A new approach for rating prediction system using collaborative filtering

Kumar, Pushpendra; Kumar, Vinod; Thakur, Ramjeevan Singh

doi:10.1007/s42044-018-00028-5

Cited by 24 publications

(14 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researches based on recommender systems [1], [2], [3] have found that collaborative filtering algorithms being the most commonly used to develop RS. Based on user preferences, the biggest hurdle is to know how users gauge these preferences such that they can like the items and rate such items either positively or negatively.…”

Section: A Recommender Systemsmentioning

confidence: 99%

Using Feature Selection Methods to Discover Common Users’ Preferences for Online Recommender Systems

Ndung'u

Kamau

Mariga

2021

IJCIT

View full text Add to dashboard Cite

Recommender systems have taken over user’s choice to choose the items/services they want from online markets, where lots of merchandise is traded. Collaborative filtering-based recommender systems uses user opinions and preferences. Determination of commonly used attributes that influence preferences used for prediction and subsequent recommendation of unknown or new items to users is a significant objective while developing recommender engines. In conventional systems, study of user behavior to know their dis/like over items would be carried-out. In this paper, presents feature selection methods to mine such preferences through selection of high influencing attributes of the items. In machine learning, feature selection is used as a data pre-processing method but extended its use on this work to achieve two objectives; removal of redundant, uninformative features and for selecting formative, relevant features based on the response variable. The latter objective, was suggested to identify and determine the frequent and shared features that would be preferred mostly by marketplace online users as they express their preferences. The dataset used for experimentation and determination was synthetic dataset. The Jupyter Notebook™ using python was used to run the experiments. Results showed that given a number of formative features, there were those selected, with high influence to the response variable. Evidence showed that different feature selection methods resulted with different feature scores, and intrinsic method had the best overall results with 85% model accuracy. Selected features were used as frequently preferred attributes that influence users’ preferences.

show abstract

Section: A Recommender Systemsmentioning

confidence: 99%

Using Feature Selection Methods to Discover Common Users’ Preferences for Online Recommender Systems

Ndung'u

Kamau

Mariga

2021

IJCIT

View full text Add to dashboard Cite

show abstract

“…These two experiments produced very close results (less than 1% difference observed), hence, we report on the results from the first experiment, for conciseness. The practice described above is the typical one when evaluating a rating prediction CF algorithm [31,63,64].…”

Section: Algorithm Tuning and Experimental Evaluationmentioning

confidence: 99%

Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering

2021

View full text Add to dashboard Cite

In this work, an algorithm for enhancing the rating prediction accuracy in collaborative filtering, which does not need any supplementary information, utilising only the users’ ratings on items, is presented. This accuracy enhancement is achieved by augmenting the importance of the opinions of ‘black sheep near neighbours’, which are pairs of near neighbours with opinion agreement on items that deviates from the dominant community opinion on the same item. The presented work substantiates that the weights of near neighbours can be adjusted, based on the degree to which the target user and the near neighbour deviate from the dominant ratings for each item. This concept can be utilized in various other CF algorithms. The experimental evaluation was conducted on six datasets broadly used in CF research, using two user similarity metrics and two rating prediction error metrics. The results show that the proposed technique increases rating prediction accuracy both when used independently and when combined with other CF algorithms. The proposed algorithm is designed to work without the requirements to utilise any supplementary sources of information, such as user relations in social networks and detailed item descriptions. The aforesaid point out both the efficacy and the applicability of the proposed work.

show abstract

“…The recommendation system is software that helps users to get relevant items from millions of items in the database [5,6]. The recommendation system's main task is to offer users personalized item recommendations through information filtering.…”

Section: Introductionmentioning

confidence: 99%

“…The provided suggestions are useful to support users in various decision-making processes, such as what books to read, which locations to visit, what news to read, and more [7]. Based on the utilized data source and computation method, the recommendation system is divided into three approaches, namely: collaborative filtering, contentbased filtering, and hybrid filtering [6,8]. The collaborative filtering approach uses the collaborative power of ratings given by all users to make recommendations.…”

Section: Introductionmentioning

confidence: 99%

User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

Widiyaningtyas

Hidayah

Adji

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

A new approach for rating prediction system using collaborative filtering

Cited by 24 publications

References 11 publications

Using Feature Selection Methods to Discover Common Users’ Preferences for Online Recommender Systems

Using Feature Selection Methods to Discover Common Users’ Preferences for Online Recommender Systems

Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering

User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

Contact Info

Product

Resources

About