Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation

TL;DR

This paper introduces Fossil, a method that combines similarity-based models with Markov Chains to improve personalized sequential recommendations, especially in sparse data scenarios, by capturing both long-term preferences and short-term dynamics.

Contribution

The paper proposes a novel fusion of similarity models with Markov Chains for sequential recommendation, addressing sparsity issues and enhancing personalization.

Findings

Fossil outperforms existing algorithms on large real-world datasets.

Fossil effectively captures personalized user dynamics.

The method shows significant improvements in sparse data environments.

Abstract

Predicting personalized sequential behavior is a key task for recommender systems. In order to predict user actions such as the next product to purchase, movie to watch, or place to visit, it is essential to take into account both long-term user preferences and sequential patterns (i.e., short-term dynamics). Matrix Factorization and Markov Chain methods have emerged as two separate but powerful paradigms for modeling the two respectively. Combining these ideas has led to unified methods that accommodate long- and short-term dynamics simultaneously by modeling pairwise user-item and item-item interactions. In spite of the success of such methods for tackling dense data, they are challenged by sparsity issues, which are prevalent in real-world datasets. In recent years, similarity-based methods have been proposed for (sequentially-unaware) item recommendation with promising results on sparse datasets. In this paper, we propose to fuse such methods with Markov Chains to make personalized sequential recommendations. We evaluate our method, Fossil, on a variety of large, real-world datasets. We show quantitatively that Fossil outperforms alternative algorithms, especially on sparse datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations.