# Session-Based Recommendation by Exploiting Substitutable and Complementary Relationships from Multi-behavior Data

arXiv: https://arxiv.org/abs/2312.14957 · HTML: https://mlsanity.com/paper/2312.14957/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2023.

## Built on by (most cited first, 1 of 1)

- [AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents](https://mlsanity.com/paper/2510.05598/) (2025)

## Abstract (arXiv)

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.