Time-Aware and Grey Incidence Theory Based User Interest Modeling for Document Recommendation

Cheng, Shulin; Liu, Yuejun

doi:10.1515/cait-2015-0027

“…Lo et al [22] developed a temporal approach for tracking concept drift in each user latent vector. Cheng et al [23] proposed a time aware-based user interest model to improve document recommendation by distinguishing the main interests from the minor user interests. Jiang et al [24] recommended target items to target users by forming user-rated target items at a nearby current time as a group.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Temporal-Concept Drift Model for Sequential Recommendation

Thaipisutikul

¹

2022

ECTI-CIT Transactions

0

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Recently, owing to the great advances in Web 2.0 and mobile devices, various online commercial services have emerged. Recommendation systems play an important role in dealing with abundant product information from massive numbers of online e-commerce transactions. Providing an accurate recommendation at the correct time to customers can contribute to a surge in business success. In this paper, an adaptive temporal-concept drift learning-based recommendation system, ATCRec, is developed for precisely tackling the sequential recommendation problem. We embed sequences of items into the latent spaces and learn both general preferences and sequential patterns concurrently via a recurrent neural network. Specifically, ATCRec captures dynamic changes in the temporal and concept drift contexts by modifying the gate units in a traditional recurrent neural network. The proposed model provides a unified and flexible network structure to learn and reveal the opaque variation of user preferences over time. We evaluate the robustness and performance of ATCRec on two real-world datasets, and the experimental results demonstrate that ATCRec consistently outperforms existing sequential recommendation approaches on various metrics. This indicates that integrating users' temporal information and concept drift variation through time are indispensable in improving the performance of recommendation systems.

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“…Lo et al [22] developed a temporal approach for tracking concept drift in each user latent vector. Cheng et al [23] proposed a time aware-based user interest model to improve document recommendation by distinguishing the main interests from the minor user interests. Jiang et al [24] recommended target items to target users by forming user-rated target items at a nearby current time as a group.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Temporal-Concept Drift Model for Sequential Recommendation

Thaipisutikul

¹

2022

ECTI-CIT Transactions

1

0

View full text Add to dashboard Cite

Recently, owing to the great advances in Web 2.0 and mobile devices, various online commercial services have emerged. Recommendation systems play an important role in dealing with abundant product information from massive numbers of online e-commerce transactions. Providing an accurate recommendation at the correct time to customers can contribute to a surge in business success. In this paper, an adaptive temporal-concept drift learning-based recommendation system, ATCRec, is developed for precisely tackling the sequential recommendation problem. We embed sequences of items into the latent spaces and learn both general preferences and sequential patterns concurrently via a recurrent neural network. Specifically, ATCRec captures dynamic changes in the temporal and concept drift contexts by modifying the gate units in a traditional recurrent neural network. The proposed model provides a unified and flexible network structure to learn and reveal the opaque variation of user preferences over time. We evaluate the robustness and performance of ATCRec on two real-world datasets, and the experimental results demonstrate that ATCRec consistently outperforms existing sequential recommendation approaches on various metrics. This indicates that integrating users' temporal information and concept drift variation through time are indispensable in improving the performance of recommendation systems.

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“…As a result, the quality of the recommendation will be low. For this reason, researchers have proposed many time-weighted CF algorithms, some of which achieved better results than the traditional CF algorithm [7]. However, they did not consider that different users would have different degrees of sensitivity to time.…”

Section: Introductionmentioning

confidence: 99%

Rating Prediction Algorithm Based on User Time-Sensitivity

Cheng

¹

,

Wang

²

2019

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Rating prediction is an important technology in the personalized recommendation field. Prediction results are influenced by many factors, such as time, and their accuracy directly affects the quality of the recommendation. Current time-based collaborative filtering (CF) algorithms have improved the technology of prediction accuracy to a certain extent, but they fail to differentiate the time-sensitivity of different users, which further affects prediction accuracy. To address this issue, we have proposed a rating prediction algorithm based on user time-sensitivity differences. First, we analyzed and modeled the time sensitivities of users, utilized cosine distance and relative entropy to build a judgment function, and then judged the time sensitivities of users based on a voting strategy. Next, we applied the time-sensitivity difference to improve the traditional CF algorithm and optimized the combination of parameters. Finally, we tested our algorithm on standard datasets. The experimental results showed that there are many users who have different sensitivities to time. According to these experimental results, our proposed algorithm has achieved a higher prediction accuracy than other state-of-the-art algorithms.

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Time-Aware and Grey Incidence Theory Based User Interest Modeling for Document Recommendation

Abstract: Document recommendation involves the recommendation of documents similar to those that a user has preferred in the past. The Vector Space Model

Cited by 6 publications

References 25 publications

An Adaptive Temporal-Concept Drift Model for Sequential Recommendation

An Adaptive Temporal-Concept Drift Model for Sequential Recommendation

An Adaptive Temporal-Concept Drift Model for Sequential Recommendation

Rating Prediction Algorithm Based on User Time-Sensitivity

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