An Intention-Aware Markov Chain Based Method for Top-K Recommendation

Abstract

Recommender systems play significant roles in business, especially in e-commerce. Nevertheless, users’ behaviors are usually mixing and drifting, which is hard to tackle. Current sequential methods of item-wise interest extracting suffer from the intricacy and sparsity of data. Inspired by that category-wise interests may be intrinsic drivers of user behaviors and heterogeneous actions may reveal certain behavior patterns, we introduce intention as a tuple of category and action to address the data issues. In this paper, an intention-aware Markov chain based sequential recommendation model (IMRec) is proposed. We model the overall preferences of users as the integration of long-term preferences and short-term intents. In particular, the matrix factorization method is adopted to extract the long-term user-item preference. For the modeling of the short-term intents transition, we adopt high-order Markov chain based methods. A factorized mixture transition distribution model for high-order Markov chain approximation is leveraged in this paper to reduce the algorithm complexity. An auxiliary loss on intention representation is utilized, which brings considerable performance improvements. Experiments on real-world datasets demonstrate that our model outperforms state-of-the-art baseline models in terms of three common metrics, and shows superior stability, scalability, and training efficiency. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In e-commerce, sequential recommenders are essential to facilitate decision-making and promote business. A big challenge is to capture the sequential patterns from the mixing and drifting user-item interaction sequences. Customer behaviors are often driven by their inherent intentions, which may present more stable and reliable patterns. Motivated by that, we propose a novel intention-aware next-item recommendation algorithm with high performance. Our method models the user-item preferences as the integration of long-term preferences and short-term intents. More specifically, long-term preferences show the general tastes of users, which are modeled by latent factors of users and items. Additionally, the intention is defined as a tuple of category and action. We model the short-term intents as the intention transition from the past intentions by a high-order Markov chain. We further leverage a factorization-based mixture transition distribution model for high-order Markov chain approximation and reduce the algorithm complexity. The proposed model is validated in 4 real-world datasets and shows good recommendation performance, performance stability, model scalability, and training efficiency. Our model provides an effective recommendation method at low computational costs for e-commerce companies.