scholarly journals Hybrid crow search and uniform crossover algorithm-based clustering for top-N recommendation system

2020 ◽  
Author(s):  
Walaa H. El-Ashmawi ◽  
Ahmed F. Ali ◽  
Adam Slowik
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Information Filtering ◽  
Absolute Error ◽  
African Buffalo ◽  
Solution Quality ◽  
Uniform Crossover ◽  
Standard Particle Swarm Optimization

AbstractRecommender systems (RSs) have gained immense popularity due to their capability of dealing with a huge amount of information available in various domains. They are considered to be information filtering systems that make predictions or recommendations to users based on their interests. One of the most common recommender system techniques is user-based collaborative filtering. In this paper, we follow this technique by proposing a new algorithm which is called hybrid crow search and uniform crossover algorithm (HCSUC) to find a set of feasible clusters of similar users to enhance the recommendation process. Invoking the genetic uniform crossover operator in the standard crow search algorithm can increase the diversity of the search and help the algorithm to escape from trapping in local minima. The top-N recommendations are presented for the corresponding user according to the most feasible cluster’s members. The performance of the HCSUC algorithm is evaluated using the Jester dataset. A set of experiments have been conducted to validate the solution quality and accuracy of the HCSUC algorithm against the standard particle swarm optimization (PSO), African buffalo optimization (ABO), and the crow search algorithm (CSA). In addition, the proposed algorithm and the other meta-heuristic algorithms are compared against the collaborative filtering recommendation technique (CF). The results indicate that the HCSUC algorithm has obtained superior results in terms of mean absolute error, root means square errors and in minimization of the objective function.

scholarly journals Building A Movie Recommendation System Using Collaborative Filtering With TF-IDF

2021 ◽  
Vol 9 (9) ◽  
pp. 181-191
Author(s):  
S. A. Azeem Farhan
Keyword(s):  
Collaborative Filtering ◽  
Language Processing ◽  
Recommender System ◽  
Recommendation System ◽  
Mean Squared Error ◽  
Information Filtering ◽  
Recommendation Systems ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Value Decomposition

Abstract: The recommendation problem involves the prediction of a set of items that maximize the utility for users. As a solution to this problem, a recommender system is an information filtering system that seeks to predict the rating given by a user to an item. There are theree types of recommendation systesms namely Content based, Collaborative based and the Hybrid based Recommendation systems. The collaborative filtering is further classified into the user based collaborative filtering and item based collaborative filtering. The collaborative filtering (CF) based recommendation systems are capable of grasping the interaction or correlation of users and items under consideration. We have explored most of the existing collaborative filteringbased research on a popular TMDB movie dataset. We found out that some key features were being ignored by most of the previous researches. Our work has given significant importance to 'movie overviews' available in the dataset. We experimented with typical statistical methods like TF-IDF , By using tf-idf the dimensions of our courps(overview and other text features) explodes, which creates problems ,we have tackled those problems using a dimensionality reduction technique named Singular Value Decomposition(SVD). After this preprocessing the Preprocessed data is being used in building the models. We have evaluated the performance of different machine learning algorithms like Random Forest and deep neural networks based BiLSTM. The experiment results provide a reliable model in terms of MAE(mean absolute error) ,RMSE(Root mean squared error) and the Bi-LSTM turns out to be a better model with an MAE of 0.65 and RMSE of 1.04 ,it generates more personalized movie recommendations compared to other models. Keywords: Recommender system, item-based collaborative filtering, Natural Language Processing, Deep learning.

A review of a recommendation filtering system approach based on reliable sustainable opinion mining

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imen Gmach ◽  
Nadia Abaoub ◽  
Rubina Khan ◽  
Naoufel Mahfoudh ◽  
Amira Kaddour
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Search ◽  
Recommendation System ◽  
Opinion Mining ◽  
Information Filtering ◽  
Recommendation Systems ◽  
Content Filtering ◽  
Content Type ◽  
The Impact

PurposeIn this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.Design/methodology/approachMethodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.FindingsThe purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.Originality/valueThe authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.

Research on Location-Based Personalized Recommendation System

2014 ◽  
Vol 490-491 ◽  
pp. 1493-1496
Author(s):  
Huan Gao ◽  
Xi Tian ◽  
Xiang Ling Fu
Keyword(s):  
Collaborative Filtering ◽  
Real Time ◽  
Recommendation System ◽  
Information Overload ◽  
Information Filtering ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
New System

With the mobile Internet developing in China, the problem of information overload has been brought to us. The traditional personalized recommendation cannot meet the needs of the mobile Internet. In this paper, the recommendation algorithm is mainly based on the collaborative filtering, but the new factors are introduced into the recommendation system. The new system takes the user's location and friends recommendation into the personalized recommendation system so that the recommendation system can meet the mobile Internet requirements. Besides, this paper also puts forward the concept of moving business circle for information filtering, which realizes the precise and real-time personalized recommendations. This paper also proves the recommendation effects through collecting and analyzing the data, which comes from the website of dianping.com.

scholarly journals Solving Cell Placement Problem Using Harmony Search Algorithms

2018 ◽  
Vol 8 (4) ◽  
pp. 3172-3176
Author(s):  
R. M. Al Qasem ◽  
S. M. Massadeh
Keyword(s):  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Placement Problem ◽  
Solution Quality ◽  
Chip Design ◽  
Cell Placement ◽  
Placement Algorithm

Cell placement is a phase in the chip design process, in which cells are assigned to physical locations. A placement algorithm is a way that satisfies the objectives and minimizes the total area while keeping enough space for routing. Cell placement is an NP-complete problem of very large size. In order to solve this problem, diversified heuristic algorithms are used. In this work, a new algorithm is proposed based on the harmony search algorithm. The harmony search algorithm mimics music improvisation process to find the optimal solution. Cell placement problem has many constraints, so in this work, the harmony search algorithm is modified to adapt to these constraints. Experiment results show that this algorithm is efficient for solving cell placement and is characterized by good performance, solution quality and likelihood of optimality.

