Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.

Hybrid Recommender System for Tourism Based on Big Data and AI:A Conceptual Framework

With the development of the Internet,technology,and means of communication,the production of tourist data has multiplied at all levels(hotels,restaurants,transport,heritage,tourist events,activities,etc.),especially with the development of Online Travel Agency(OTA).However,the list of possibilities offered to tourists by these Web search engines(or even specialized tourist sites)can be overwhelming and relevant results are usually drowned in informational"noise",which prevents,or at least slows down the selection process.To assist tourists in trip planning and help them to find the information they are looking for,many recommender systems have been developed.In this article,we present an overview of the various recommendation approaches used in the field of tourism.From this study,an architecture and a conceptual framework for tourism recommender system are proposed,based on a hybrid recommendation approach.The proposed system goes beyond the recommendation of a list of tourist attractions,tailored to tourist preferences.It can be seen as a trip planner that designs a detailed program,including heterogeneous tourism resources,for a specific visit duration.The ultimate goal is to develop a recommender system based on big data technologies,artificial intelligence,and operational research to promote tourism in Morocco,specifically in the Daraa-Tafilalet region.

An Ensemble Learning Recommender System for Interactive Platforms

In interactive platforms, we often want to predict which items could be more relevant for users, either based on their previous interactions with the system or their preferences. Such systems are called Recommender Systems. They are divided into three main groups, including content-based, collaborative and hybrid recommenders. In this paper, we focus on collaborative filtering and the improvement of the accuracy of its techniques. Then, we suggest an Ensemble Learning Recommender System model made of a probabilistic model and an efficient matrix factorization method. The interactions between users and the platform are scored by explicit and implicit scores. At each user session, implicit scores are used to train a probabilistic model to compute the maximum likelihood estimator for the probability that an item will be recommended in the next session. The explicit scores are used to know the impact of the user’s vote on an item at the time of the recommendation.

Hybrid Recommendation Based on Graph Embedding

In recent years,online reservation systems of country hotel have become increasingly popular in rural areas.How to accurately recommend the houses of country hotel to the users is an urgent problem to be solved.Aiming at the problem of cold start and data sparseness in recommendation,a Hybrid Recommendation method based on Graph Embedding(HRGE)is proposed.First,three types of network are built,including user-user network based on user tag,househouse network based on house tag,and user-user network based on user behavior.Then,by using the method of graph embedding,three types of network are respectively embedded into low-dimensional vectors to obtain the characterization vectors of nodes.Finally,these characterization vectors are used to make a hybrid recommendation.The datasets in this paper are derived from the Country Hotel Reservation System in Guizhou Province.The experimental results show that,compared with traditional recommendation algorithms,the comprehensive evaluation index(F1)of the HRGE is improved by 20% and the Mean Average Precision(MAP)is increased by 11%.

Design of Hybrid Recommendation Algorithm in Online Shopping System

In order to improve user satisfaction and loyalty on e-commerce websites,recommendation algorithms are used to recommend products that may be of interest to users.Therefore,the accuracy of the recommendation algorithm is a primary issue.So far,there are three mainstream recommendation algorithms,content-based recommendation algorithms,collaborative filtering algorithms and hybrid recommendation algorithms.Content-based recommendation algorithms and collaborative filtering algorithms have their own shortcomings.The content-based recommendation algorithm has the problem of the diversity of recommended items,while the collaborative filtering algorithm has the problem of data sparsity and scalability.On the basis of these two algorithms,the hybrid recommendation algorithm learns from each other’s strengths and combines the advantages of the two algorithms to provide people with better services.This article will focus on the use of a content-based recommendation algorithm to mine the user’s existing interests,and then combine the collaborative filtering algorithm to establish a potential interest model,mix the existing and potential interests,and calculate with the candidate search content set.The similarity gets the recommendation list.

Bayesian dual neural networks for recommendation

Most traditional collaborative filtering(CF)methods only use the user-item rating matrix to make recommendations,which usually suffer from cold-start and sparsity problems.To address these problems,on the one hand,some CF methods are proposed to incorporate auxiliary information such as user/item profiles;on the other hand,deep neural networks,which have powerful ability in learning effective representations,have achieved great success in recommender systems.However,these neural network based recommendation methods rarely consider the uncertainty of weights in the network and only obtain point estimates of the weights.Therefore,they maybe lack of calibrated probabilistic predictions and make overly confident decisions.To this end,we propose a new Bayesian dual neural network framework,named BDNet,to incorporate auxiliary information for recommendation.Specifically,we design two neural networks,one is to learn a common low dimensional space for users and items from the rating matrix,and another one is to project the attributes of users and items into another shared latent space.After that,the outputs of these two neural networks are combined to produce the final prediction.Furthermore,we introduce the uncertainty to all weights which are represented by probability distributions in our neural networks to make calibrated probabilistic predictions.Extensive experiments on real-world data sets are conducted to demonstrate the superiority of our model over various kinds of competitors.

Recommendation Algorithm Based on Improved Convolutional Neural Network and Matrix Factorization

The traditional collaborative filtering algorithm uses the user rating information as a recommendation basis,but the ratings matrices are usually sparse and cannot reflect users’preference exactly,so the recommendation results are not very accurate.Therefore,this paper proposes an improved convolutional neural network for collaborative filtering(CNNCF),using the deep learning model to deeply mine the hidden feature information.then implicit the semantic model,Then the extracted explicit feature information was replaced by the implicit feature information in the LFM to further improve the prediction accuracy,and finally personalized recommendation through the user-item preference matrix.Experimental results on the MovieLens dataset show that the model can overcome data sparse,and recommendation accuracy is better than the traditional collaborative filtering model.