RECOMMENDER SYSTEMS: PREDICTING WITH MACHINE LEARNING BASED ON POPULATION-BASED ALGORITHM

2020 ◽  
pp. 48-56
Author(s):  
S. I. Rodzin ◽  
O. N. Rodzina
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Hybrid Model ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Experimental Studies ◽  
Search Space ◽  
Population Based ◽  
Content Filtering ◽  
Population Based Algorithm

The article considers the formulation of the forecasting problem as well as such problems of recommender systems as data sparsity, cold start, scalability, synonymy, fraud, diversity, white crows. Combining the results of collaborative and content filtering gives us two possibilities. On the one hand, to weigh the results according to the content data. On the other hand, to shift these weights towards collaborative filtering as soon as data about a particular user appears. In turn, this improves the accuracy of the recommendations. The authors propose a hybrid model of a recommender system. Such a system includes the characteristics of collaborative and content filtering both. Also, the population-based algorithm for filtering and the architecture of a recommendation system based on it are described in the article. The algorithm consists of the following steps: study the search space; synthesis of solutions, i.e. points of this space; request quality assessment decisions or “fitness”; using it to make “natural selection”. Here we see the learning process about which areas of the search space contain the best solutions. The population of user “characteristics” encoded in the population-based algorithm supports a variety of input data in a hybrid model. The authors propose a coding structure for decisions in a population-based algorithm using the example of a recommender movie viewing system. Drift analysis evaluates the polynomial complexity of the algorithm. The authors demonstrate the results of experimental studies on an array of benchmarks. We also present an assessment of filtration efficiency based on a hybrid model and a population-based algorithm in comparison with the traditional method of collaborative filtering using the Pearson correlation coefficient. We can see that the prediction accuracy of the population-based algorithm is higher than that of the Pearson algorithm.

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.

Improved Cluster-Minkowski Recommendation System (ICRS) for Collaborative Filtering

2019 ◽  
Vol 16 (9) ◽  
pp. 3892-3896
Author(s):  
Bhavana ◽  
Neeraj Raheja
Keyword(s):  
Comparative Analysis ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommendation System ◽  
Intelligent System ◽  
User Preferences ◽  
User Profiles ◽  
Content Filtering ◽  
History Of ◽  
Clustering Approach

Recommendation systems are intelligent system which provides suggestion according to user adaptability. Recommender systems i.e., collaborative filtering and content filtering works on the basis of user profiles, extensive history of user preferences and item descriptions. This paper proposes an improved recommendation system based on clustering approach. The comparative analysis shows that the proposed system provides better results in terms of RMSE as compared to other already existing methods.

Legal document recommendation system: A cluster based pairwise similarity computation

2021 ◽  
pp. 1-13
Author(s):  
Jenish Dhanani ◽  
Rupa Mehta ◽  
Dipti Rana
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Real Life ◽  
Citation Network ◽  
Search Space ◽  
Pairwise Similarity ◽  
Large Numbers ◽  
Legal Document ◽  
Legal Domain ◽  
Similarity Scores

Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O (n2). However, there is a limited number of pairs consisting of strong relevance among the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems.

An Effective Recommender System Based on Clustering Technique for TED Talks

2020 ◽  
Vol 15 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Faiz Maazouzi ◽  
Hafed Zarzour ◽  
Yaser Jararweh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Enormous Amount ◽  
Powerful Mechanism ◽  
Ted Talks

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.

scholarly journals Cognitive Similarity-Based Collaborative Filtering Recommendation System

2020 ◽  
Vol 10 (12) ◽  
pp. 4183 ◽  
Author(s):  
Luong Vuong Nguyen ◽  
Min-Sung Hong ◽  
Jason J. Jung ◽  
Bong-Soo Sohn
Keyword(s):  
Performance Evaluation ◽  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Cognitive Network ◽  
Data Set ◽  
New Approach ◽  
Layered Architecture ◽  
F Measure

This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria P r e c i s i o n and R e c a l l . Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance.

scholarly journals An Automated Music Recommendation System Based on Listener Preferences

2021 ◽  
Author(s):  
Mukkamala. S.N.V. Jitendra ◽  
Y. Radhika
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Recommendation System ◽  
Software Tool ◽  
Vital Role ◽  
Music Recommendation ◽  
The World ◽  
Different Types ◽  
Music Recommendation System

Recommender systems play a vital role in e-commerce. It is a big source of a market that brings people from all over the world to a single place. It has become easy to access and reach the market while sitting anywhere. Recommender systems do a major role in the commerce mobility go smoothly easily as it is a software tool that helps in showing or recommending items based on user’s preferences by analyzing their taste. In this paper, we make a recommender system that would be specifically for music applications. Different people listen to different types of music, so we make note of their taste in music and suggest to them the next song based on their previous choice. This is achieved by using a popularity algorithm, classification, and collaborative filtering. Finally, we make a comparison of the built system for its effectiveness with different evaluation metrics.

