Online optimization for user-specific hybrid recommender systems

Dooms, Simon; Pessemier, Toon De; Martens, Luc

doi:10.1007/s11042-014-2232-7

Cited by 9 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 7: This process continues until each and every one Users data points (users) are marked as visited. The important parameters are ε and minPts which needs to be resolved by using Anarchic Society Optimization (ASO) algorithm [18]. The clustering accuracy of every part is calculated depending on the two parameters ε and minPts.…”

Section: Algorithmmentioning

confidence: 99%

Improvised DBSC and transductive SVM for hybrid recommendation systems

Anima¹,

Kumar²

2022

ijhs

View full text Add to dashboard Cite

Recommendation Systems play a crucial role in improving the business strategies and providing the customers or users with the choice to opt for a product or service. Given the growth and power of information analytics tools and inclination of data mining techniques, a wide range of recommender systems particularly for the web based users have been evolved over the decade. The conventional “One model – Fit for all” model fails in most of the cases due to the common fact that not all users have same interests and have paved way for tailor made recommender systems. It is hence highly important to provide a personalized framework which can accumulate specific interests of the users. Web data are normally highly sparse in nature and formulating a personalized user profile was a difficult task. The previous methods used user based CF method to address the former issue while Extended Jaccard Similarity (EJS) was introduced to address the latter. Although these methods found to be effective, there were scopes for improvement in the Query processing time and accuracy. To solve this problem the proposed system design an Enhanced DBSCAN and TSVM based hybrid recommendation scheme.

show abstract

Section: Algorithmmentioning

confidence: 99%

Improvised DBSC and transductive SVM for hybrid recommendation systems

Anima¹,

Kumar²

2022

ijhs

View full text Add to dashboard Cite

show abstract

“…The CF recommendation algorithm that relies on the scoring matrix as the original input data tends to have a negative impact on the prediction accuracy of the algorithm due to the excessive sparsity of the scoring matrix. In order to overcome the data sparsity problem of a single CF recommendation algorithm, researchers have proposed many hybrid recommendation algorithms [10][11] [12]. Hybrid recommendation algorithm is a method of combining several recommendation algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning

Duan¹,

Wang²,

Han³

2021

KSII TIIS

View full text Add to dashboard Cite

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the coldstart problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

show abstract

Improving the effectiveness of personalized recommendations through attributional cues

Weidig,

Kuehnl

2023

Psychology and Marketing

View full text Add to dashboard Cite

Firms often employ personalized recommendations to help customers make purchase decisions. To improve the effectiveness of their personalized recommendations, some firms use cues to offer transparency on how they collect and use data to derive recommendations. We draw on attribution theory to propose an additional mechanism to improve the effectiveness of personalized recommendations with cues. Attributional cues, which refer to the underlying data (i.e., customers' own data vs. similar customers' data) used for personalized recommendations, aim to increase customers' self‐attribution of personalized recommendations. Specifically, in three experimental studies, we show that attributional cues increase customers' self‐attribution of personalized recommendations, leading to higher trust in and lower reactance to personalized recommendations. The accuracy and valence of the personalized recommendations moderate this attributional effect. As a result, employing attributional cues can be an essential and affordable tool for firms to increase the effectiveness of their personalized recommendations.

show abstract

Online optimization for user-specific hybrid recommender systems

Cited by 9 publications

References 35 publications

Improvised DBSC and transductive SVM for hybrid recommendation systems

Improvised DBSC and transductive SVM for hybrid recommendation systems

A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning

Improving the effectiveness of personalized recommendations through attributional cues

Contact Info

Product

Resources

About