Algorithms for Reconfiguring NoC-Based Fault-Tolerant Multiprocessor Arrays

2019 ◽  
Vol 28 (07) ◽  
pp. 1950111
Author(s):  
Jigang Wu ◽  
Yalan Wu ◽  
Guiyuan Jiang ◽  
Siew Kei Lam
Keyword(s):  
Heuristic Algorithms ◽  
Fault Tolerant ◽  
Search Algorithm ◽  
Fixed Number ◽  
Processor Array ◽  
Solution Quality ◽  
Processing Elements ◽  
Original Size ◽  
Target Array ◽  
Initial Target

This paper investigates the techniques to construct high-quality target processor array (fault-free logical subarray) from a physical array with faulty processing elements (PEs), where a fixed number of spare PEs are pre-integrated that can be used to replace the faulty ones when necessary. A reconfiguration algorithm is successfully developed based on our proposed novel shifting operations that can efficiently select proper spare PEs to replace the faulty ones. Then, the initial target array is further refined by a carefully designed tabu search algorithm. We also consider the problem of constructing a fault-free subarray with given size, instead of the original size, which is often required in energy-efficient MPSoC design. We propose two efficient heuristic algorithms to construct target arrays of given sizes leveraging a sliding window on the physical array. Simulation results show that the improvements of the proposed algorithms over the state of the art are [Formula: see text] and [Formula: see text], in terms of congestion factor and distance factor, respectively, for the case that all faulty PEs can be replaced using the spare ones. For the case of finding [Formula: see text] target array on [Formula: see text] host array, the proposed heuristic algorithm saves the running time up to [Formula: see text] while the solution quality keeps nearly unchanged, in comparison with the baseline algorithms.

Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model

2021 ◽  
pp. 1-22
Author(s):  
Celestine Iwendi ◽  
Ebuka Ibeke ◽  
Harshini Eggoni ◽  
Sreerajavenkatareddy Velagala ◽  
Gautam Srivastava
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Absolute Error ◽  
Learning Model ◽  
Text Recall ◽  
Machine Learning Model ◽  
Similarity Algorithm ◽  
Recommendation Accuracy ◽  
Better Than

The creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item–item collaborative filtering. Presently, item recommendation is based completely on ratings like 1–5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users’ reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with [Formula: see text] accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of [Formula: see text], precision at [Formula: see text], recall at [Formula: see text] and F1-Score at [Formula: see text]. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy.

scholarly journals Whale Optimization based Recommendation System

2019 ◽  
Vol 8 (10) ◽  
pp. 2640-2644
Keyword(s):  
Performance Analysis ◽  
Em Algorithm ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Information Filtering ◽  
Optimization Technique ◽  
Current User ◽  
Active User ◽  
Whale Optimization ◽  
Rating Prediction

Recommendation System is an information filtering system which seeks to predict the “liking” of a user for an item, with the aim to suggest the user those items which he/she is most likely to select/buy. The focus of this paper is on rating prediction whose main objective is to predict the ratings the current user is going to give to the items which are yet to be rated/viewed by him/her. This paper uses a collaborative filtering based approach for generating recommendation, and the model used is a clustering-based model. In this approach all the existing users are clustered using whale optimization technique, instead of traditional clustering approaches like k-means, EM algorithm, etc. The appropriate cluster is then identified for the active user, and the ratings of the active user are predicted based on ratings given by other users belonging to the same cluster. Different measures like MAE, SD, RMSE and t-value are used for performance analysis of the proposed method and the results obtained are found to be highly accurate

scholarly journals Discretization Based Framework to Improve the Recommendation Quality

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Bilal Ahmed ◽  
Wang Li
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Information Filtering ◽  
Nearest Neighbors ◽  
User Preferences ◽  
Primary Concern ◽  
K Nearest Neighbors ◽  
Chi Square ◽  
Similarity Computation ◽  
Rating Prediction

Recommendation systems are information filtering software that delivers suggestions about relevant stuff from a massive collection of data. Collaborative filtering approaches are the most popular in recommendations. The primary concern of any recommender system is to provide favorable recommendations based on the rating prediction of user preferences. In this article, we propose a novel discretization based framework for collaborative filtering to improve rating prediction. Our framework includes discretization-based preprocessing, chi-square based attribution selection, and K-Nearest Neighbors (KNN) based similarity computation. Rating prediction affords some basis for the judgment to decide whether recommendations are generated or not, subject to the ratio of performance of any recommendation system. Experiments on two datasets MovieLens and BookCrossing, demonstrate the effectiveness of our method.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

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