RL-Based Recommendation System

• Reinforcement learning based recommendation systems are defined as MDP-formulated models that maximize long-term user satisfaction by integrating biclustering.
• The methodology employs biclustering for state/action space reduction and uses Q-learning to address challenges such as cold-start and data sparsity.
• Empirical evaluations on MovieLens datasets demonstrate improved precision and recall, with enhanced transparency and scalability in recommendations.

Reinforcement learning (RL) based recommendation systems formulate the recommendation process as a sequential decision-making problem, modeling the user–system interaction as a Markov decision process (MDP). Unlike traditional supervised learning methods that optimize for immediate user feedback, RL-based approaches aim to maximize long-term user satisfaction, allowing the system to adapt dynamically to evolving user preferences and behavioral patterns. Central challenges include large state and action spaces, the sparsity of user-item interactions, the need for dynamic policy adaptation, and the requirement for interpretable and robust solutions.

1. MDP Formulation and Biclustering-based State/Action Space Reduction

RL-based recommendation systems are commonly formalized as MDPs, where the key components are as follows:

• State Space (S): Each state represents information relevant to the user’s current situation or context. In the biclustering-based model, each state is mapped to a bicluster $(U, I)$, where $U$ is a group of users and $I$ a group of items (Choi et al., 2018). The biclusters are obtained by applying methods such as Bimax or Bibit to partition the user-item interaction matrix, producing $n^2$ biclusters mapped onto an $n \times n$ grid (the "gridworld").
• Action Space (A): To address the otherwise intractable action space, the biclustering approach restricts actions to four gridworld movements (up, down, left, right), drastically reducing the number of actions compared to the raw set of individual items.
• Transition Function (T): Transitions are deterministic; moving in direction $a_t$ from state $s_t$ yields state $s_{t+1}$.
• Reward Function (R): Rewards are computed using the Jaccard similarity of user sets for neighboring biclusters, i.e.,

$$R(s_t, a_t, s_{t+1}) = \frac{|U_{s_t} \cap U_{s_{t+1}}|}{|U_{s_t} \cup U_{s_{t+1}}|}$$

This rewards the agent for visiting states (biclusters) with overlapping user interests, promoting smooth transitions along relevant user/item groupings.

• Policy and Q-function: The policy $\pi(s)$ is learned using Q-learning or SARSA, with the state-action value function defined as:

$$Q_{\pi}(s, a) = \mathbb{E}_{\pi}\left\{\sum_{k=0}^{\infty} \gamma^k r_{t+k} \mid s_t=s, a_t=a\right\}$$

where $\gamma$ is the discount factor.

By leveraging biclustering, the method achieves two critical effects: a drastic reduction in state and action space cardinality, and the grouping of similar users/items to facilitate robust policy learning and easy cold-start handling.

2. Addressing Cold-Start and Data Sparsity

Cold-start—where users or items lack historical interaction data—poses a critical challenge for recommendation systems, especially under RL paradigms that rely on exploration and feedback signals.

The biclustering approach offers two effective strategies:

• User Grouping: When a new user enters, the system computes the Jaccard similarity between the user's interaction history and existing biclusters. The user is then associated with the best-matching bicluster, ensuring immediate, relevant recommendations even with minimal data (Choi et al., 2018).
• Online Model Updating: After each interaction, if the user is satisfied, they are added to the current bicluster's user set. This process instantly influences future recommendations and reward assignments, enabling adaptive learning that incorporates user feedback in real time.

The biclustering approach also inherently addresses matrix sparsity by operating in user–item subgroups, thus smoothing over missing data and allowing the agent to generalize from similar cases.

3. Integration of Biclustering and Explainability

A central advantage of the biclustering-based RL recommendation paradigm is its inherent explainability:

• Transparent Recommendations: Each recommendation is a function of the current bicluster $(U, I)$, and the system can explicitly inform users that “these items are recommended because many users with similar interests have engaged with them” (Choi et al., 2018).
• State Interpretability: Since states map to meaningful biclusters, both offline analysis and online interfaces can describe the rationale behind each recommendation, directly attributing decision-making to observed group characteristics.

This integration of model structure and human-interpretable signals improves both trust and user satisfaction in practical deployments.

4. Empirical Performance and Evaluation Metrics

The biclustering-based RL recommendation approach has been empirically validated:

• Datasets: Experiments were conducted on the MovieLens 100k and 1M datasets, under strict cold-start conditions (with only 10% of ratings per test user) (Choi et al., 2018).
• Metrics: Precision@30 ($\mathrm{P@30}$) and Recall@30 ($\mathrm{R@30}$) are computed as

$$\mathrm{P@N} = \frac{|I_N \cap I_D|}{N}, \quad \mathrm{R@N} = \frac{|I_N \cap I_D|}{|I_D|}$$

where $I_N$ is the top-N recommended list and $I_D$ the relevant items.

• Results: On MovieLens 1M, the proposed RL system achieves a precision of 0.277 and recall of 0.155, outperforming baselines such as Global-average, User-based, and Item-based recommendation models.

These results provide strong evidence that the biclustering-driven state/action compression does not merely offer computational benefits but also translates to tangible gains in quality—particularly in the presence of data sparsity and cold start.

5. RL-based Recommendation: Broader Context and Alternatives

Beyond biclustering, RL-based recommendation systems are developed along several axes:

• State Representation: Advanced models consider user–item histories, session context, or graph-based features as the state, enabling dynamic response to both short-term and long-term interests (Liu et al., 2018).
• Action Space Handling: Alternatives to biclustering include action candidate pruning, text-based embeddings, and slate-based decompositions. These approaches address tractability in large item repositories but require careful engineering to avoid loss of personalization (Wang et al., 2020).
• Reward Design: Reward functions may be immediate (click/purchase) or long-term (session retention, customer value), with ongoing research into grounding reward definitions in business objectives and user satisfaction.
• Exploration–Exploitation Tradeoff: RL systems employ various strategies (e.g., $\epsilon$-greedy, real-time online updates) to balance between recommending known favorites and exploring new, potentially relevant content.

The use of biclustering presents an elegant, theoretically grounded solution to several of these challenges, especially for systems grappling with sparse, high-dimensional user–item data.

6. Implementation Considerations and Deployment

Critical factors for deploying biclustering-based RL recommender systems include:

• Computational Efficiency: The $n^2$ state space and four-action structure enable rapid convergence and low per-decision compute cost, suitable for real-world applications.
• Model Maintenance: Online updates to bicluster user sets require efficient recomputation of Jaccard rewards but remain tractable due to the restricted structure.
• Scalability: The approach is robust to scaling in both user and item dimensions, as biclustering reduces raw complexity before policy optimization.
• Explainability and Trust: Alignment of biclusters with interpretable user/item groups naturally supports transparency for end-users and troubleshooting by engineers.

Systems designed this way can be integrated into operational recommendation pipelines, supporting both initial onboarding (cold-start) and long-term engagement through continual learning.

7. Conclusion

Biclustering-based reinforcement learning presents a principled, empirically validated framework for recommendation systems facing large and sparse state/action spaces. By decomposing the user–item matrix into meaningful groups, mapping these groups to RL tractable grids, and leveraging reward structures based on inter-group user similarity, the approach advances both performance and explainability. Empirical results on benchmark datasets confirm superior accuracy over traditional techniques, and the design inherently resolves long-standing cold-start and sparsity challenges. This methodology exemplifies the broader trajectory of RL-based recommenders: moving from static, one-off predictions to dynamic, context-aware, and interpretable decision-making systems.