HyFairCRS: Hypergraph Contrastive Learning for Fair CRS

• The paper introduces a multi-module architecture that integrates hypergraph and line graph convolutions with contrastive multi-view learning to mitigate fairness issues.
• It leverages diverse data sources—entities, items, words, and reviews—to build robust multi-interest representations for dynamic conversational recommendation.
• Empirical findings demonstrate improved fairness metrics and recommendation quality over baselines, highlighting reduced popularity bias and enhanced dialogue generation.

Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (HyFairCRS) is a framework designed to simultaneously address fairness and diversity in dynamic conversational recommendation environments by leveraging multi-dimensional hypergraph modeling and contrastive learning. The approach is motivated by persistent fairness challenges—such as popularity bias, filter bubbles, and the Matthew effect—that arise in real-world recommender systems and are often amplified in interactive conversational settings. HyFairCRS introduces a principled, multi-module architecture that learns, fuses, and exploits rich user multi-interest representations to provide both effective and equitable recommendations and dialogue responses (2507.02000).

1. Motivation and Problem Formulation

Unfairness in conversational recommender systems (CRSs) often manifests as biased exposure or over-personalization, disproportionately affecting users or items based on attributes such as popularity, gender, or demographic group. Conventional offline or static fairness interventions are insufficient in interactive CRSs, where bias is compounded over time (e.g., leading to filter bubbles or echo chambers). HyFairCRS is designed to promote multi-interest diversity fairness: capturing the full range of user interests, preventing domination by popular or majority tastes, and ensuring iterative, conversational recommendations resist degenerating into unfair feedback cycles.

The central problem addressed by HyFairCRS is: Given a sequence of interactive dialogues and historical behavioral/contextual data, how can one construct multi-dimensional user interest representations and integrate them into a recommendation and dialogue generation pipeline in a way that promotes both state-of-the-art performance and rigorous fairness? The system must deliver high-quality item suggestions and informative responses while also mitigating classic unfairness effects.

2. Hypergraph-Based Multi-Interest Representation Learning

The core representational paradigm in HyFairCRS is the use of multiple hypergraphs to encode the heterogeneity and diversity of user interests. Distinct from classic graphs (whose edges link only two nodes), hypergraphs enable each hyperedge to connect an arbitrary subset of nodes—suitable for modeling higher-order relations among users, items, entities, and contexts.

Multi-dimensional Hypergraph Construction

HyFairCRS constructs several hypergraphs, each guided by a distinct external signal:

• Entity-guided hypergraph: Nodes represent entities extracted from knowledge graphs (e.g., DBpedia) as mentioned in user dialogues.
• Item-guided hypergraph: Nodes are items appearing in conversational history.
• Word-guided hypergraph: Nodes are keywords or semantic concepts derived from dialogue (potentially expanded via resources such as ConceptNet).
• Review-guided hypergraph: Nodes correspond to extracted tokens or features from user-generated reviews.

Each hypergraph at conversation turn $t$ is denoted as: $$\mathcal{G}^{(t)}_* = (\mathcal{V}^{(t)}_*, \mathcal{E}^{(t)}_*, H^{(t)}_*)$$ where $\mathcal{V}^{(t)}_*$ is the node set, $\mathcal{E}^{(t)}_*$ the hyperedge set, and $H^{(t)}_*$ the incidence matrix for the $*$-guided channel.

Intra- and Inter-hyperedge Relationships

For each hypergraph, the corresponding line graph is generated, whose nodes represent hyperedges and whose edges reflect overlaps among the participating entities/items. This dual representation allows simultaneous exploitation of local (within hyperedge) and global (across-hyperedge) relational patterns.

Hypergraph and Line Graph Convolutions

To obtain user interest embeddings, both the hypergraph (node-level) and line graph (edge-level) paths are processed through dedicated (hyper)graph convolution modules; for instance: $$O^{(l+1)}_* = V_*^{-1} H^{(t)}_* W^{(l)}_* E_*^{-1} (H^{(t)}_*)^\top O^{(l)}_*$$ where $W^{(l)}_*$ are trainable convolution parameters, and $V_*$, $E_*$ are node and hyperedge degree matrices. These produce multiple, diverse embeddings for each user, corresponding to various interest signals.

3. Contrastive Multi-View Learning for Interest Diversity

To further enhance representation richness, contrastive learning is applied both within and across the multi-hypergraph and line graph channels. The aim is to align positive pairs (representations from congruent interest sources) and push apart negative pairs (from incongruent sources), thus promoting diversity and mitigating bias.

A representative InfoNCE-style contrastive loss is used: $$\mathcal{J}^{\mathcal{H}_{(a,b)}} = -\log \frac{\exp[\operatorname{sim}(X^{(h)}_a, X^{(h)+}_b)/\tau]}{\sum_{k=0}^K \exp[\operatorname{sim}(X^{(h)}_a, X^{(h)-}_{b,k})/\tau]}$$ where $\operatorname{sim}(\cdot, \cdot)$ is cosine similarity, $\tau$ is a temperature parameter, $X^{(h)}_*$ represents hypergraph-based embeddings for various channels, and $+$/$-$ refer to positive/negative samples. A similar loss is defined over the line graph representations; the total contrastive loss is averaged over these components.

