Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Wangwatcharakul, Charinya; Wongthanavasu, Sartra

doi:10.1109/access.2020.2993289

Cited by 13 publications

(5 citation statements)

References 57 publications

(102 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Addressing the issue of time changing user preferences, the study [17] utilized ant colony pheromones to capture real-time changes in user interests, resulting in improved recommendation accuracy compared to traditional algorithms. In the literature [18], the researchers extracted user latent transition patterns using a joint decomposition method, combining dynamic environment topic modeling with latent factors and relevant textual topics to capture dynamic user preferences in the rating matrix. The study [19] considered future similarity trends, proposing an algorithm to predict similarity trends by rearranging user or item neighborhood sets and updating the final nearest neighbor set of the CF formula based on trend fluctuations to enhance algorithm accuracy.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Filtering Recommender System for Online Learning Resources with Integrated Dynamic Time Weighting and Trust Value Calculation

Guo,

Mohamad Nasir,

2024

TEM Journal

View full text Add to dashboard Cite

Traditional educational models struggle to meet the demands of students seeking personalized online learning resources (OLRs). Collaborative filtering (CF) algorithms are widely employed for personalized OLR recommendations, yet they encounter issues such as poor scalability, cold start, and sparse data issues. In response, an enhanced CF algorithm is proposed, incorporating a fusion of time weighting and a credibility selection strategy. Initially, interactions and ratings among learners are analyzed. Subsequently, the algorithm integrates learner similarity and trust, calculating the credibility value weight between learners. Dynamic time weighting is then introduced separately into CF algorithms based on OLRs and learners, respectively. Ultimately, the algorithm predicts learner ratings for unknown OLRs. Experimental comparisons demonstrate that the performance metrics of the hybrid algorithm presented in this paper show significant improvement over traditional and other improved algorithms. It exhibits enhanced rating prediction accuracy, facilitating precise recommendations of personalized OLRs to learners.

show abstract

Section: Related Workmentioning

confidence: 99%

Collaborative Filtering Recommender System for Online Learning Resources with Integrated Dynamic Time Weighting and Trust Value Calculation

Guo,

Mohamad Nasir,

2024

TEM Journal

View full text Add to dashboard Cite

show abstract

“…First, a unified decomposition technique is applied for capturing the dynamic user preference. This method extracts latent shifts in user patterns and merges these latent elements with the evolving themes found in review texts through dynamic topic modeling [34].…”

Section: E Dynamic User Preference Volume XX 2023mentioning

confidence: 99%

BiLSTCAN: A Novel SRS-Based Bidirectional Long Short-Term Capsule Attention Network for Dynamic User Preference and Next-Item Recommendation

Kannikaklang,

Thamviset,

Wongthanavasu

2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

Numerous research efforts are endeavoring to boost the performance of dynamic user preferences and next-item recommendations, which are pivotal tasks within sequential recommender systems. It is a challenging research problem in recommender systems. However, the majority of research faces notable hurdles, especially the limitation of unidirectional methods, which can solely be executed in the forward direction. In this paper, we propose a novel sequential recommender system-based bidirectional long short-term capsule attention network (BiLSTCAN). It amalgamates novel bidirectional capsule attention network and bidirectional long short-term memory network. BiLSTCAN integrates attention network and incorporates time updates into bidirectional capsule attention network for capturing dynamic user preference of sequential recommendation tasks. A bidirectional architecture is a promising framework that can concurrently capture information from both the forward and backward directions, yielding optimal performance. Additionally, we utilize bidirectional long short-term memory for predicting next-item recommendation. Empirical experiments on five benchmark datasets illustrate that BiLSTCAN surpasses the compared state-of-the-art methods in every Top-N and every density.

show abstract

“…Jin et al proposed a temporal model that captures multiple drifts using deep learning to incorporate user interest or item changes into preference prediction [26]. It has been shown that this approach performs well by tracking preference changes and can achieve better results than recommender systems that do not consider preference changes [27,28]. Among DRSs, TimeSVD++ is an extension of the SVD (Singular Value Decomposition) model that utilizes user and item bias over time to reflect trends in user activity or items [29].…”

Section: Dynamic Recommender Systemsmentioning

confidence: 99%

Deep Learning-Based Context-Aware Recommender System Considering Change in Preference

Jeong

Kim

2023

Electronics

View full text Add to dashboard Cite

In order to predict and recommend what users want, users’ information is required, and more information is required to improve the performance of the recommender system. As IoT devices and smartphones have made it possible to know the user’s context, context-aware recommender systems have emerged to predict preferences by considering the user’s context. A context-aware recommender system uses contextual information such as time, weather, and location to predict preferences. However, a user’s preferences are not always the same in a given context. They may follow trends or make different choices due to changes in their personal environment. Therefore, in this paper, we propose a context-aware recommender system that considers the change in users’ preferences over time. The proposed method is a context-aware recommender system that uses Matrix Factorization with a preference transition matrix to capture and reflect the changes in users’ preferences. To evaluate the performance of the proposed method, we compared the performance with the traditional recommender system, context-aware recommender system, and dynamic recommender system, and confirmed that the performance of the proposed method is better than the existing methods.

show abstract

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Cited by 13 publications

References 57 publications

Collaborative Filtering Recommender System for Online Learning Resources with Integrated Dynamic Time Weighting and Trust Value Calculation

Collaborative Filtering Recommender System for Online Learning Resources with Integrated Dynamic Time Weighting and Trust Value Calculation

BiLSTCAN: A Novel SRS-Based Bidirectional Long Short-Term Capsule Attention Network for Dynamic User Preference and Next-Item Recommendation

Deep Learning-Based Context-Aware Recommender System Considering Change in Preference

Contact Info

Product

Resources

About