Wavelet-Based Collaborative Filtering for Adapting Changes in User Behavior

2006 ◽  
pp. 470-473
Author(s):  
Hyeonjae Cheon ◽  
Hongchul Lee ◽  
Insup Um
Keyword(s):  
Collaborative Filtering ◽  
User Behavior

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

scholarly journals Long Short-Term Memory Based Movie Recommendation

2020 ◽  
Vol 3 (SI1) ◽  
pp. SI1-SI9
Author(s):  
Sang Thi Thanh Nguyen ◽  
Bao Duy Tran
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Business Models ◽  
Short Term Memory ◽  
User Behavior ◽  
Learning Method ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Recommender systems (RS) have become a fundamental tool for helping users make decisions around millions of different choices nowadays – the era of Big Data. It brings a huge benefit for many business models around the world due to their effectiveness on the target customers. A lot of recommendation models and techniques have been proposed and many accomplished incredible outcomes. Collaborative filtering and content-based filtering methods are common, but these both have some disadvantages. A critical one is that they only focus on a user's long-term static preference while ignoring his or her short-term transactional patterns, which results in missing the user's preference shift through the time. In this case, the user's intent at a certain time point may be easily submerged by his or her historical decision behaviors, which leads to unreliable recommendations. To deal with this issue, a session of user interactions with the items can be considered as a solution. In this study, Long Short-Term Memory (LSTM) networks will be analyzed to be applied to user sessions in a recommender system. The MovieLens dataset is considered as a case study of movie recommender systems. This dataset is preprocessed to extract user-movie sessions for user behavior discovery and making movie recommendations to users. Several experiments have been carried out to evaluate the LSTM-based movie recommender system. In the experiments, the LSTM networks are compared with a similar deep learning method, which is Recurrent Neural Networks (RNN), and a baseline machine learning method, which is the collaborative filtering using item-based nearest neighbors (item-KNN). It has been found that the LSTM networks are able to be improved by optimizing their hyperparameters and outperform the other methods when predicting the next movies interested by users.

scholarly journals Exploiting the User Social Context to Address Neighborhood Bias in Collaborative Filtering Music Recommender Systems

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 439 ◽  
Author(s):  
Diego Sánchez-Moreno ◽  
Vivian López Batista ◽  
M. Dolores Muñoz Vicente ◽  
Ángel Luis Sánchez Lázaro ◽  
María N. Moreno-García
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
User Behavior ◽  
Error Rates ◽  
User Preferences ◽  
Music Recommendation ◽  
Power Law Distribution ◽  
Long Tail ◽  
Music Recommender Systems ◽  
Structure Metrics

Recent research in the field of recommender systems focuses on the incorporation of social information into collaborative filtering methods to improve the reliability of recommendations. Social networks enclose valuable data regarding user behavior and connections that can be exploited in this area to infer knowledge about user preferences and social influence. The fact that streaming music platforms have some social functionalities also allows this type of information to be used for music recommendation. In this work, we take advantage of the friendship structure to address a type of recommendation bias derived from the way collaborative filtering methods compute the neighborhood. These methods restrict the rating predictions for a user to the items that have been rated by their nearest neighbors while leaving out other items that might be of his/her interest. This problem is different from the popularity bias caused by the power-law distribution of the item rating frequency (long-tail), well-known in the music domain, although both shortcomings can be related. Our proposal is based on extending and diversifying the neighborhood by capturing trust and homophily effects between users through social structure metrics. The results show an increase in potentially recommendable items while reducing recommendation error rates.

scholarly journals User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

2020 ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh Bharata Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
User Profile ◽  
Profile Data ◽  
Improve Performance ◽  
Similarity Algorithm ◽  
Single User ◽  
Previous Algorithm ◽  
Movie Recommendation

Abstract A recommendation system is a software used in the e-commerce field that provides recommendations for customers to choose the items they like. Several recommendation systems have been proposed; however, collaborative filtering is the most widely used approach. The main issue in collaborative filtering is how to implement a similarity algorithm that can improve performance in the recommendation system. Several similarity algorithms based on user rating value have been developed, and recently a similarity algorithm has been developed that combines the user rating value and the user behavior value. However, the existing research is still based only on a single user behavior value, which is the genre data. Therefore, we propose a new similarity algorithm that considers not only the genre data but also the user profile data (namely age, gender, occupation, and location). The new similarity we are proposing is called User Profile Correlation-based Similarity (UPCSim). The user profile correlation similarity was obtained by calculating the correlation coefficient between the user profile data and the user rating or behavior values. An experiment was done to compare the accuracy of the UPCSim algorithm with that of the previous algorithm. The experiment results show that the UPCSim algorithm can improve the recommendation performance MAE by 1.64% and RMSE by 1.4% compared to the previous algorithm.

scholarly journals Enhanced Recommendation System for E-commerce Applications

2019 ◽  
Vol 8 (9) ◽  
pp. 473-478
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Web Applications ◽  
Online Shopping ◽  
User Behavior ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
Special Subset ◽  
And Behavior

Recommendation algorithms play a quintessential role in development of E-commerce recommendation system, Where in Collaborative filtering algorithm is a major contributor for most recommendation systems since they are a flavor of KNN algorithm specifically tailored for E-commerce Web Applications, the main advantages of using CF algorithms are they are efficient in capturing collective experiences and behavior of e-commerce customers in real time, But it is noted that , this results in the phenomenon of Mathew effect, Wherein only popular products are listed into the recommendation list and lesser popular items tend to become even more scarce. Hence this results in products which are already familiar to users being discovered redundantly, thus potential discovery of niche and new items in the e-commerce application is compromised. To address this issue , this paper throws light on user behavior on the online shopping platform , accordingly a novel selectivity based collaborative filtering algorithm is proposed with innovator products that can recommend niche items but less popular products to users by introducing the concept of collaborative filtering with consumer influencing capability. Specifically, innovator products are a special subset of products which are less popular/ have received less traction from users but are genuinely of higher quality, therefore, these aforementioned products can be captured in the recommendation list via innovator-recognition table, achieving the balance between popularity and practicability for the user

scholarly journals A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xibin Wang ◽  
Zhenyu Dai ◽  
Hui Li ◽  
Jianfeng Yang
Keyword(s):  
Support Vector Machine ◽  
Active Learning ◽  
Collaborative Filtering ◽  
User Behavior ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Support Vector ◽  
Item Information ◽  
Quality Model ◽  
Rating Data

In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a “maximum-minimum segmentation” of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency.

scholarly journals Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach

2020 ◽  
Vol 10 (15) ◽  
pp. 5324 ◽  
Author(s):  
Diego Sánchez-Moreno ◽  
Yong Zheng ◽  
María N. Moreno-García
Keyword(s):  
Collaborative Filtering ◽  
User Behavior ◽  
User Preferences ◽  
Context Aware ◽  
Listening Behavior ◽  
Streaming Services ◽  
Music Recommender Systems ◽  
Listening Habits ◽  
Online Streaming ◽  
Context Free

Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest them from the vast amount of music available. However, many are not reliable as they may not take into account contextual aspects or the ever-evolving user behavior. Therefore, it is necessary to develop systems that consider these aspects. In the field of music, time is one of the most important factors influencing user preferences and managing its effects, and is the motivation behind the work presented in this paper. Here, the temporal information regarding when songs are played is examined. The purpose is to model both the evolution of user preferences in the form of evolving implicit ratings and user listening behavior. In the collaborative filtering method proposed in this work, daily listening habits are captured in order to characterize users and provide them with more reliable recommendations. The results of the validation prove that this approach outperforms other methods in generating both context-aware and context-free recommendations.

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