Generative recommendation systems have recently seen a surge in interest, largely due to the promising advancements in generative AI. As a competitive solution for multi-behavior sequence recommendations, much of the recent research has concentrated on predicting the next item a user will likely interact with using a generative approach. However, these methods often 1). assign multiple residual quantization layers to obtain item codes, which leads to extra storage costs of more codebooks. And 2). explicitly utilize behavior sequences leading to longer sequences, potentially increasing the training time as well as inference time compared with original sequences. In response to these challenges, we introduce theImplicitMulti-BehaviorGenerative recommendation with a mixture of quantization (IMBGen) approach in this paper. Specifically, we have devised aMixtureofQuantization (MoQ) that combines the merits of both residual and parallel quantization for a more effective tokenization process. Additionally, we propose an Implicit Behavior Modeling (IBM) framework, allowing for more efficient integration of users' behaviors into the interacted items. Finally, we conducted extensive experiments on two widely used benchmark datasets and further confirmed our findings with an online A/B test. The results consistently demonstrate the advantages of our approach over other baseline methods.

Online games often house virtual shops where players can acquire character skins. Our task is centered on tailoring skin recommendations across diverse scenarios by analyzing historical interactions such as clicks, usage, and purchases. Traditional multi-behavior recommendation models employed for this task are limited. They either only predict skins based on a single type of behavior or merely recommend skins for target behavior type/task. These models lack the ability to control predictions of skins that are associated with different scenarios and behaviors. To overcome these limitations, we utilize the pretraining capabilities of Large Sequential Models (LSMs) coupled with a novel stimulus prompt mechanism and build a controllable multi-behavior recommendation (CMBR) model. In our approach, the pretraining ability is used to encapsulate users' multi-behavioral sequences into the representation of users' general interests. Subsequently, our designed stimulus prompt mechanism stimulates the model to extract scenario-related interests, thus generating potential skin purchases (or clicks and other interactions) for users. To the best of our knowledge, this is the first work to provide controlled multi-behavior recommendations, and also the first to apply the pretraining capabilities of LSMs in game domain. Through offline experiments and online A/B tests, we validate our method significantly outperforms baseline models, exhibiting about a tenfold improvement on various metrics during the offline test.

Incorporating knowledge bases (KB) into endto-end task-oriented dialogue systems is challenging, since it requires to properly represent the entity of KB, which is associated with its KB context and dialogue context.The existing works represent the entity with only perceiving a part of its KB context, which can lead to the less effective representation due to the information loss, and adversely favor KB reasoning and response generation.To tackle this issue, we explore to fully contextualize the entity representation by dynamically perceiving all the relevant entities and dialogue history.To achieve this, we propose a COntextaware Memory Enhanced Transformer framework (COMET), which treats the KB as a sequence and leverages a novel Memory Mask to enforce the entity to only focus on its relevant entities and dialogue history, while avoiding the distraction from the irrelevant entities.Through extensive experiments, we show that our COMET framework can achieve superior performance over the state of the arts.