Contrastive Learning for Cold-Start Recommendation (2107.05315v3)

Abstract: Recommending cold-start items is a long-standing and fundamental challenge in recommender systems. Without any historical interaction on cold-start items, CF scheme fails to use collaborative signals to infer user preference on these items. To solve this problem, extensive studies have been conducted to incorporate side information into the CF scheme. Specifically, they employ modern neural network techniques (e.g., dropout, consistency constraint) to discover and exploit the coalition effect of content features and collaborative representations. However, we argue that these works less explore the mutual dependencies between content features and collaborative representations and lack sufficient theoretical supports, thus resulting in unsatisfactory performance. In this work, we reformulate the cold-start item representation learning from an information-theoretic standpoint. It aims to maximize the mutual dependencies between item content and collaborative signals. Specifically, the representation learning is theoretically lower-bounded by the integration of two terms: mutual information between collaborative embeddings of users and items, and mutual information between collaborative embeddings and feature representations of items. To model such a learning process, we devise a new objective function founded upon contrastive learning and develop a simple yet effective Contrastive Learning-based Cold-start Recommendation framework(CLCRec). In particular, CLCRec consists of three components: contrastive pair organization, contrastive embedding, and contrastive optimization modules. It allows us to preserve collaborative signals in the content representations for both warm and cold-start items. Through extensive experiments on four publicly accessible datasets, we observe that CLCRec achieves significant improvements over state-of-the-art approaches in both warm- and cold-start scenarios.

Contrastive Learning for Cold-Start Recommendation: An In-Depth Analysis

The research paper titled "Contrastive Learning for Cold-Start Recommendation" presents a novel approach to addressing the fundamental challenge of recommending purely cold-start items within recommender systems. This problem arises when there is no historical interaction data available for newly introduced items, which makes traditional collaborative filtering methods inadequate due to their reliance on past user-item interactions to infer user preferences.

Key Contributions and Methodology

The paper introduces a reformulation of the cold-start item representation learning from an information-theoretic perspective. The proposed approach aims to maximize the mutual dependencies between item content and collaborative signals. To achieve this, the representation learning process is theoretically framed around maximizing two types of mutual information:

1. User-Item (U-I) Mutual Information: This represents the mutual information between the collaborative embeddings of users and items.
2. Representation-Embedding (R-E) Mutual Information: This signifies the mutual information between the feature representations derived from item content and the collaborative embeddings.

The authors propose a new objective function, leveraging contrastive learning to model the learning process. They introduce a framework named Contrastive Learning-based Cold-start Recommendation (CLCRec), which comprises three primary components:

• Contrastive Pair Organization: This involves the creation of positive and negative pairs for contrastive learning. Positive pairs consist of examples with similar semantics, while negative pairs comprise semantically dissimilar samples.
• Contrastive Embedding Networks (CENs): There are two CENs, one for U-I pairs and another for R-E pairs. These networks output scores that form the basis for computing mutual information.
• Contrastive Optimization: The optimization process aims at increasing the mutual information, therefore refining both the collaborative embeddings and the feature representations to preserve essential collaborative signals.

Experimental Evaluation

The authors conduct extensive experiments on four real-world datasets: Movielens, Tiktok, Kwai, and Amazon. The results demonstrate that CLCRec significantly outperforms existing state-of-the-art methods across various scenarios. Notably, the framework excels in cold-start conditions by effectively capturing the collaborative information inherent in content features. The results for both recall at top-k and NDCG are markedly improved, illustrating the potency of maximizing mutual information in aligning collaborative embeddings with the content-derived representations.

Implications and Future Directions

The proposed CLCRec framework shows significant promise in solving the cold-start item recommendation problem by bridging the gap between content features and collaborative signals. The theoretical foundation of mutual information maximization offers a solid framework for understanding and improving recommendation models for new items or users.

In terms of theoretical implications, this work opens avenues for further exploration of mutual information optimization in other domains within AI, potentially including explainable AI, where information disentanglement is critical. Practically, the framework is adaptable to various datasets and contexts, making it a versatile tool for commercial recommendation systems facing the cold-start challenge.

Looking ahead, future research could explore the integration of this framework with models incorporating additional side information or user-generated data. Furthermore, advancing this approach to dynamic recommendation scenarios, where user preferences and item attributes evolve over time, represents a compelling avenue for further exploration.

This paper fundamentally advances the field by presenting a mathematically grounded, innovative approach to a long-standing problem in recommendation systems, showcasing the effective application of contrastive learning techniques in a practical context.