Multi-View Interactive Collaborative Filtering
This addresses the challenge of making effective recommendations in dynamic, data-sparse environments, though it appears incremental by combining existing techniques like latent factors and bandit policies.
The paper tackled the problem of sparse user interaction data and high item turnover in recommender systems by proposing MV-ICTR, a partially online latent factor algorithm that integrates rating and contextual information with multi-armed bandit policies, resulting in significantly increased performance for cold-start users and items.
In many scenarios, recommender system user interaction data such as clicks or ratings is sparse, and item turnover rates (e.g., new articles, job postings) high. Given this, the integration of contextual "side" information in addition to user-item ratings is highly desirable. Whilst there are algorithms that can handle both rating and contextual data simultaneously, these algorithms are typically limited to making only in-sample recommendations, suffer from the curse of dimensionality, and do not incorporate multi-armed bandit (MAB) policies for long-term cumulative reward optimization. We propose multi-view interactive topic regression (MV-ICTR) a novel partially online latent factor recommender algorithm that incorporates both rating and contextual information to model item-specific feature dependencies and users' personal preferences simultaneously, with multi-armed bandit policies for continued online personalization. The result is significantly increased performance on datasets with high percentages of cold-start users and items.