Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation (1609.09152v1)

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.

• The paper’s main contribution is the Fossil model that combines similarity-based recommendations with Markov Chains to address sparse data challenges.
• It integrates Factored Item Similarity Models with sequential dynamics, capturing both long-term user preferences and short-term item transitions.
• Evaluations on real-world datasets reveal Fossil improves AUC by up to 6.54% over traditional methods, demonstrating its practical effectiveness.

Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation

This paper addresses the challenge of predicting personalized sequential behavior in recommender systems by proposing a novel method, "Fossil." The model is designed to combine similarity-based methods with Markov Chains to effectively handle sparsity in datasets, a common problem in real-world applications.

Summary of the Approach

Recommender systems must model both long-term user preferences and short-term sequential patterns to predict user actions accurately. Traditional approaches, such as Matrix Factorization, focus on user-item interactions, while Markov Chains capture item-item transitions. However, these methods often struggle with sparse data, leading to ineffective recommendations.

To overcome this, the authors introduce FactOrized Sequential Prediction with Item SImilarity ModeLs (Fossil), which leverages Factored Item Similarity Models (FISM) for similarity-based item recommendation and integrates them with Markov Chains. Fossil parameterizes each user with weighting schemes over item sequences, allowing for the effective modeling of both user preferences and sequential behaviors.

Key Results

The authors evaluate Fossil across various large, real-world datasets including Amazon product reviews and Foursquare check-ins. The results demonstrate that Fossil significantly outperforms several state-of-the-art algorithms, particularly in sparse settings. The model showed an average of 2.79% improvement over FISM and 6.54% over FPMC on key AUC metrics, establishing its efficacy in handling sparsity.

Implications and Future Directions

The practical implication of Fossil is its enhanced capability to generate meaningful recommendations in environments where user behavior data is limited. Theoretically, the paper contributes to the field by illustrating how similarity-based methods can be effectively combined with sequential models to balance long- and short-term dynamics.

Future research could explore extensions of Fossil to incorporate higher-order dependencies or hybrid approaches that include additional data features such as user demographics or contextual information. Moreover, testing Fossil in real-world deployment scenarios could provide insights into user interaction and system design.

In conclusion, the Fossil model offers an innovative approach to addressing the sparsity challenge in sequential recommendation systems, providing a robust framework that enhances performance by effectively balancing different types of user dynamics.