A Semantic Approach to Recommendation System Based on User Ontology and Spreading Activation Model

2008 ◽  
Author(s):  
Qi Gao ◽  
Junwei Yan ◽  
Min Liu
Keyword(s):  
Recommendation System ◽  
Spreading Activation ◽  
Activation Model ◽  
Semantic Approach

scholarly journals AN ONTOLOGY-BASED TOURISM RECOMMENDER SYSTEM BASED ON SPREADING ACTIVATION MODEL

2015 ◽  
Vol XL-1-W5 ◽  
pp. 83-90 ◽  
Author(s):  
Z. Bahramian ◽  
R. Ali Abbaspour
Keyword(s):  
Recommender System ◽  
Recommendation System ◽  
Information Overload ◽  
User Profile ◽  
Spreading Activation ◽  
Activation Model ◽  
Points Of Interest ◽  
Overall Performance ◽  
Available Information ◽  
Degree Of Similarity

A tourist has time and budget limitations; hence, he needs to select points of interest (POIs) optimally. Since the available information about POIs is overloading, it is difficult for a tourist to select the most appreciate ones considering preferences. In this paper, a new travel recommender system is proposed to overcome information overload problem. A recommender system (RS) evaluates the overwhelming number of POIs and provides personalized recommendations to users based on their preferences. A content-based recommendation system is proposed, which uses the information about the user’s preferences and POIs and calculates a degree of similarity between them. It selects POIs, which have highest similarity with the user’s preferences. The proposed content-based recommender system is enhanced using the ontological information about tourism domain to represent both the user profile and the recommendable POIs. The proposed ontology-based recommendation process is performed in three steps including: ontology-based content analyzer, ontology-based profile learner, and ontology-based filtering component. User’s feedback adapts the user’s preferences using Spreading Activation (SA) strategy. It shows the proposed recommender system is effective and improves the overall performance of the traditional content-based recommender systems.

scholarly journals Creativity Is Optimal Novelty and Maximal Positive Affect: A New Definition Based on the Spreading Activation Model

2021 ◽  
Vol 15 ◽  
Author(s):  
Emery Schubert
Keyword(s):  
Boundary Conditions ◽  
Positive Affect ◽  
Temporal Contiguity ◽  
Spreading Activation ◽  
Creative Work ◽  
Interconnected Network ◽  
Activation Model ◽  
Early Evidence ◽  
Definition Of ◽  
Total Activation

Creativity is commonly defined as a process that leads to a novel and useful outcome (an idea, product, or expression). However, two dilemmas about this definition remain unresolved: (1) A strict application of usefulness is difficult to apply to artistic works: who decides what artwork is useful, and how it is useful? (2) The implied boundary conditions of novelty are problematic: The default perspective is that novelty has a monotonic increasing relationship with creativity, or it is categorical—i.e., novel or not. To address these dilemmas, this paper proposes a spreading activation model of creativity (SAMOC), a model built on a brain-architecture-inspired vast interconnected network of nodes, each node representing information, and assigned meanings through interaction with the environment. Nodes are linked to each other according to principles of temporal contiguity (linking objects/events in time) and similarity (linking objects/events by shared features). A node activated by attention spreads through the network through previously linked nodes. Nodes that are well connected activate each other easily, while those that are weakly connected do not. Net total activation corresponds to positive affect (e.g., pleasure), and this is proposed as an essential criteria for a creative work of art, instead of usefulness. SAMOC also predicts that creativity will be optimized at an intermediate, not extreme, level of novelty. Too much activation will occur with the activation of preexisting ideas (hence reproduction rather than creativity), and too much novelty will not produce spread of activation. The two functions (spreading activation and the novelty curve) are superposed to demonstrate this optimal novelty hypothesis. Early evidence of the hypothesis comes from the data that some great works of art were critically rejected at premiers (suggesting excessive novelty), but after sufficient repetition (and therefore linking) became suitably associated and commenced generating activation. The hypothesis has important implications for future empirical research programs on creativity, and for the definition of creativity itself.

Clustering algorithm for Ad Hoc network based on spreading activation model

2009 ◽  
Vol 29 (1) ◽  
pp. 155-157
Author(s):  
Tao HAN ◽  
Jin-min YANG ◽  
Da-fang ZHANG ◽  
Yin LI
Keyword(s):  
Ad Hoc Network ◽  
Clustering Algorithm ◽  
Ad Hoc ◽  
Spreading Activation ◽  
Activation Model

scholarly journals Systemic States of Spreading Activation in Describing Associative Knowledge Networks II: Generalisations with Fractional Graph Laplacians and q-Adjacency Kernels

Systems ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 22
Author(s):  
Ismo T. Koponen
Keyword(s):  
Random Walk ◽  
Diffusion Process ◽  
Graph Laplacian ◽  
Knowledge Networks ◽  
Jump Processes ◽  
Knowledge Network ◽  
Spreading Activation ◽  
Activation Model ◽  
Graph Laplacians ◽  
Generalized Matrix

Associative knowledge networks are often explored by using the so-called spreading activation model to find their key items and their rankings. The spreading activation model is based on the idea of diffusion- or random walk -like spreading of activation in the network. Here, we propose a generalisation, which relaxes an assumption of simple Brownian-like random walk (or equally, ordinary diffusion process) and takes into account nonlocal jump processes, typical for superdiffusive processes, by using fractional graph Laplacian. In addition, the model allows a nonlinearity of the diffusion process. These generalizations provide a dynamic equation that is analogous to fractional porous medium diffusion equation in a continuum case. A solution of the generalized equation is obtained in the form of a recently proposed q-generalized matrix transformation, the so-called q-adjacency kernel, which can be adopted as a systemic state describing spreading activation. Based on the systemic state, a new centrality measure called activity centrality is introduced for ranking the importance of items (nodes) in spreading activation. To demonstrate the viability of analysis based on systemic states, we use empirical data from a recently reported case of a university students’ associative knowledge network about the history of science. It is shown that, while a choice of model does not alter rankings of the items with the highest rank, rankings of nodes with lower ranks depend essentially on the diffusion model.

Category Activation Model: A Spreading Activation Network Model of Subcategory Positioning When Categorization Uncertainty Is High

2009 ◽  
Vol 36 (1) ◽  
pp. 122-136 ◽  
Author(s):  
Joseph Lajos ◽  
Zsolt Katona ◽  
Amitava Chattopadhyay ◽  
Miklos Sarvary
Keyword(s):  
Network Model ◽  
Spreading Activation ◽  
Activation Model
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