This multi-view contrastive approach ensures no single interest or data source dominates, reinforcing fair and robust multi-interest extraction.

4. Integration into Fair Conversational Recommendation

The multi-faceted user interest representations are integrated directly into both the recommendation pipeline and dialogue generation architecture.

Representation Fusion

Outputs from all hypergraph and line graph modules are aggregated: $$X_{\rm fair} = [X^{(h)}_e; X^{(h)}_i; X^{(h)}_w; X^{(h)}_r; X^{(l)}_e; X^{(l)}_i; X^{(l)}_w; X^{(l)}_r]$$ where concatenation captures all channel signals. Further pooling and multi-head attention are employed: $$\begin{aligned} X_{\mathrm{fair\_reco}} &= \text{Pooling}\left([\text{Pooling}(X_{\rm fair}); X_{\mathrm{curr}}]\right) \ X_{\mathrm{fair\_conv}} &= \mathrm{MHA}(X_{\mathrm{curr}}, X_{\rm fair}, X_{\rm fair}) \end{aligned}$$ Here, $X_{\mathrm{curr}}$ is the current conversation context, ensuring dynamic adaptation.

Recommendation and Dialogue Modules

• Recommendation: Item candidates are scored via a softmax over the dot product between $X_{\mathrm{fair\_reco}}$ and item representations. The recommendation objective combines standard cross-entropy with the contrastive loss:

$$\mathcal{J}_{\rm CL\_R} = \alpha\, \mathcal{J}_{\rm CL} + \mathcal{J}_R$$

• Dialogue Generation: The conversational response decoder is a Transformer-based encoder-decoder integrating $X_{\mathrm{fair\_conv}}$ via multi-head attention at each generation step:

$$A^n_1 = \mathrm{MHA}(A^n_0, X_{\mathrm{fair\_conv}}, X_{\mathrm{fair\_conv}})$$

allowing generated utterances to be dynamically conditioned on fair, multi-interest user context.

5. Fairness Enhancement Mechanisms

The HyFairCRS design systematically addresses fairness through its architecture and learning objectives:

• Multi-interest fairness: By learning user preferences from heterogeneous sources, the likelihood that one dominant interest (e.g., a popular trend) controls recommendations is diminished.
• Dynamic diversity: The use of contrastive losses between multiple views (entities, items, words, reviews) breaks filter bubbles and avoids long-term feedback drift common in conversational settings.
• Balanced representation fusion: Both local (hypergraph) and global (line graph) information are incorporated, offering resilience against noise or bias in any single source.

Empirical results demonstrate improved fairness as quantified by lower Average Popularity ($A@K$), Gini coefficient ($G@K$), KL-divergence ($L@K$), and higher Difference ($D@K$) compared to prior methods. This corresponds to a preference distribution that is less skewed and more inclusive (2507.02000).

6. Empirical Validation and Benchmark Results

HyFairCRS has been evaluated on multiple CRS datasets, including REDIAL, TG-REDIAL, OpenDialKG, and DuRecDial. Key empirical findings include:

• Recommendation performance: HyFairCRS achieves superior Recall@K, MRR, and NDCG compared to strong baselines such as HiCore and HyCoRec.
• Conversational quality: Generated dialogues are judged by automatic metrics (BLEU, diverse n-gram counts) to be both more contextually appropriate and more linguistically diverse.
• Fairness metrics: Across all major fairness metrics, HyFairCRS consistently demonstrates reduced popularity bias and more equitable exposure, even as system-user interactions evolve temporally.

Ablation studies confirm that the removal of any hypergraph or line graph channel results in degraded performance—supporting the necessity of capturing multifaceted and hierarchical interest signals.

7. Theoretical and Practical Implications

HyFairCRS provides evidence that unifying multi-hypergraph contrastive learning with conversational modeling yields gains in fairness, diversity, and utility in CRS settings. The architecture is applicable across languages and domains (as shown by REDIAL and TG-REDIAL results) and is compatible with contemporary dialogue and recommendation backbones (Transformer models, knowledge graph integrations, etc.).

This framework is highly pertinent for practitioners seeking to deploy CRS systems in sectors where algorithmic fairness and user satisfaction are critical, including e-commerce, digital media, and social platforms. The ability to counteract reinforcement of majority preferences—while maintaining recommendation quality—addresses longstanding concerns on feedback loop bias and equity in interactive recommendation settings.

Conclusion

HyFairCRS establishes a robust, empirically validated approach for achieving fairness in conversational recommender systems by harnessing the representational power of multi-dimensional hypergraphs combined with contrastive multi-interest learning. Its architectural strategies—heterogeneous hypergraph modeling, line graph augmentation, contrastive loss application, and fusion into both recommendation and dialogue modules—enable it to advance both state-of-the-art performance and fairness, with substantial implications for the design of next-generation CRS platforms (2507.02000).