Recent work has explored generative recommender systems as an alternative to traditional ID-based models, reframing item recommendation as a sequence generation task over discrete item tokens. While promising, such methods often underperform in practice compared to well-tuned ID-based baselines like SASRec. In this paper, we identify two key limitations holding back generative approaches: the lack of collaborative signal in item tokenization, and inefficiencies in the commonly used encoder-decoder architecture. To address these issues, we introduce COSETTE, a contrastive tokenization method that integrates collaborative information directly into the learned item representations, jointly optimizing for both content reconstruction and recommendation relevance. Additionally, we propose MARIUS, a lightweight, audio-inspired generative model that decouples timeline modeling from item decoding. MARIUS reduces inference cost while improving recommendation accuracy. Experiments on standard sequential recommendation benchmarks show that our approach narrows, or even eliminates, the performance gap between generative and modern ID-based models, while retaining the benefits of the generative paradigm.

Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts.

Marius cursed and jammed a mic stand between the crash bars of the TV studio door. "If SWAT's on its way, we don't have much time," he said. Michaela, who up until a couple of minutes ago had been streaming their interview live, still sat on one of the oval chairs under the hot lights. "What are they talking about?" The cube-shaped television studio had black-painted walls surrounding the bright stage area. Big monitors on the walls were showing the same "live" feed as they had five minutes ago, but now a red banner flashed at the bottom of the screens: ACTIVE SHOOTER AT COMPLETE PICTURES BUILDING. Michaela pointed at a moving figure on the screen. Apparently I like assault rifles." Adan, their cameraman, had called up a local news feed after the first shouts of panic and confusion filtered through the studio's thick doors. What it showed was entirely and completely not what the three of them were seeing. Marius was inside the windowless second-floor studio, empty-handed, yet the monitors showed what looked like a drone feed of him moving into and out of view through the building's windows on the 10th floor. He was armed, and every now and then he would pause and shoot, calmly and methodically. Marius shook his head in disgust. "Hey, Adan, could you give me a hand with this?" The cameraman was hunched over his laptop. "The same people who own the SWAT team," said Marius. "But forget what I said.

Editor's note: This month, Elsevier Connect is exploring "the creative face of science and medicine." In learning how to play chess, we learn how the pieces move and the relative value of knights and rooks and pawns. But as we master the game, the creative elements emerge. We discover that we can choose an opening that will lead to a slow, cautious game with the strategic maneuvering of pieces – or a wide open board where pieces are exchanged in rapid succession and the position changes constantly. We realize that recognizing patterns is as important as cold calculation.