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- Publisher Website: 10.1109/TASE.2022.3230783
- Scopus: eid_2-s2.0-85146231604
- WOS: WOS:000926561000001
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Article: An Intention-Aware Markov Chain Based Method for Top-K Recommendation
Title | An Intention-Aware Markov Chain Based Method for Top-K Recommendation |
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Authors | |
Keywords | Behavioral sciences Computational modeling Electronic commerce intention Markov chain Markov processes matrix factorization mixture transition distribution Recommendation systems Recommender systems Sparse matrices Training |
Issue Date | 2022 |
Citation | IEEE Transactions on Automation Science and Engineering, 2022 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/336362 |
ISSN | 2023 Impact Factor: 5.9 2023 SCImago Journal Rankings: 2.144 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ni, Shiying | - |
dc.contributor.author | Hu, Shiyan | - |
dc.contributor.author | Li, Lefei | - |
dc.date.accessioned | 2024-01-15T08:26:10Z | - |
dc.date.available | 2024-01-15T08:26:10Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | IEEE Transactions on Automation Science and Engineering, 2022 | - |
dc.identifier.issn | 1545-5955 | - |
dc.identifier.uri | http://hdl.handle.net/10722/336362 | - |
dc.description.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>Note to Practitioners</italic>—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. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Automation Science and Engineering | - |
dc.subject | Behavioral sciences | - |
dc.subject | Computational modeling | - |
dc.subject | Electronic commerce | - |
dc.subject | intention | - |
dc.subject | Markov chain | - |
dc.subject | Markov processes | - |
dc.subject | matrix factorization | - |
dc.subject | mixture transition distribution | - |
dc.subject | Recommendation systems | - |
dc.subject | Recommender systems | - |
dc.subject | Sparse matrices | - |
dc.subject | Training | - |
dc.title | An Intention-Aware Markov Chain Based Method for Top-K Recommendation | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TASE.2022.3230783 | - |
dc.identifier.scopus | eid_2-s2.0-85146231604 | - |
dc.identifier.eissn | 1558-3783 | - |
dc.identifier.isi | WOS:000926561000001 | - |