Multi-criteria recommender systems (MCRS) extend traditional recommendation techniques by incorporating multiple factors or attributes to improve the relevance of suggestions. Unlike single-criterion systems, which typically rely on user-item interactions (e.g., ratings or purchases), MCRS considers additional dimensions such as user preferences for specific features, contextual data, or item characteristics. For example, a movie recommender might use criteria like genre, director, runtime, and user ratings for acting or plot quality. By evaluating these factors together, MCRS aims to generate more personalized and nuanced recommendations that better align with user needs.
MCRS typically uses one of three technical approaches: aggregation, model-based methods, or machine learning. Aggregation combines multiple criteria into a single score, such as averaging weighted scores for price, durability, and design in a product recommender. Model-based methods, like multi-criteria matrix factorization, decompose user-item interactions into latent factors while preserving criteria-specific relationships. For instance, a restaurant recommender might model how users value cuisine type, price, and location differently. Machine learning approaches, such as decision trees or neural networks, train on datasets where each criterion is a feature. A hybrid system could combine collaborative filtering (user behavior) with content-based filtering (item attributes), using criteria like user demographics or item metadata to refine predictions. Developers often implement these methods using libraries like TensorFlow or Surprise, with custom logic to handle criteria weighting.
Challenges in MCRS include balancing criteria importance, handling sparse data, and computational complexity. For example, a travel app might prioritize budget over amenities for some users but reverse this for others, requiring dynamic weighting. Data sparsity arises when users rarely provide feedback on all criteria (e.g., rating a hotel’s cleanliness but not its WiFi). Techniques like matrix completion or transfer learning can mitigate this. Scalability is another concern, as adding criteria increases the dimensionality of models. Optimization strategies, such as dimensionality reduction or distributed computing frameworks like Apache Spark, help address this. Developers must also validate systems through A/B testing to ensure criteria weights align with real-world user satisfaction, iterating based on metrics like click-through rates or conversion scores.