# Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation

arXiv: https://arxiv.org/abs/2006.06922 · HTML: https://mlsanity.com/paper/2006.06922/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 18 of 18)

- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [A Survey on User Behavior Modeling in Recommender Systems](https://mlsanity.com/paper/2302.11087/) (2023)
- [Multi-Behavior Sequential Recommendation with Temporal Graph Transformer](https://mlsanity.com/paper/2206.02687/) (2022)
- [Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation](https://mlsanity.com/paper/2402.14473/) (2024)
- [M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations](https://mlsanity.com/paper/2209.11824/) (2022)
- [Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation](https://mlsanity.com/paper/2403.16427/) (2024)
- [Preference Discerning with LLM-Enhanced Generative Retrieval](https://mlsanity.com/paper/2412.08604/) (2024)
- [Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach](https://mlsanity.com/paper/2311.04760/) (2023)
- [Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems](https://mlsanity.com/paper/2210.12940/) (2022)
- [Uncovering the Unseen: Discover Hidden Intentions by Micro-Behavior Graph Reasoning](https://mlsanity.com/paper/2308.15169/) (2023)
- [FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning](https://mlsanity.com/paper/2402.03481/) (2024)
- [Matryoshka Model Learning for Improved Elastic Student Models](https://mlsanity.com/paper/2505.23337/) (2025)
- [Hierarchical Multi-Task Learning Framework for Session-based Recommendations](https://mlsanity.com/paper/2309.06533/) (2023)
- [BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation](https://mlsanity.com/paper/2402.12733/) (2024)
- [Forecasting Live Chat Intent from Browsing History](https://mlsanity.com/paper/2408.04668/) (2024)
- [Identifying Shopping Intent in Product QA for Proactive Recommendations](https://mlsanity.com/paper/2404.06017/) (2024)
- [A Survey of Reasoning for Substitution Relationships: Definitions, Methods, and Directions](https://mlsanity.com/paper/2404.08687/) (2024)
- [Making Recommender Systems More Knowledgeable: A Framework to Incorporate Side Information](https://mlsanity.com/paper/2406.00615/) (2024)

## Abstract (arXiv)

Session-based recommendation (SR) has become an important and popular component of various e-commerce platforms, which aims to predict the next interacted item based on a given session. Most of existing SR models only focus on exploiting the consecutive items in a session interacted by a certain user, to capture the transition pattern among the items. Although some of them have been proven effective, the following two insights are often neglected. First, a user's micro-behaviors, such as the manner in which the user locates an item, the activities that the user commits on an item (e.g., reading comments, adding to cart), offer fine-grained and deep understanding of the user's preference. Second, the item attributes, also known as item knowledge, provide side information to model the transition pattern among interacted items and alleviate the data sparsity problem. These insights motivate us to propose a novel SR model MKM-SR in this paper, which incorporates user Micro-behaviors and item Knowledge into Multi-task learning for Session-based Recommendation. Specifically, a given session is modeled on micro-behavior level in MKM-SR, i.e., with a sequence of item-operation pairs rather than a sequence of items, to capture the transition pattern in the session sufficiently. Furthermore, we propose a multi-task learning paradigm to involve learning knowledge embeddings which plays a role as an auxiliary task to promote the major task of SR. It enables our model to obtain better session representations, resulting in more precise SR recommendation results. The extensive evaluations on two benchmark datasets demonstrate MKM-SR's superiority over the state-of-the-art SR models, justifying the strategy of incorporating knowledge learning.