Source-linked AI summary
Deep Reinforcement Learning for List-wise Recommendations
Xiangyu Zhao, Liang Zhang, Long Xia, Zhuoye Ding, Dawei Yin, Jiliang Tang
TL;DR
The paper addresses the limits of static, short-term recommendation strategies in environments where user preferences and rewards evolve over sequential interactions. It proposes LIRD, a list-wise deep reinforcement learning framework with an offline user-agent simulator, and reports effective performance on a real-world e-commerce dataset. The study also identifies positional order as a scope boundary for its list-wise interaction model.
Problem
Existing recommender systems commonly follow fixed strategies and optimize immediate rewards, overlooking users’ dynamic preferences and long-term rewards.
Method
LIRD models recommendation sessions as Markov Decision Processes, uses deep reinforcement learning for list-wise actions, and trains and evaluates through an offline user-agent interaction simulator.
Results
The proposed framework outperforms most representative baselines in recommendation performance, while LIRD can be trained more efficiently than DQN.
Takeaways & Limitations
List-wise reinforcement learning can capture relationships among recommended items while supporting large and dynamic item spaces and reducing redundant computation.
Takeaways & Limitations
LIRD assumes users browse recommended items in positional order, although later items may influence decisions about earlier items; temporal order remains future work.
Abstract
from arXiv · showhide
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedbacks. In particular, we introduce an online user-agent interacting environment simulator, which can pre-train and evaluate model parameters offline before applying the model online. Moreover, we validate the importance of list-wise recommendations during the interactions between users and agent, and develop a novel approach to incorporate them into the proposed framework LIRD for list-wide recommendations. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.
1 INTRODUCTION
Traditional recommender systems often use fixed strategies and optimize immediate rewards, despite users’ preferences and outcomes changing over time. LIRD applies deep reinforcement learning, list-wise recommendation, and offline simulation to learn strategies for dynamic, large-scale recommendation settings.
- Traditional recommender systems use fixed greedy strategies and often maximize immediate ordering rewards rather than future cumulative rewards.
- Reinforcement learning models recommendation as sequential user-agent interaction, continuously updating strategies to fit changing preferences and maximize long-term cumulative reward.
- 1.1 List-wise Recommendations: List-wise recommendation models each recommendation as multiple interdependent items, enabling complementary and diverse lists instead of independently selected similar items.
- 1.2 Architecture Selection: LIRD uses an Actor-Critic policy whose actor outputs state-specific scoring parameters, allowing efficient recommendation over large or dynamic action spaces.
- 1.3 Online Environment Simulator: An online user-agent environment simulator maps states and selected actions to simulated rewards from historical user records, supporting offline training and evaluation before deployment.
- 1.4 Our Contributions: The framework combines a simulator, deep reinforcement learning, and list-wise recommendations, and its effectiveness is demonstrated on a real-world e-commerce dataset.
2 THE PROPOSED FRAMEWORK
The framework models recommendation as sequential user-agent interactions in an MDP, using an online simulator and deep reinforcement learning to learn list-wise policies from historical behavior. It combines state-action reward prediction, discounted list rewards, state-specific scoring, and Actor-Critic updates for large action spaces.
- 2.1 Problem Statement: The recommender agent models sequential item-list recommendations as an MDP with states, actions, rewards, transitions, and discount factor γ.The state is based on browsing history, the action is a list of K items, and feedback includes skipping, clicking, or ordering items.
- 2.2 Online User-Agent Interaction Environment Simulator: The online simulator predicts rewards by matching current state-action pairs to historical interactions stored in memory.It uses users’ browsing histories and similarity-based stochastic mapping to generate simulated feedback for offline training and evaluation.
- 2.2 Online User-Agent Interaction Environment Simulator: Reward permutations reduce simulator computation by grouping historical interactions according to the limited number of possible list-level reward combinations.For two recommended items with skip/click/order rewards of 0/1/5, there are nine possible reward permutations rather than one group per historical record.
