  Generative recommendation has emerged as a promising paradigm aimed at
augmenting recommender systems with recent advancements in generative
artificial intelligence. This task has been formulated as a
sequence-to-sequence generation process, wherein the input sequence encompasses
data pertaining to the user's previously interacted items, and the output
sequence denotes the generative identifier for the suggested item. However,
existing generative recommendation approaches still encounter challenges in (i)
effectively integrating user-item collaborative signals and item content
information within a unified generative framework, and (ii) executing an
efficient alignment between content information and collaborative signals.
  In this paper, we introduce content-based collaborative generation for
recommender systems, denoted as ColaRec. To capture collaborative signals, the
generative item identifiers are derived from a pretrained collaborative
filtering model, while the user is represented through the aggregation of
interacted items' content. Subsequently, the aggregated textual description of
items is fed into a language model to encapsulate content information. This
integration enables ColaRec to amalgamate collaborative signals and content
information within an end-to-end framework. Regarding the alignment, we propose
an item indexing task to facilitate the mapping between the content-based
semantic space and the interaction-based collaborative space. Additionally, a
contrastive loss is introduced to ensure that items with similar collaborative
GIDs possess comparable content representations, thereby enhancing alignment.
To validate the efficacy of ColaRec, we conduct experiments on three benchmark
datasets. Empirical results substantiate the superior performance of ColaRec.
