Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation
This work addresses the sparsity challenge in sequential recommender systems, which is a common issue in real-world applications, but it is incremental as it builds on existing paradigms.
The paper tackles the problem of making personalized sequential recommendations in sparse datasets by fusing similarity-based methods with Markov Chains, resulting in the proposed Fossil method that outperforms alternative algorithms on large, real-world datasets, especially under sparsity conditions.
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.