users as a single
static latent vector. However, user behavior and interests may dynamically change in the …

Recommender Systems (RS) play an integral role in enhancing user experiences by
providing personalized item suggestions. This survey reviews the progress in RS inclusively …

Maximum inner product search (or k-MIPS) is a fundamental operation in recommender
systems that infer preferable items for users. To support large-scale recommender systems …

Recommender systems can be characterized as software solutions that provide users with
convenient access to relevant content. Traditionally, recommender systems research …

Recommender systems (RSs), as used by Netflix, YouTube, or Amazon, are one of the most
compelling success stories of AI. Enduring research activity in this area has led to a …

The origins of modern recommender systems date back to the early 1990s when they were
mainly applied experimentally to personal email and information filtering. Today, 30 years …