Multi-Domain Collaborative Filtering (1203.3535v1)

Abstract: Collaborative filtering is an effective recommendation approach in which the preference of a user on an item is predicted based on the preferences of other users with similar interests. A big challenge in using collaborative filtering methods is the data sparsity problem which often arises because each user typically only rates very few items and hence the rating matrix is extremely sparse. In this paper, we address this problem by considering multiple collaborative filtering tasks in different domains simultaneously and exploiting the relationships between domains. We refer to it as a multi-domain collaborative filtering (MCF) problem. To solve the MCF problem, we propose a probabilistic framework which uses probabilistic matrix factorization to model the rating problem in each domain and allows the knowledge to be adaptively transferred across different domains by automatically learning the correlation between domains. We also introduce the link function for different domains to correct their biases. Experiments conducted on several real-world applications demonstrate the effectiveness of our methods when compared with some representative methods.

• The paper introduces Multi-Domain Collaborative Filtering (MCF), a probabilistic framework utilizing probabilistic matrix factorization to address data sparsity in recommendation systems by adaptively learning inter-domain correlations and correcting domain biases.
• Experimental results on MovieLens and Book-Crossing datasets show that MCF and its variant MCF-LF consistently outperform baseline methods like PMF and CMF in terms of RMSE, particularly demonstrating robust performance on sparser data like Book-Crossing.
• MCF offers significant potential for enhancing accuracy and mitigating sparsity in large-scale recommendation systems across diverse domains, with possibilities for future advancements through techniques like active learning.

Multi-Domain Collaborative Filtering: Addressing Data Sparsity in Recommendation Systems

Collaborative filtering (CF) techniques have widely been appreciated for their efficiency in recommendation systems, capitalizing on the assumption that users with similar preferences will rate items similarly. Despite their successful application across platforms like Amazon and Netflix, these methods face a significant hurdle—data sparsity, which arises due to insufficient rating data from users, leading to suboptimal prediction accuracy. The paper by Zhang, Cao, and Yeung introduces an innovative probabilistic framework to tackle this issue, termed as Multi-Domain Collaborative Filtering (MCF).

Proposed Framework and Methodology

The researchers propose a multi-domain CF problem where ratings across several domains are modeled collectively. They employ probabilistic matrix factorization (PMF) in each domain, facilitating knowledge transfer across domains by learning inter-domain correlations. This inter-domain exchange potentially alleviates the data sparsity challenge by leveraging shared user preferences across different categories, thus enriching the rating matrix.

The crux of the method lies in automatic learning and adjustment of domain correlation through a matrix-variate normal distribution. This approach allows each domain to customize its latent user and item feature matrices while integrating relational knowledge from other domains seamlessly.

Furthermore, the introduction of a link function aims to correct biases inherent in different domains, enhancing the accuracy of predictions by transforming the discrete rating scale to better fit the probabilistic model.

Experimental Validation and Results

The paper details experiments conducted using the MovieLens and Book-Crossing datasets, both of which demonstrate heterogeneous item domains suitable for multi-domain analysis. The MCF and its variant with the link function (MCF-LF) consistently outperform baseline methods, including traditional PMF and CMF approaches, in terms of RMSE across multiple domains.

For the MovieLens dataset, the application of MCF-LF led to substantial improvements with RMSE scores being lower than those achieved by CMF, highlighting the advantage of adaptive domain correlation learning. On the Book-Crossing dataset, while CMF's shared feature assumption faltered, MCF-LF showed robust performance owing to its dynamic correlation modeling.

Implications and Future Directions

The implications of this research are profound for large-scale recommendation systems, particularly in e-commerce platforms with diverse product categories. The proposed MCF framework supports the idea that leveraging multiple domains can effectively mitigate sparsity while enhancing rating prediction accuracy across the board.

Theoretical implications also abound; the correlation matrix learning can provide insights into domain similarity and user preference crossover, potentially influencing subsequent model refinements in the CF domain.

Looking forward, incorporating active learning techniques could further empower CF models by selectively querying informative data points to enhance learning efficiency. The integration of active learning within this probabilistic framework could present a formidable stride in robust recommendation system development.

This paper provides a significant contribution in multi-domain recommendation systems, laying the groundwork for continually advancing CF techniques in both accuracy and adaptability.