Discovery-oriented collaborative filtering for improving user satisfaction

2008 ◽  
Author(s):  
Yoshinori Hijikata ◽  
Takuya Shimizu ◽  
Shogo Nishida
Keyword(s):  
Collaborative Filtering ◽  
User Satisfaction

scholarly journals MINING TRUST VALUES FROM RECOMMENDATION ERRORS

2006 ◽  
Vol 15 (06) ◽  
pp. 945-962 ◽  
Author(s):  
JOHN O'DONOVAN ◽  
BARRY SMYTH
Keyword(s):  
Collaborative Filtering ◽  
User Satisfaction ◽  
Information Needs ◽  
Recommendation System ◽  
Predictive Accuracy ◽  
Target User ◽  
Fair Comparison ◽  
Collaborative Filtering Algorithm ◽  
History Of ◽  
User Trust

Increasing availability of information has furthered the need for recommender systems across a variety of domains. These systems are designed to tailor each user's information space to suit their particular information needs. Collaborative filtering is a successful and popular technique for producing recommendations based on similarities in users' tastes and opinions. Our work focusses on these similarities and the fact that current techniques for defining which users contribute to recommendation are in need of improvement. In this paper we propose the use of trustworthiness as an improvement to this situation. In particular, we define and empirically test a technique for eliciting trust values for each producer of a recommendation based on that user's history of contributions to recommendations. We compute a recommendation range to present to a target user. This is done by leveraging under/overestimate errors in users' past contributions in the recommendation process. We present three different models to compute this range. Our evaluation shows how this trust-based technique can be easily incorporated into a standard collaborative filtering algorithm and we define a fair comparison in which our technique outperforms a benchmark algorithm in predictive accuracy. We aim to show that the presentation of absolute rating predictions to users is more likely to reduce user trust in the recommendation system than presentation of a range of rating predictions. To evaluate the trust benefits resulting from the transparency of our recommendation range techniques, we carry out user-satisfaction trials on BoozerChoozer, a pub recommendation system. Our user-satisfaction results show that the recommendation range techniques perform up to twice as well as the benchmark.

scholarly journals An Approach to Integrating Sentiment Analysis into Recommender Systems

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5666
Author(s):  
Cach N. Dang ◽  
María N. Moreno-García ◽  
Fernando De la Prieta
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Recommender Systems ◽  
User Satisfaction ◽  
Online News ◽  
Learning Models ◽  
Adaptive Architecture ◽  
Wide Range ◽  
The One

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

scholarly journals Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1997
Author(s):  
Tae-Yeun Kim ◽  
Hoon Ko ◽  
Sung-Hwan Kim ◽  
Ho-Da Kim
Keyword(s):  
Collaborative Filtering ◽  
Kernel Function ◽  
User Satisfaction ◽  
Recommendation System ◽  
Feature Selection Method ◽  
Support Vector ◽  
Emotional States ◽  
User Characteristics ◽  
Rbf Kernel ◽  
Optimal Kernel

Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.

State-of-the-Art Recommender Systems

2011 ◽  
pp. 1-22 ◽  
Author(s):  
Laurent Candillier ◽  
Kris Jack ◽  
Françoise Fessant ◽  
Frank Meyer
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Hybrid System ◽  
User Satisfaction ◽  
State Of The Art ◽  
Predictive Performance ◽  
Performance Accuracy ◽  
Item Content ◽  
User Ratings ◽  
Content Based Filtering

The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative filtering approaches can be distinguished from content-based ones. The former is based on a set of user ratings on items, while the latter uses item content descriptions and user thematic profiles. While collaborative filtering systems often result in better predictive performance, content-based filtering offers solutions to the limitations of collaborative filtering, as well as a natural way to interact with users. These complementary approaches thus motivate the design of hybrid systems. In this chapter, the main algorithmic methods used for recommender systems are presented in a state of the art. The evaluation of recommender systems is currently an important issue. The authors focus on two kinds of evaluations. The first one concerns the performance accuracy: several approaches are compared through experiments on two real movies rating datasets MovieLens and Netflix. The second concerns user satisfaction and for this a hybrid system is implemented and tested with real users.

scholarly journals An Approach to Integrating Sentiment Analysis into Recommender Systems

2021 ◽  
Author(s):  
Cach Nhan Dang ◽  
María N. Moreno ◽  
Fernando De la Prieta
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Recommender Systems ◽  
Recommender System ◽  
User Satisfaction ◽  
Online News ◽  
Learning Models ◽  
Adaptive Architecture ◽  
Wide Range

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data in order to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

scholarly journals Cooperation Based Proactive Caching in Multi-Tier Cellular Networks

2020 ◽  
Vol 10 (18) ◽  
pp. 6145
Author(s):  
Fawad Ahmad ◽  
Ayaz Ahmad ◽  
Irshad Hussain ◽  
Peerapong Uthansakul ◽  
Suleman Khan
Keyword(s):  
Collaborative Filtering ◽  
Cellular Networks ◽  
User Satisfaction ◽  
Data Transfer ◽  
Base Station ◽  
Fifth Generation ◽  
Local Cache ◽  
Content Popularity ◽  
Significant Performance ◽  
Cache Hit Ratio

The limited caching capacity of the local cache enabled Base station (BS) decreases the cache hit ratio (CHR) and user satisfaction ratio (USR). However, Cache enabled multi-tier cellular networks have been presented as a promising candidate for fifth generation networks to achieve higher CHR and USR through densification of networks. In addition to this, the cooperation among the BSs of various tiers for cached data transfer, intensify its significance many folds. Therefore, in this paper, we consider maximization of CHR and USR in a multi-tier cellular network. We formulate a CHR and USR problem for multi-tier cellular networks while putting major constraints on caching space of BSs of each tier. The unsupervised learning algorithms such as K-mean clustering and collaborative filtering have been used for clustering the similar BSs in each tier and estimating the content popularity respectively. A novel scheme such as cluster average popularity based collaborative filtering (CAP-CF) algorithm is employed to cache popular data and hence maximizing the CHR in each tier. Similarly, two novel methods such as intra-tier and cross-tier cooperation (ITCTC) and modified ITCTC algorithms have been employed in order to optimize the USR. Simulations results witness, that the proposed schemes yield significant performance in terms of average cache hit ratio and user satisfaction ratio compared to other conventional approaches.

scholarly journals Top-k Recommended Items: Applying Clustering Technique for Recommendation

2019 ◽  
Vol 12 (2) ◽  
pp. 106-117
Author(s):  
Kittisak Onuean ◽  
Sunantha Sodsee ◽  
Phayung Meesad
Keyword(s):  
Collaborative Filtering ◽  
User Satisfaction ◽  
Recommendation System ◽  
Experimental Results ◽  
User Preference ◽  
Data Sparsity ◽  
Clustering Techniques ◽  
Item List ◽  
Filtering Technique ◽  
Item Types

This research proposes the Top-k Items Recommendation System which uses clustering techniques based on memory-based collaborative filtering technique. Currently, data sparsity and quantity of system are problems in memory-based collaborative filtering technique. We offer recommend or show some items set for user’s preference.  In this research, we propose methods for recommended items set to user preference on data sparsity, movie lens datasets (1M) consisting of 671 users and 163,949 product items were used by determining the preference level between 1 and 5 and user satisfaction levels of all 98,903 items being build and test the models. Methods was divided into three parts included 1) Simple Agent Module 2) Neighbor Filtering and 3) Prediction and Recommend. Simple clustering was used to create a system to provide suggestions for sparsity data. Datasets obtained from clustering represented the sample agent of dataset to being create the recommendation system. Datasets were divided into two categories, 1) Traditional Data (TD) and 2) Statistic Data (SD), and each dataset clustered by k-means clustering. The experimental results demonstrated that the number of item types in the system were recommended in the TD and Euclidean (DIS). DIS was used to find the nearest value in TD for the item list recommendation to active users in the system with the a lot of number choice of recommendation system.

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