scholarly journals Preference Modeling by Rectangular Bilattices

2006 ◽  
pp. 22-33 ◽  
Author(s):  
Ofer Arieli ◽  
Chris Cornelis ◽  
Glad Deschrijver
Keyword(s):  
Preference Modeling

scholarly journals Modeling Imprecise and Bipolar Algebraic and Topological Relations using Morphological Dilations

2021 ◽  
Vol 5 (1) ◽  
pp. 1-20
Author(s):  
Isabelle Bloch
Keyword(s):  
Information Processing ◽  
Fuzzy Sets ◽  
Mathematical Morphology ◽  
Complete Lattice ◽  
Spatial Reasoning ◽  
Topological Relations ◽  
Preference Modeling ◽  
Logical Sense ◽  
Bipolar Fuzzy Sets ◽  
Core Features

Abstract In many domains of information processing, such as knowledge representation, preference modeling, argumentation, multi-criteria decision analysis, spatial reasoning, both vagueness, or imprecision, and bipolarity, encompassing positive and negative parts of information, are core features of the information to be modeled and processed. This led to the development of the concept of bipolar fuzzy sets, and of associated models and tools, such as fusion and aggregation, similarity and distances, mathematical morphology. Here we propose to extend these tools by defining algebraic and topological relations between bipolar fuzzy sets, including intersection, inclusion, adjacency and RCC relations widely used in mereotopology, based on bipolar connectives (in a logical sense) and on mathematical morphology operators. These definitions are shown to have the desired properties and to be consistent with existing definitions on sets and fuzzy sets, while providing an additional bipolar feature. The proposed relations can be used for instance for preference modeling or spatial reasoning. They apply more generally to any type of functions taking values in a poset or a complete lattice, such as L-fuzzy sets.

scholarly journals Bipolar preference modeling and aggregation in decision support

2008 ◽  
Vol 23 (9) ◽  
pp. 970-984 ◽  
Author(s):  
Meltem Öztürk ◽  
Alexis Tsoukiàs
Keyword(s):  
Decision Support ◽  
Preference Modeling

scholarly journals User Diverse Preference Modeling by Multimodal Attentive Metric Learning

2019 ◽  
Author(s):  
Fan Liu ◽  
Zhiyong Cheng ◽  
Changchang Sun ◽  
Yinglong Wang ◽  
Liqiang Nie ◽  
...  
Keyword(s):  
Metric Learning ◽  
Preference Modeling

scholarly journals Personalized Mobile Video Recommendation Based on User Preference Modeling by Deep Features and Social Tags

2019 ◽  
Vol 9 (18) ◽  
pp. 3858
Author(s):  
Jiafeng Li ◽  
Chenhao Li ◽  
Jihong Liu ◽  
Jing Zhang ◽  
Li Zhuo ◽  
...  
Keyword(s):  
Recommendation System ◽  
Moving Average ◽  
Likelihood Estimation ◽  
User Preference ◽  
Personalized Recommendation ◽  
Mobile Video ◽  
Preference Modeling ◽  
Social Tags ◽  
Personalized Video ◽  
Video Recommendation

With the explosive growth of mobile videos, helping users quickly and effectively find mobile videos of interest and further provide personalized recommendation services are the developing trends of mobile video applications. Mobile videos are characterized by their wide variety, single content, and short duration, and thus traditional personalized video recommendation methods cannot produce effective recommendation performance. Therefore, a personalized mobile video recommendation method is proposed based on user preference modeling by deep features and social tags. The main contribution of our work is three-fold: (1) deep features of mobile videos are extracted by an improved exponential linear units-3D convolutional neural network (ELU-3DCNN) for representing video content; (2) user preference is modeled by combining user preference for deep features with user preference for social tags that are respectively modeled by maximum likelihood estimation and exponential moving average method; (3) a personalized mobile video recommendation system based on user preference modeling is built after detecting key frames with a differential evolution optimization algorithm. Experiments on YouTube-8M dataset have shown that our method outperforms state-of-the-art methods in terms of both precision and recall of personalized mobile video recommendation.

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