- 2.2 Online User-Agent Interaction Environment Simulator: The simulator computes a discounted overall list reward so higher-ranked items contribute more strongly to the recommendation objective.The discount factor Γ ∈(0, 1] weights the item position, encouraging the recommender agent to place potentially ordered items near the top.
- 2.3 The Actor Framework: The list-wise recommendation procedure generates state-specific scoring parameters and then constructs an action from item scores based on the user’s current state.This addresses variation in user interests by using current positive browsing signals rather than relying only on scores averaged across users.
- 2.4 The Critic Framework: The Critic approximates the action-value function, while the Actor updates its parameters toward actions judged more compatible with the current state.Deep neural approximators are used because recommender systems have enormous state and action spaces and nonlinear action-value functions.
3 EXPERIMENTS
Experiments on a real e-commerce dataset compare LIRD with representative baselines and examine list length and simulator parameter sensitivity. LIRD performs competitively, trains faster than DQN, and achieves its best performance with K = 4 and α = 0.2, while positional order remains a stated limitation.
- Experimental Settings: The experiments use 100,000 recommendation sessions containing 1,156,675 items, split temporally into 70% training and 30% testing sessions.Each state uses N = 10 previously clicked or ordered items, and each recommendation list contains K = 4 items.
- Overall Performance: In short sessions, RNN, DQN, and LIRD achieve comparable performance, whereas in long sessions DQN and LIRD significantly outperform RNN.The paper attributes the long-session advantage to reinforcement learning’s trade-off between short-term and long-term rewards, unlike RNN’s immediate-reward objective.
- Overall Performance: LIRD outperforms most representative baselines in recommendation performance and trains more efficiently than DQN.The comparison includes CF, FM, DNN, RNN, and DQN; LIRD is reported to perform similarly to DQN while training much faster.
- List-Wise Recommendations: LIRD performance first increases and then decreases as recommendation-list length K grows, with the best performance at K = 4.The paper frames K = 1 as item-wise recommendation and links smaller K to lost within-list correlations and larger K to introduced noise.
- List-Wise Recommendations: An appropriately selected list-wise strategy boosts recommendation performance by accounting for correlations among items in the same recommendation list.The authors identify list-wise recommendation as the answer to their second experimental question.
- Simulator Sensitivity: LIRD achieves its best reported performance when the simulator parameter α = 0.2, with action similarity contributing more than state similarity to reward mapping.α controls the trade-off between state and action similarity in the online simulator.
- Discussion of Positional and Temporal Order: The framework assumes users browse recommendation pages from top to bottom, although later-positioned items may influence earlier items when users defer purchase decisions.The authors leave the mismatch between positional and temporal order for future investigation.
4 RELATED WORK
Related work covers traditional recommendation techniques and reinforcement-learning approaches for sequential recommendation. It also situates the paper among methods addressing high-dimensional or combinatorial recommendation action spaces.
- Traditional Recommendation Techniques: Traditional recommendation techniques include collaborative filtering, content-based filtering, and knowledge-based systems.Collaborative filtering uses preference information from users with similar tastes, while content-based filtering uses properties of previously ordered items.
- Reinforcement Learning for Recommendations: Reinforcement-learning recommendation methods model recommendation as sequential interaction and learn actions from web usage or user-related data.The reviewed approaches include Q-learning formulations and agents for sequential decision problems with high-dimensional combinatorial slate-action spaces.
- Reinforcement Learning for Recommendations: The related-work discussion distinguishes fixed recommendation techniques from reinforcement-learning methods that continuously learn recommendation strategies.The paper frames its own recommendation procedure as sequential user-agent interaction rather than a static recommendation process.
5 CONCLUSION
LIRD improves recommendation performance and shows that list-wise strategies outperform item-wise strategies in experiments on real e-commerce data. The paper also identifies broader interaction patterns, item orders, and applications for future investigation.
- LIRD improves recommendation performance on a real-world e-commerce dataset.
- List-wise recommendation strategies outperform item-wise strategies.
- Future work includes investigating temporal item order in addition to the positional order used in this work.
- The authors propose studying additional agent-user interaction patterns, including adding items to shopping carts.
- Further applications are planned, especially those involving both positive and negative skip signals.