  Session-based recommendation (SR) aims to dynamically recommend items to a
user based on a sequence of the most recent user-item interactions. Most
existing studies on SR adopt advanced deep learning methods. However, the
majority only consider a special behavior type (e.g., click), while those few
considering multi-typed behaviors ignore to take full advantage of the
relationships between products (items). In this case, the paper proposes a
novel approach, called Substitutable and Complementary Relationships from
Multi-behavior Data (denoted as SCRM) to better explore the relationships
between products for effective recommendation. Specifically, we firstly
construct substitutable and complementary graphs based on a user's sequential
behaviors in every session by jointly considering `click' and `purchase'
behaviors. We then design a denoising network to remove false relationships,
and further consider constraints on the two relationships via a particularly
designed loss function. Extensive experiments on two e-commerce datasets
demonstrate the superiority of our model over state-of-the-art methods, and the
effectiveness of every component in SCRM.
