Nor Haniza Sarmin

A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA-ANFIS

Display Omitted A multi-criteria CF recommender system in tourism domain is proposed.Predictive accuracy of multi-criteria CF recommender systems is improved.EM algorithm, ANFIS and PCA are applied in the proposed method.PCA is applied for solving multi-collinearity problem.ANFIS is applied for developing the prediction models. In order to improve the tourist experience, recommender systems are used to offer personalized information for online users. The hotel industry is a leading stakeholder in the tourism sector, which needs to provide online facilities to their customers. Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a users needs based on data coming from similar users, are becoming increasingly popular as ways to combat information overload. They use a single rating as input. However, the multi-criteria based CF presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects and they can be an appropriate choice for the tourist. In this paper, we propose a new hybrid method for hotel recommendation using dimensionality reduction and prediction techniques. Accordingly, we have developed the multi-criteria CF recommender systems for hotel recommendation to enhance the predictive accuracy by using Gaussian mixture model with Expectation Maximization (EM) algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS). We have also used the Principal Component Analysis (PCA) for dimensionality reduction and to address multi-collinearity induced from the interdependencies among criteria in multi-criteria CF dataset. Our experiments confirmed that the proposed hybrid method achieved high accuracy for hotel recommendation for the tourism sector.

Two-generator two-groups of class two and their nonabelian tensor squares

The nonabelian tensor square G[otimes ] G of a group G is generated by the symbols g[otimes ] h, g,h ∈ G, subject to the relations

Generalized conjugacy class graph of some finite non-abelian groups

gg\prime\otimesh=(^gg\prime\otimes^gh)(g\otimesh) and g\otimeshh\prime-(g\otimesh)(^hg\otimes^hh\prime),

An Extension of First Order Limit Language

for all

An Extension of DNA Splicing System

g,g\prime,h,h\prime \in G , where

The Schur Multiplier of Pairs of Groups of Order

^gg\prime=gg\primeg^{−1}

An extended partitioning technique to transform trees into single-row networks

. The nonabelian tensor square is a special case of the nonabelian tensor product which has its origins in homotopy theory. It was introduced by R. Brown and J.-L. Loday in [ 4 ] and [ 5 ], extending ideas of J.H.C. Whitehead in [ 10 ]. The topic of this paper is the classification of 2-generator 2-groups of class two up to isomorphism and the determination of nonabelian tensor squares for these groups.

Relative commutativity degree of some dihedral groups

Channel assignments in a wireless cellular network is a discrete-time event process involving the correct mapping of the channels to the caller-receiver pairs. The channels are assigned in such a way that they are constantly reused once a particular assignment is completed. In this work, a dynamic single-row routing technique is proposed to handle channel assignments for wireless cellular networks. The dynamic technique refers to the constantly changing shape of the single-row network over time as a result of its transformation from its base graph. The base graph is formed from the caller-receiver pairs in the cellular network whose shape is determined from factors such as their cellular location and duration of calls. This graph is transformed into a single-row network where the pins in the network become the channels. The transformation process is conducted dynamically over time to produce a dynamic single-row network as part of the circuitry in the multi-switching center.