MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation (1908.00413v1)

Abstract: This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items. To identify a user's preference in the cold state, existing recommender systems, such as Netflix, initially provide items to a user; we call those items evidence candidates. Recommendations are then made based on the items selected by the user. Previous recommendation studies have two limitations: (1) the users who consumed a few items have poor recommendations and (2) inadequate evidence candidates are used to identify user preferences. We propose a meta-learning-based recommender system called MeLU to overcome these two limitations. From meta-learning, which can rapidly adopt new task with a few examples, MeLU can estimate new user's preferences with a few consumed items. In addition, we provide an evidence candidate selection strategy that determines distinguishing items for customized preference estimation. We validate MeLU with two benchmark datasets, and the proposed model reduces at least 5.92% mean absolute error than two comparative models on the datasets. We also conduct a user study experiment to verify the evidence selection strategy.

• The paper introduces a meta-learning framework using MAML to efficiently adapt recommendations in cold-start scenarios.
• It employs a user preference estimator with embedding and decision layers that update locally via gradient descent.
• Evaluation on MovieLens 1M and Bookcrossing demonstrates a notable 5.92% MAE reduction, confirming enhanced recommendation accuracy.

Meta-Learning for Cold-Start Recommendation: An Analysis of MeLU

The paper "MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation," presented at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, introduces a novel approach to recommender systems specifically targeting the cold-start problem. Cold-start scenarios often impede recommendation systems due to sparse user-item interactions. The authors propose MeLU, a meta-learning-based user preference estimator designed to provide efficient recommendations with minimal user interaction data, using a model-agnostic meta-learning (MAML) framework. The significance of this work lies in its ability to rapidly adapt to the recommendation task of new users or items with only a few examples, initializing a promising direction for cold-start problems in recommendation systems.

System Overview and Methodology

MeLU aims to overcome two predominant limitations in existing recommender systems: inadequate performance for users with minimal interaction history and suboptimal candidate evidence selection that informs user preferences. Traditional collaborative filtering and content-based approaches often rely on substantial interaction history or rich user profiles, which are scarce in cold-start scenarios. In contrast, MeLU leverages meta-learning principles to quickly adapt to new users or items without extensive prior knowledge.

The architecture of MeLU includes a user preference estimator composed of embedding layers for user and item features, decision-making layers based on a fully connected neural network, and an output layer providing preference predictions. The innovation stems from employing a MAML approach, enabling the model to undertake personalized recommendations by locally updating the decision-making and output layers for each user based on their specific consumption history. This is achieved by computing local gradients that personalize the general model to the specific environment of the new user or item.

Evaluation and Results

The effectiveness of MeLU is rigorously validated on two benchmark datasets, MovieLens 1M and Bookcrossing, under various cold-start conditions. The results reveal substantial improvements over comparative models, with MeLU achieving at least a 5.92% reduction in mean absolute error (MAE) across test scenarios involving new users and items. These scenarios are particularly challenging as they mimic real-world conditions where user-item interaction data is sparse.

Critical to the system's success is the innovative evidence candidate selection strategy. The paper evaluates items based on the Frobenius norm gradient during personalization and popularity to choose evidence candidates that distinctively capture a user's preferences. This strategy leads to more relevant recommendations and better identification of user preferences, validated both through quantitative metrics and qualitative user studies.

Implications and Future Directions

MeLU’s framework represents a significant advancement in addressing the cold-start problem within recommendation systems through meta-learning. Practically, it suggests that personalization and effective recommendation can be achieved with minimal user data, potentially enhancing user satisfaction and retention in digital platforms. Theoretically, this work enriches the discourse on adapting meta-learning techniques to dynamic and sparse environments typical in web-based ecosystems.

Continued exploration of meta-learning in recommendation systems could yield further enhancements. Future research could investigate model update cycles in real-world applications, ensuring performance stability as user bases and item inventories evolve. Additionally, expanding the theoretical framework to incorporate a broader set of factors or alternative meta-learning algorithms could yield even more precise and user-centric recommendations.

In summation, MeLU exhibits the profound potential that meta-learning holds for cold-start problems, presenting a compelling blueprint for future recommender systems in environments characterized by limited user interactions.