[Paper Review] Collaborative Filtering Bandits

Shuai Li, Alexandros Karatzoglou+1 more|arXiv (Cornell University)|Feb 11, 2015

Advanced Bandit Algorithms Research36 references3 citations

TL;DR

This paper proposes COFIBA, a collaborative filtering bandit algorithm that dynamically clusters users and items based on interaction patterns in real-time, enabling adaptive, scalable recommendations in highly dynamic environments like news and ads. It achieves lower regret and higher click-through rates than state-of-the-art clustering bandits by leveraging sparse graph-based clustering and a regret analysis under stochastic linear noise.

ABSTRACT

Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid. In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on medium-size real-world datasets, showing scalability and increased prediction performance (as measured by click-through rate) over state-of-the-art methods for clustering bandits. We also provide a regret analysis within a standard linear stochastic noise setting.

Motivation & Objective

• Address the limitations of classical collaborative filtering and contextual bandits in highly dynamic recommendation domains such as news and online advertising.
• Overcome the cold-start problem in user and item recommendations by exploiting collaborative effects through dynamic clustering.
• Develop a scalable, online algorithm that integrates user-item interaction data to form adaptive clusters over users and items simultaneously.
• Provide a theoretical regret analysis for the proposed method under a standard linear stochastic noise setting.
• Demonstrate improved prediction performance and scalability on real-world and benchmark datasets compared to existing clustering bandit methods.

Proposed method

• COFIBA uses sparse graph representations to model user and item interactions, avoiding computationally expensive matrix factorization.
• It alternately clusters users based on items under consideration and items based on the similarity of user clusterings induced by each item.
• The algorithm employs an exploration-exploitation strategy in a contextual multi-armed bandit framework, dynamically updating cluster assignments as new interactions occur.
• It leverages aggregate feature vectors (e.g., $ar{oldsymbol{w}}_{N_{k},t}$) to propagate information from established users to new users, aiding cold-start performance.
• The regret bound is derived by analyzing the expected sum of rewards, with the key term $ extstyle ilde{igcirc}ig(ig( ext{E}[S(h)] + ext{variance term}ig)ig)ig( ext{sqrt}(dT/n)ig)$, where $S(h)$ measures cluster size in a normalized way.
• The method is designed to be compatible with any standard clustering or co-clustering technique, with a focus on computational efficiency and theoretical tractability.

Experimental results

Research questions

• RQ1 Can dynamic clustering of users and items based on interaction patterns improve recommendation performance in highly dynamic environments like news and ads?
• RQ2 How can collaborative filtering effects be effectively integrated into a contextual bandit framework without relying on batch matrix factorization?
• RQ3 What is the theoretical regret performance of a bandit algorithm that adaptively clusters users and items based on mutual preference patterns?
• RQ4 Can the proposed method handle the cold-start problem more effectively than existing bandit or collaborative filtering approaches?
• RQ5 How does the algorithm scale and perform in practice compared to state-of-the-art clustering bandit methods on real-world datasets?

Key findings

• COFIBA achieves significantly higher click-through rates than state-of-the-art clustering bandit methods on medium-size real-world datasets, demonstrating improved prediction performance.
• The algorithm exhibits strong scalability, making it suitable for large-scale, dynamic recommendation systems such as online advertising and news recommendation.
• The regret bound of COFIBA is $ ilde{igcirc}ig(ig( ext{E}[S(h)] + ext{variance term}ig)ig)ig( ext{sqrt}(dT/n)ig)$, which matches the performance of a method that knows the latent clustering structure in advance.
• The method effectively mitigates the cold-start problem by propagating information from established users to new users through shared cluster memberships.
• Empirical results show that COFIBA outperforms existing approaches in both accuracy and adaptability in dynamic environments.
• The algorithm remains computationally efficient by relying on sparse graph representations rather than dense matrix factorization, enabling real-time deployment.