scholarly journals Machine Learning as a method of adapting offers to the clients

2019 ◽  
Vol 13 ◽  
pp. 267-271
Author(s):  
Jacek Bielecki ◽  
Oskar Ceglarski ◽  
Maria Skublewska-Paszkowska
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Filtering Method ◽  
Advantages And Disadvantages ◽  
Information Filter ◽  
User Ratings ◽  
Research Show

Recommendation systems are class of information filter applications whose main goal is to provide personalized recommendations. The main goal of the research was to compare two ways of creating personalized recommendations. The recommendation system was built on the basis of a content-based cognitive filtering method and on the basis of a collaborative filtering method based on user ratings. The conclusions of the research show the advantages and disadvantages of both methods.

scholarly journals Movie Recommenadation System

2021 ◽  
Vol 9 (4) ◽  
pp. 13-16
Author(s):  
Gandhali Malve ◽  
Lajree Lohar ◽  
Tanay Malviya ◽  
Shirish Sabnis
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Amount Of Information ◽  
System Recommendation ◽  
User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

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 Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1566 ◽  
Author(s):  
Zeinab Shahbazi ◽  
Debapriya Hazra ◽  
Sejoon Park ◽  
Yung Cheol Byun
Keyword(s):  
Machine Learning ◽  
South Korea ◽  
Collaborative Filtering ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Gradient Boosting ◽  
Mean Square ◽  
Extreme Gradient Boosting

With the spread of COVID-19, the “untact” culture in South Korea is expanding and customers are increasingly seeking for online services. A recommendation system serves as a decision-making indicator that helps users by suggesting items to be purchased in the future by exploring the symmetry between multiple user activity characteristics. A plethora of approaches are employed by the scientific community to design recommendation systems, including collaborative filtering, stereotyping, and content-based filtering, etc. The current paradigm of recommendation systems favors collaborative filtering due to its significant potential to closely capture the interest of a user as compared to other approaches. The collaborative filtering harnesses features like user-profile details, visited pages, and click information to determine the interest of a user, thereby recommending the items that are related to the user’s interest. The existing collaborative filtering approaches exploit implicit and explicit features and report either good classification or prediction outcome. These systems fail to exhibit good results for both measures at the same time. We believe that avoiding the recommendation of those items that have already been purchased could contribute to overcoming the said issue. In this study, we present a collaborative filtering-based algorithm to tackle big data of user with symmetric purchasing order and repetitive purchased products. The proposed algorithm relies on combining extreme gradient boosting machine learning architecture with word2vec mechanism to explore the purchased products based on the click patterns of users. Our algorithm improves the accuracy of predicting the relevant products to be recommended to the customers that are likely to be bought. The results are evaluated on the dataset that contains click-based features of users from an online shopping mall in Jeju Island, South Korea. We have evaluated Mean Absolute Error, Mean Square Error, and Root Mean Square Error for our proposed methodology and also other machine learning algorithms. Our proposed model generated the least error rate and enhanced the prediction accuracy of the recommendation system compared to other traditional approaches.

Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment

2020 ◽  
Vol 19 (02) ◽  
pp. 385-412 ◽  
Author(s):  
P. Shanmuga Sundari ◽  
M. Subaji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Semantic Analysis ◽  
Least Square ◽  
Data Sparsity ◽  
Filtering Method ◽  
Sparsity Problem ◽  
Hybrid Collaborative Filtering ◽  
User Ratings ◽  
Hybrid Recommendation

Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation system (Iter_ALS Iterative Alternate Least Square) to enhance the recommendation accuracy by integrating rating and emotion tags. The rating score reveals overall perception of the item and emotion tags reflects user’s feelings. In the absence of emotional tags, scores found in rating is assumed as positive or negative emotional tag score. Lexicon based semantic analysis on emotion tags value is adopted to represent the exclusive value of tag. Unified value is represented into Iter_ALS model to reduce the sparsity problem. In addition, this method handles opinion bias between ratings and tags. Experiments were tested and verified using a benchmark project of MovieLens dataset. Initially this model was tested with different sparsity levels varied between 0%-100 percent and the results obtained from the experiments shows the proposed method outperforms with baseline methods. Further tests were conducted to authenticate how it handles opinion bias by users before recommending the item. The proposed method is more capable to be adopted in many real world applications

scholarly journals Recommendation System for Purchasing Goods Based on the Decision Tree Algorithm

2021 ◽  
Vol 6 (2) ◽  
pp. 121-127
Author(s):  
Yurii Kohut ◽  
◽  
Iryna Yurchak
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Preference Profile ◽  
Final Decision ◽  
Filtering Method ◽  
Binary Descriptors ◽  
Short Period ◽  
The Impact ◽  
Content Based Filtering

Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The content-based filtering method is based on the description of the object and the customer’s preference profile. An object description is a finite set of its descriptors, such as keywords, binary descriptors, etc., and a preference profile is a weighted vector of object descriptors in which scales reflect the importance of each descriptor to the client and its contribution to the final decision. This model selects items that are similar to the customer’s favorite items before. The second model, which implements the method of collaborative filtering, is based on information about the history of behavior of all customers on the resource: data on their purchases, assessments of product quality, reviews, marked product. The model finds clients that are similar in behavior and the recommendation is based on their assessments of this element. Voting was used to combine the results issued by individual models — the best result is chosen from the results of two models of the ensemble. This approach minimizes the impact of randomness and averages the errors of each model. The aim: The purpose of work is to create real competitive recommendation system for short period of time and minimum costs.

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

Song Recommendations Based on Artists with Cosine Similarity Algorithms and K-Nearest Neighbor

2020 ◽  
Vol 8 (4) ◽  
pp. 367
Author(s):  
Muhammad Arief Budiman ◽  
Gst. Ayu Vida Mastrika Giri
Keyword(s):  
Collaborative Filtering ◽  
Mobile Phones ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
Music Recommendation ◽  
K Nearest Neighbor ◽  
Filtering Method ◽  
K Nearest Neighbor Algorithm ◽  
Music Recommendation System

The development of the music industry is currently growing rapidly, millions of music works continue to be issued by various music artists. As for the technologies also follows these developments, examples are mobile phones applications that have music subscription services, namely Spotify, Joox, GrooveShark, and others. Application-based services are increasingly in demand by users for streaming music, free or paid. In this paper, a music recommendation system is proposed, which the system itself can recommend songs based on the similarity of the artist that the user likes or has heard. This research uses Collaborative Filtering method with Cosine Similarity and K-Nearest Neighbor algorithm. From this research, a system that can recommend songs based on artists who are related to one another is generated.

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

Sign in / Sign up

Export Citation Format

Share Document