Comparison of measures of collaborative filtering recommender systems: rating prediction accuracy versus usage prediction accuracy

2017 ◽  
Author(s):  
Rohit ◽  
Anil Kumar Singh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Prediction Accuracy ◽  
Rating Prediction

scholarly journals An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 68301-68310 ◽  
Author(s):  
Dionisis Margaris ◽  
Anna Kobusinska ◽  
Dimitris Spiliotopoulos ◽  
Costas Vassilakis
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Rating Prediction

A Collaborative Filtering Recommendation Algorithm Based on Cloud Model, Item Attribute and User Data

2014 ◽  
Vol 610 ◽  
pp. 747-751
Author(s):  
Jian Sun ◽  
Xiao Ying Chen
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Cloud Model ◽  
Prediction Algorithm ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
User Data ◽  
Item Attribute ◽  
Rating Prediction ◽  
User Ratings

Aiming at the problems of extremely sparse of user-item rating data and poor recommendation quality, we put forward a collaborative filtering recommendation algorithm based on cloud model, item attribute and user data which combined with the existing literatures. A rating prediction algorithm based on cloud model and item attribute is proposed, based on idea that the similar users rating for the same item are similar and the same user ratings for the similar items are similar and stable. Through compare and analysis this paper’s and other studies experimental results, we get the conclusion that the rating prediction accuracy is improved.

scholarly journals Improved Movie Recommendations Based on a Hybrid Feature Combination Method

2019 ◽  
Vol 06 (03) ◽  
pp. 363-376 ◽  
Author(s):  
Gharbi Alshammari ◽  
Stelios Kapetanakis ◽  
Abdullah Alshammari ◽  
Nikolaos Polatidis ◽  
Miltos Petridis
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Hybrid Method ◽  
Prediction Accuracy ◽  
Combination Method ◽  
Evaluation Metrics ◽  
Classification Algorithms ◽  
Feature Combination ◽  
Knowledge Based ◽  
Content Based Filtering

Recommender systems help users find relevant items efficiently based on their interests and historical interactions with other users. They are beneficial to businesses by promoting the sale of products and to user by reducing the search burden. Recommender systems can be developed by employing different approaches, including collaborative filtering (CF), demographic filtering (DF), content-based filtering (CBF) and knowledge-based filtering (KBF). However, large amounts of data can produce recommendations that are limited in accuracy because of diversity and sparsity issues. In this paper, we propose a novel hybrid method that combines user–user CF with the attributes of DF to indicate the nearest users, and compare four classifiers against each other. This method has been developed through an investigation of ways to reduce the errors in rating predictions based on users’ past interactions, which leads to improved prediction accuracy in all four classification algorithms. We applied a feature combination method that improves the prediction accuracy and to test our approach, we ran an offline evaluation using the 1M MovieLens dataset, well-known evaluation metrics and comparisons between methods with the results validating our proposed method.

scholarly journals A Rating-Based Integrated Recommendation Framework with Improved Collaborative Filtering Approaches

2017 ◽  
Vol 12 (3) ◽  
pp. 307
Author(s):  
Shulin Cheng ◽  
Bofeng Zhang ◽  
Guobing Zou
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Information Sources ◽  
Information Source ◽  
Strong Robustness ◽  
Personal Recommendation ◽  
Rating Prediction ◽  
Interest Model ◽  
Low Sensitivity ◽  
Individual Information

Collaborative filtering (CF) approach is successfully applied in the rating prediction of personal recommendation. But individual information source is leveraged in many of them, i.e., the information derived from single perspective is used in the user-item matrix for recommendation, such as user-based CF method mainly utilizing the information of user view, item-based CF method mainly exploiting the information of item view. In this paper, in order to take full advantage of multiple information sources embedded in user-item rating matrix, we proposed a rating-based integrated recommendation framework of CF approaches to improve the rating prediction accuracy. Firstly, as for the sparsity of the conventional item-based CF method, we improved it by fusing the inner similarity and outer similarity based on the local sparsity factor. Meanwhile, we also proposed the improved user-based CF method in line with the user-item-interest model (UIIM) by preliminary rating. Second, we put forward a background method called user-item-based improved CF (UIBCF-I), which utilizes the information source of both similar items and similar users, to smooth itembased and user-based CF methods. Lastly, we leveraged the three information sources and fused their corresponding ratings into an Integrated CF model (INTE-CF). Experiments demonstrate that the proposed rating-based INTE-CF indeed improves the prediction accuracy and has strong robustness and low sensitivity to sparsity of dataset by comparisons to other mainstream CF approaches.

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

2021 ◽  
Vol 11 (18) ◽  
pp. 8369
Author(s):  
Dionisis Margaris ◽  
Dimitris Spiliotopoulos ◽  
Costas Vassilakis
Keyword(s):  
Collaborative Filtering ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Supplementary Information ◽  
Similarity Metrics ◽  
Sources Of Information ◽  
Black Sheep ◽  
Error Metrics ◽  
Target User ◽  
Rating Prediction

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.

scholarly journals Improving the Prediction Quality in Memory-Based Collaborative Filtering Using Categorical Features

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 214
Author(s):  
Lei Chen ◽  
Yuyu Yuan ◽  
Jincui Yang ◽  
Ahmed Zahir
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Side Information ◽  
Weight Matrix ◽  
K Nearest Neighbor ◽  
Proposed Model ◽  
Real World Datasets ◽  
Better Than

Despite years of evolution of recommender systems, improving prediction accuracy remains one of the core problems among researchers and industry. It is common to use side information to bolster the accuracy of recommender systems. In this work, we focus on using item categories, specifically movie genres, to improve the prediction accuracy as well as coverage, precision, and recall. We derive the user’s taste for an item using the ratings expressed. Similarly, using the collective ratings given to an item, we identify how much each item belongs to a certain genre. These two vectors are then combined to get a user-item-weight matrix. In contrast to the similarity-based weight matrix in memory-based collaborative filtering, we use user-item-weight to make predictions. The user-item-weights can be used to explain to users why certain items have been recommended. We evaluate our proposed method using three real-world datasets. The proposed model performs significantly better than the baseline methods. In addition, we use the user-item-weight matrix to alleviate the sparsity problem associated with correlation-based similarity. In addition to that, the proposed model has a better computational complexity for making predictions than the k-nearest neighbor (kNN) method.

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