A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution

Wangwatcharakul, Charinya; Wongthanavasu, Sartra

doi:10.1016/j.eswa.2021.115626

Cited by 10 publications

(7 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Methods based on other models have also been applied to solve the concept drift problem of recommendation systems. For example, Viniski et al [24] proposed incremental learning to update the user-item relationship established in a streaming recommendation system; multitransition factor and a forgetting time function were introduced to analyze the evolution of user preferences in order to accurately recommend new items or services to users [25].…”

Section: Concept Drift In Recommendation Systemmentioning

confidence: 99%

Ensemble Learning for Interpretable Concept Drift and Its Application to Drug Recommendation

Peng

Qiu

Zhang

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

During the COVID-19 epidemic, the online prescription pattern of Internet healthcare provides guarantee for the patients with chronic diseases and reduces the risk of cross-infection, but it also raises the burden of decision-making for doctors. Online drug recommendation system can effectively assist doctors by analysing the electronic medical records (EMR) of patients. Unlike commercial recommendations, the accuracy of drug recommendations should be very high due to their relevance to patient health. Besides, concept drift may occur in the drug treatment data streams, handling drift and location drift causes is critical to the accuracy and reliability of the recommended results. This paper proposes a multi-model fusion online drug recommendation system based on the association of drug and pathological features with online-nearline-offline architecture. The system transforms drug recommendation into pattern classification and adopts interpretable concept drift detection and adaptive ensemble classification algorithms. We apply the system to the Percutaneous Coronary Intervention (PCI) treatment process. The experiment results show our system performs nearly as good as doctors, the accuracy is close to 100%

show abstract

Section: Concept Drift In Recommendation Systemmentioning

confidence: 99%

Ensemble Learning for Interpretable Concept Drift and Its Application to Drug Recommendation

Peng

Qiu

Zhang

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

show abstract

“…This combination is performed by following a wide range of methods which are outlined in the papers mentioned, but the most usual way is to apply a latent topic discovery algorithm to the available text collections, obtain the topics associated to each document and incorporate time by means of weights associated to topics. Other authors use decay functions (Wangwatcharakul and Wongthanavasu 2021) to mitigate the impact of old ratings. They also use item reviews in order to obtain the underlying topics in the collection and associate the rated items to the corresponding topics in the reviews in order to track how the topics evolve with time.…”

Section: Related Workmentioning

confidence: 99%

Use of topical and temporal profiles and their hybridisation for content-based recommendation

Campos

Fernández‐Luna

Huete

2023

User Model User-Adap Inter

View full text Add to dashboard Cite

In the context of content-based recommender systems, the aim of this paper is to determine how better profiles can be built and how these affect the recommendation process based on the incorporation of temporality, i.e. the inclusion of time in the recommendation process, and topicality, i.e. the representation of texts associated with users and items using topics and their combination. To that end, we build both topically and temporally homogeneous subprofiles to represent items. The main contribution of the paper is to present two different ways of hybridising these two dimensions and to evaluate and compare them with other alternatives. Our proposals and experiments are carried out in the specific context of publication venue recommendation.

show abstract

“…Because collaborative filtering (CF) is one of the most commonly used methods for recommendations, traditional CF methods usually cannot track temporary dynamic user preferences and subject changes to provide appropriate recommendations in relation to changing user interests over time. For this, Wangwatcharakul and Wongthanavasu [40] proposed a novel temporal recommender system based on multiple transitions in user preference drift, called MTUPD, VOLUME 11, 2023 which employs a multitransition factor and an adaptive time weight using the forgetting curve function to compute user preference correlations at different time periods. In addition, Wangwatcharakul and Wongthanavasu [40] applied a topic model that automatically classified hidden topic factors in each time period and incorporated the transition method for both user preferences and relevant review topics to address the sparsity problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For this, Wangwatcharakul and Wongthanavasu [40] proposed a novel temporal recommender system based on multiple transitions in user preference drift, called MTUPD, VOLUME 11, 2023 which employs a multitransition factor and an adaptive time weight using the forgetting curve function to compute user preference correlations at different time periods. In addition, Wangwatcharakul and Wongthanavasu [40] applied a topic model that automatically classified hidden topic factors in each time period and incorporated the transition method for both user preferences and relevant review topics to address the sparsity problem. User reviews have been exploited as an auxiliary information source to discover hidden topic evolutions that can describe why the user gives a certain rating by using topic modeling techniques at different time steps.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Time-Aware Recommender Systems: A Comprehensive Survey and Quantitative Assessment of Literature

2023

View full text Add to dashboard Cite

Recommender systems (RS) are among the most widely used applications in data mining and machine-learning technologies. These technologies recommend relevant products to customers, such as movies to watch, items to buy, and books to read. The difference in user preferences over time is one of the most significant issues faced by recommender systems. Researchers have focused on time-sensitive recommender systems, and numerous studies have been conducted in this field. These studies aim to consider the time factor while offering recommendations to users by incorporating and utilizing temporal data in recommendations. In this work, we review existing works in this field and present the most prominent techniques and algorithms that have the ability to capture changes in user preferences over time, and the most important application areas for these recommendations. In addition, we present a quantitative assessment of comprehensive literature that investigates publications in terms of publication time, publication type, and datasets used. Finally, we highlight a range of findings and conclusions and provide the reader with insights based on a general analysis of time-sensitive recommender systems.INDEX TERMS Recommender systems (RSs), time sensitive recommender system, context-aware recommender systems, time aware, artificial intelligence (AI), machine learning, deep learning.

show abstract

A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution

Cited by 10 publications

References 29 publications

Ensemble Learning for Interpretable Concept Drift and Its Application to Drug Recommendation

Ensemble Learning for Interpretable Concept Drift and Its Application to Drug Recommendation

Use of topical and temporal profiles and their hybridisation for content-based recommendation

Time-Aware Recommender Systems: A Comprehensive Survey and Quantitative Assessment of Literature

Contact Info

Product

Resources

About