  In practical recommendation scenarios, users often interact with items under
multi-typed behaviors (e.g., click, add-to-cart, and purchase). Traditional
collaborative filtering techniques typically assume that users only have a
single type of behavior with items, making it insufficient to utilize complex
collaborative signals to learn informative representations and infer actual
user preferences. Consequently, some pioneer studies explore modeling
multi-behavior heterogeneity to learn better representations and boost the
performance of recommendations for a target behavior. However, a large number
of auxiliary behaviors (i.e., click and add-to-cart) could introduce irrelevant
information to recommenders, which could mislead the target behavior (i.e.,
purchase) recommendation, rendering two critical challenges: (i) denoising
auxiliary behaviors and (ii) bridging the semantic gap between auxiliary and
target behaviors. Motivated by the above observation, we propose a novel
framework-Denoising and Prompt-Tuning (DPT) with a three-stage learning
paradigm to solve the aforementioned challenges. In particular, DPT is equipped
with a pattern-enhanced graph encoder in the first stage to learn complex
patterns as prior knowledge in a data-driven manner to guide learning
informative representation and pinpointing reliable noise for subsequent
stages. Accordingly, we adopt different lightweight tuning approaches with
effectiveness and efficiency in the following stages to further attenuate the
influence of noise and alleviate the semantic gap among multi-typed behaviors.
Extensive experiments on two real-world datasets demonstrate the superiority of
DPT over a wide range of state-of-the-art methods. The implementation code is
available online at https://github.com/zc-97/DPT.
