  In recommender systems, multi-behavior methods have demonstrated their
effectiveness in mitigating issues like data sparsity, a common challenge in
traditional single-behavior recommendation approaches. These methods typically
infer user preferences from various auxiliary behaviors and apply them to the
target behavior for recommendations. However, this direct transfer can
introduce noise to the target behavior in recommendation, due to variations in
user attention across different behaviors. To address this issue, this paper
introduces a novel approach, Behavior-Contextualized Item Preference Modeling
(BCIPM), for multi-behavior recommendation. Our proposed
Behavior-Contextualized Item Preference Network discerns and learns users'
specific item preferences within each behavior. It then considers only those
preferences relevant to the target behavior for final recommendations,
significantly reducing noise from auxiliary behaviors. These auxiliary
behaviors are utilized solely for training the network parameters, thereby
refining the learning process without compromising the accuracy of the target
behavior recommendations. To further enhance the effectiveness of BCIPM, we
adopt a strategy of pre-training the initial embeddings. This step is crucial
for enriching the item-aware preferences, particularly in scenarios where data
related to the target behavior is sparse. Comprehensive experiments conducted
on four real-world datasets demonstrate BCIPM's superior performance compared
to several leading state-of-the-art models, validating the robustness and
efficiency of our proposed approach.
