A fast combination method in DSmT and its application to recommender system

Dong, Yilin; Li, Xinde; Liu, Yihai

doi:10.1371/journal.pone.0189703

Cited by 2 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, some works have been concentrated towards the belief function theory in RSs. For instance, a new fast combination method, called modified rigid coarsening (MRC) has been introduced in [Dong et al (2018)] based on hierarchical decomposition (coarsening) of the frame of discernment. Another method for combining information about users' preferences based on the belief function theory has been proposed in [Nguyen et al (2017)] to deal with highly conflicting mass functions.…”

Section: Related Workmentioning

confidence: 99%

Towards more trustworthy predictions: A hybrid evidential movie recommender system

Abdelkhalek¹,

Boukhris²,

Elouedi³

2022

jucs

View full text Add to dashboard Cite

Recommender Systems (RSs) are considered as popular tools that have revolutionized the e-commerce and digital marketing. Their main goal is predicting the users’ future preferences and providing accessible and personalized recommendations. However, uncertainty can spread at any level throughout the recommendation process, which may affect the results. In fact, the ratings given by the users are often unreliable. The final provided predictions itself may also be pervaded with uncertainty and doubt. Obviously, the reliability of the predictions cannot be fully certain and trustworthy. For the system to be effective, recommendations must inspire trust in the system and provide reliable and credible recommendations. The user may speculate about the uncertainty pervaded behind the given recommendation. He could tend to a reliable recommendation offering him a global overview about his preferences rather than an inappropriate one that contradicts his activities and objectives. While such imperfection cannot be ignored, traditional RSs are rarely able to deal with the uncertainty spreading around the prediction process, which may affect the credibility, the transparency and the trustworthiness of the current RS. Thus, in this paper, we opt for the uncertain framework of the belief function theory (BFT), which allows us to represent, quantify and manage imperfect evidence. By using the BFT, the users’ preferences and the interactions between the neighbors can be represented under uncertainty. Evidence from different information sources can then be combined leading to more reliable results. The proposed approach is a hybrid evidential movie RS that uses different data sources and delivers a personalized user-interface allowing a global overview of the possible future preferences. This representation would increase the users’ confidence towards the system as well as their satisfaction. Experiments are performed on MovieLens and their additional features provided by the Internet Movie Database (IMDb) and Rotten Tomatoes. The new approach achieves promising results compared to traditional approaches in terms of MAE, NMAE and RMSE. It also reaches interesting Precision, Recall and F-measure values of respectively, 0.782, 0.792 and 0.787.

show abstract

Section: Related Workmentioning

confidence: 99%

Towards more trustworthy predictions: A hybrid evidential movie recommender system

Abdelkhalek¹,

Boukhris²,

Elouedi³

2022

jucs

View full text Add to dashboard Cite

show abstract

Dempster-Shafer Theory in Recommender Systems: A Survey

Belmessous,

Sebbak,

Mataoui

et al. 2024

Int. J. Unc. Fuzz. Knowl. Based Syst.

View full text Add to dashboard Cite

Due to the limitations associated with the use of a single type of data during the recommendation process, recent research has focused on developing new fusion-based recommenders that make use of multiple heterogeneous sources of information to provide more accurate suggestions. However, the realistic and flexible methods available to users for expressing their preferences for products and services inherently generate uncertain, imperfect, and ambiguous data that feed recommenders and thus affect their accuracy. As a result, Recommender Systems (RS) make significant use of soft mathematical tools to deal with uncertainty. Among these tools is Dempster-Shafer Theory (DST), which has been shown to be effective at dealing with the inherent uncertainty in numerous applications. This article provides a survey of the use of DST in the RS field. Thus, after a brief introduction to recommender systems and the DST, this survey discusses recent DST applications in RS. It introduces a new taxonomy that encompasses the primary application context for DST-based RS solutions, as well as a comprehensive multi-criteria analysis of the peer-reviewed papers. The resulting comparisons are analyzed to draw conclusions, identify current study limitations, and define future research directions. This survey serves as a valuable resource for the entire research community that is interested in recommender systems and DST.

show abstract

A fast combination method in DSmT and its application to recommender system

Cited by 2 publications

References 31 publications

Towards more trustworthy predictions: A hybrid evidential movie recommender system

Towards more trustworthy predictions: A hybrid evidential movie recommender system

Dempster-Shafer Theory in Recommender Systems: A Survey

Contact Info

Product

Resources

About