scholarly journals Enhancing Item-Based Collaborative Filtering with Item Correlations for Music Recommendation System

2020 ◽  
Vol 9 (1) ◽  
pp. 1548-1553
Keyword(s):  
Collaborative Filtering ◽  
Digital Libraries ◽  
Correlation Matrix ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Recommendation Systems ◽  
Vital Role ◽  
Music Recommendation ◽  
Filtering Method ◽  
Music Recommendation System

Music recommendation systems are playing a vital role in suggesting music to the users from huge volumes of digital libraries available. Collaborative filtering (CF) is a one of the well known method used in recommendation systems. CF is either user centric or item centric. The former is known as user-based CF and later is known as item-based CF. This paper proposes an enhancement to item-based collaborative filtering method by considering correlation among items. Lift and Pearson Correlation coefficient are used to find the correlation among items. Song correlation matrix is constructed by using correlation measures. Proposed method is evaluated on the benchmark dataset and results obtained are compared with basic item-based CF

scholarly journals Book Recommendation System using Matrix Factorization

2021 ◽  
Vol 9 (VI) ◽  
pp. 4578-4582
Author(s):  
K. Venkata Ruchitha
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Previous History ◽  
Speed Up ◽  
Area Unit ◽  
Content Based Filtering

In recent years, recommender systems became more and more common and area unit applied to a various vary of applications, thanks to development of things and its numerous varieties accessible, that leaves the users to settle on from bumper provided choices. Recommendations generally speed up searches and create it easier for users to access content that they're curious about, and conjointly surprise them with offers they'd haven't sought for. By victimisation filtering strategies for pre-processing the information, recommendations area unit provided either through collaborative filtering or through content-based Filtering. This recommender system recommends books supported the description and features. It identifies the similarity between the books supported its description. It conjointly considers the user previous history so as to advocate the identical book.

A Review on User Recommendation System Based Upon Semantic Analysis

2017 ◽  
Vol 7 (11) ◽  
pp. 35
Author(s):  
Lovedeep Kaur ◽  
Naveen Kumari
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Semantic Analysis ◽  
Pearson Correlation ◽  
Prediction Method ◽  
Prediction Algorithm ◽  
Support Vector ◽  
User Interest ◽  
Basic Algorithm ◽  
Abstract Class

Recommender system applied various techniques and prediction algorithm to predict user interest on information, items and services from the tremendous amount of available data on the internet. Recommender systems are now becoming increasingly important to individual users, businesses and specially e-commerce for providing personalized recommendations. Recommender systems have been evaluated and improved in many, often incomparable, ways. In this paper, we review the evaluation and improvement techniques for improving overall performance of recommendation systems and proposing a semantic analysis based approach for clustering based collaborative filtering to improve the coverage of recommendation. The basic algorithm or predictive model we use are – simple linear regression, k-nearest neighbours(kNN), naives bayes, support vector machine. We also review the pearson correlation coefficient algorithm and an associative analysis-based heuristic. The algorithms themselves were implemented from abstract class recommender, which was extended from weka distribution classifier class. The abstract class adds prediction method to the classifier.

A Hybrid Nature-Inspired Artificial Bee Colony Algorithm for Uncapacitated Examination Timetabling Problems

2015 ◽  
Vol 24 (1) ◽  
pp. 37-54 ◽  
Author(s):  
Asaju La’aro Bolaji ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah
Keyword(s):  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Space ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Second Phase ◽  
Examination Timetabling ◽  
Bee Colony ◽  
Population Based Algorithm

AbstractThis article presents a Hybrid Artificial Bee Colony (HABC) for uncapacitated examination timetabling. The ABC algorithm is a recent metaheuristic population-based algorithm that belongs to the Swarm Intelligence technique. Examination timetabling is a hard combinatorial optimization problem of assigning examinations to timeslots based on the given hard and soft constraints. The proposed hybridization comes in two phases: the first phase hybridized a simple local search technique as a local refinement process within the employed bee operator of the original ABC, while the second phase involves the replacement of the scout bee operator with the random consideration concept of harmony search algorithm. The former is to empower the exploitation capability of ABC, whereas the latter is used to control the diversity of the solution search space. The HABC is evaluated using a benchmark dataset defined by Carter, including 12 problem instances. The results show that the HABC is better than exiting ABC techniques and competes well with other techniques from the literature.

Sign in / Sign up

Export Citation Format

Share Document