TAILIEUCHUNG - Collaborative recommenderation based on statistical implication rules

In recent researches, many approaches based on association rules have been proposed to improve the accuracy of recommender systems. These approaches are primarily based on Apriori data mining algorithm in order to generate the association rules and apply them to improving the recommendation results. | Journal of Computer Science and Cybernetics, , (2017), 247-262 DOI: COLLABORATIVE RECOMMENDERATION BASED ON STATISTICAL IMPLICATION RULES NGHIA QUOC PHAN1 , PHUONG HOAI DANG2 , HIEP XUAN HUYNH3 1 Testing Office, Tra Vinh University; nghiatvnt@ Faculty, Da Nang University of Science and Technology 3 College of Information & Communication Technology, Can Tho University 2 IT Abstract. In recent researches, many approaches based on association rules have been proposed to improve the accuracy of recommender systems. These approaches are primarily based on Apriori data mining algorithm in order to generate the association rules and apply them to improving the recommendation results. However, these approaches also reveal some disadvantages of the system, such as taking a longer time for generating association rules; applying the Apriori algorithm on rating sparse matrix resulting in irrelevant information and causing poor recommendation results to target users and association rules generated primarily relying on given threshold of Support and Confidence measures leading to the focus on the majority of rules and ignoring the astonishment of rules to affect the recommendation results. In this study, we propose a new model for collaborative filtering recommender systems: The collaborative recommendation is based on statistical implication rules (IIR); Differently from collaborative recommendation based on association rules (AR), the IIR predicts the items for users based on statistical implication rules generated from rating matrix and Implication intensity measures measuring the surprisingness of rules. To evaluate the effectiveness of the model, in the experimental section, we implement the model on three real datasets and compare the results with some different effective models. The results show that the IIR has higher precision on the experimental datasets. Keywords. Statistical implication rules, association rules, .

• Toán học
• Journal of Computer Science and Cybernetics
• Statistical implication rules
• Collaborative filtering recommender system
• Statistical implicative analysis
• The accuracy of recommender systems
• Nonstandard finite difference schemes
• Solving a modified epidemiological model for computer viruses
• Nonstandard finite difference
• Computer virus propagation model
• Multi objective evolutionary algorithms
• Evolutionary algorithms and foundation
• development and open issues
• EC enables computer scientists
• Evolutionary algorithms designed
• Learning interaction measure
• Relevance feedback in image retrieval
• Multi feature query
• The fuzzy density values
• Quality of transmission aware routing in ad hoc networks
• Cross layer model combined
• The static agent
• Ad hoc networks
• QoT aware routing
• sattic agent
• Algorithm to build fuzzy decision tree for data classification problem
• Fuzziness intervals matching
• Fuzzy decision tree
• The fuzzy data mining problem
• Hedges algebras and fuzzy partition problem for qualitative attributes
• Construct the membership functions
• Fuzzy Association Rules
• Member ship functions
• Characterizations for several classes of alternative codes
• Several classes of alternative codes
• norm alternative code
• Left norm alternative code
• Two sided alternative code
• Left alternative code
• Strict alternative code
• Packet classification algorithm
• Multi way priority trie
• Significant number of researchers
• Both theoretically and experimentally proved
• Solving min max capacitated vehicle routing problem by local search
• Solving min max capacitated vehicle routing problem
• Solving min max capacitated
• Proposed algorithms compared
• Novel control approach for optimal power flow
• Hybrid wind photovoltaic diesel generation systems
• Diesel generation systems
• Analytical control model
• Mobile ad hoc network
• Valid route testing mechanism
• Using Network Simulator
• Random movement network topology
• Rough set theory
• Polynomial time complexity
• Algorithm finding object reducts
• Improved genetic algorithm
• Test data generation for simulink models
• Conventional genetic algorithm
• Generate test data for Simulink models
• Nonholonomic wheeled mobile robot
• Wheeled mobile robot
• Function neural network
• Unknown external disturbances
• Modeling the differential motion of a mobile manipulator
• Designing a new visual servoing
• Tracking a flying target
• Reinforcement learning algorithm
• Efficient navigation framework
• Autonomous mobile robots
• The mobile robots
• Interpolation based on semantic distance weighting
• Hedge algebra and its application
• Semantic distance weighting
• Bi linear interpolation
• Linguistic rule base system
• Transform the current Web
• Conceptual data model
• Ti meER model
• The temporal databases into OWL ontology
• The ER model into ontology
• Sliding mode control
• Sliding mode control for a planar parallel robot driven
• Electric motors in a task space
• The substructure method
• Greedy Randomized Adaptive Search Procedure
• Multiple Traveling Repairmen Problem with Distance Constraints
• Variable Neighborhood Descent
• Traveling Repairman Problem
• On the real world semantics interpretability
• Fuzzy rule based systems under fuzzy set approach
• Hedge algebra approach
• The RWS interpretability
• Alternating current electrical systems
• Integer quadratic programming
• Non linear knapsack problems arising
• Polynomial time approximation schemes
• Picture fuzzy sets
• Picture fuzzy t norms
• Picture fuzzy t conorm
• De Morgan picture operator triple
• Taxonomic assignment for large scale metagenomic data
• High perfomance systems
• Large scale metagenomic data
• Achieve good performance