Multi-View Interactive Collaborative Filtering
Maria Lentini Umashanger Thayasivam
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.