Addressing Hybrid Confounder Issue for Causal Session-Based Recommendation
In the field of session-based recommendation, users’ interaction behaviors are affected by both popularity and surrounding environmental factors, such as item popularity, season, or salary, and so on. Utilizing causal learning techniques to mitigate the negative impact of hybrid confounde...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11053863/ |
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| author | Quan Li Xinhua Xu Jinjun Liu Guangmin Li |
| author_facet | Quan Li Xinhua Xu Jinjun Liu Guangmin Li |
| author_sort | Quan Li |
| collection | DOAJ |
| description | In the field of session-based recommendation, users’ interaction behaviors are affected by both popularity and surrounding environmental factors, such as item popularity, season, or salary, and so on. Utilizing causal learning techniques to mitigate the negative impact of hybrid confounders on user preferences is a pressing challenge in session-based recommendation. Aiming at the above problems, this paper proposes a kind of causal session-based recommendation model for addressing hybrid confounder issue (HCCSRec). First, the conditional probability about user preference given a user sequence is solved by intervention and structural equation. Then, implicit environmental features are learned with observation sequence data. Next, user preference for the next item is estimated from the observation sequence, environmental feature and item popularity. Finally, the objective function is constructed by cross entropy and KL scatter, and the model parameters are learned. We perform extensive experiments on three real-world datasets from Beauty, MovieLens,<xref ref-type="fn" rid="fn2">2</xref> and Yelp. Empirical studies validate that compared with the state-of-art recommendation methods, the HCCSRec method is helpful to discover user real interests, and can further improve the recommendation accuracy. |
| format | Article |
| id | doaj-art-c7fb7d1dc64a4555b187faf705236e99 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c7fb7d1dc64a4555b187faf705236e992025-08-20T03:29:06ZengIEEEIEEE Access2169-35362025-01-011311205611206610.1109/ACCESS.2025.358385111053863Addressing Hybrid Confounder Issue for Causal Session-Based RecommendationQuan Li0https://orcid.org/0000-0001-6211-1046Xinhua Xu1Jinjun Liu2Guangmin Li3Department of Artificial Intelligence and Computer, Hubei Normal University, Huangshi, Hubei, ChinaDepartment of Artificial Intelligence and Computer, Hubei Normal University, Huangshi, Hubei, ChinaDepartment of Artificial Intelligence and Computer, Hubei Normal University, Huangshi, Hubei, ChinaDepartment of Artificial Intelligence and Computer, Hubei Normal University, Huangshi, Hubei, ChinaIn the field of session-based recommendation, users’ interaction behaviors are affected by both popularity and surrounding environmental factors, such as item popularity, season, or salary, and so on. Utilizing causal learning techniques to mitigate the negative impact of hybrid confounders on user preferences is a pressing challenge in session-based recommendation. Aiming at the above problems, this paper proposes a kind of causal session-based recommendation model for addressing hybrid confounder issue (HCCSRec). First, the conditional probability about user preference given a user sequence is solved by intervention and structural equation. Then, implicit environmental features are learned with observation sequence data. Next, user preference for the next item is estimated from the observation sequence, environmental feature and item popularity. Finally, the objective function is constructed by cross entropy and KL scatter, and the model parameters are learned. We perform extensive experiments on three real-world datasets from Beauty, MovieLens,<xref ref-type="fn" rid="fn2">2</xref> and Yelp. Empirical studies validate that compared with the state-of-art recommendation methods, the HCCSRec method is helpful to discover user real interests, and can further improve the recommendation accuracy.https://ieeexplore.ieee.org/document/11053863/Session-based recommendationcausal learninghybrid confoundercross entropy |
| spellingShingle | Quan Li Xinhua Xu Jinjun Liu Guangmin Li Addressing Hybrid Confounder Issue for Causal Session-Based Recommendation IEEE Access Session-based recommendation causal learning hybrid confounder cross entropy |
| title | Addressing Hybrid Confounder Issue for Causal Session-Based Recommendation |
| title_full | Addressing Hybrid Confounder Issue for Causal Session-Based Recommendation |
| title_fullStr | Addressing Hybrid Confounder Issue for Causal Session-Based Recommendation |
| title_full_unstemmed | Addressing Hybrid Confounder Issue for Causal Session-Based Recommendation |
| title_short | Addressing Hybrid Confounder Issue for Causal Session-Based Recommendation |
| title_sort | addressing hybrid confounder issue for causal session based recommendation |
| topic | Session-based recommendation causal learning hybrid confounder cross entropy |
| url | https://ieeexplore.ieee.org/document/11053863/ |
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