Collaborative Filtering Recommendation Algorithm Based on Rating Prediction and Ranking Prediction

LI Gai; CHEN Qiang; LI Lei

doi:10.3969/j.issn.0372-2112.2017.12.033

您当前的位置：

首页 >

文章列表页 >

Collaborative Filtering Recommendation Algorithm Based on Rating Prediction and Ranking Prediction

更新时间：2025-07-16

- Collaborative Filtering Recommendation Algorithm Based on Rating Prediction and Ranking Prediction
- Acta Electronica Sinica Vol. 45, Issue 12, Pages: 3070-3075(2017)
- 作者机构：
  
  1. 顺德职业技术学院电子与信息工程学院, 广东顺德,528333
  2. 中山大学数据科学与计算机学院,广东,广州,510006
  3. 广东第二师范学院计算机科学系,广东,广州,510303
  4. 顺德职业技术学院电子与信息工程学院广东顺德,528333
  5. 中山大学数据科学与计算机学院,广东,广州,510006
  6. 广东第二师范学院计算机科学系,广东,广州,510303
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2017.12.033
  CLC： TP302
- Published Online：25 December 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
LI Gai, CHEN Qiang, LI Lei. Collaborative Filtering Recommendation Algorithm Based on Rating Prediction and Ranking Prediction[J]. Acta Electronica Sinica, 2017, 45(12): 3070-3075.
DOI：

LI Gai, CHEN Qiang, LI Lei. Collaborative Filtering Recommendation Algorithm Based on Rating Prediction and Ranking Prediction[J]. Acta Electronica Sinica, 2017, 45(12): 3070-3075. DOI： 10.3969/j.issn.0372-2112.2017.12.033.

摘要

协同过滤推荐算法在电子商务领域运用广泛.之前的研究要么仅从评分预测的角度来研究，要么仅从排序预测的角度来研究.为了兼顾这两个方面，本文在传统的基于评分预测的PMF（Probabilistic Matrix Factorization）算法和基于排序预测的xCLiMF（Extended Collaborative Less-is-More Filtering）算法的基础上提出了一种基于评分预测与排序预测的协同过滤推荐算法URA（Unified Recommendation Algorithm），该方法通过在PMF和xCLiMF算法中共享用户和推荐对象的特征空间，利用PMF算法来学习高精度的用户和推荐对象的特征向量，从而进一步增强排序推荐性能.实验验证，该方法在评价指标NDCG和ERR下均优于PMF和xCLiMF算法，且复杂度与评分点个数线性相关.URA算法可运用于互联网信息推荐领域的大数据处理.

Abstract

Collaborative filtering (CF) recommendation algorithm is widely used in the field of e-commerce.The previous researches on CF focused on either rating prediction or ranking prediction.In order to take into account these two aspects

a collaborative filtering recommendation algorithm based on rating prediction and ranking prediction (Unified Recommendation Algorithm

URA) is proposed.URA shares common latent features of users and items in PMF (Probabilistic Matrix Factorization

rating-oriented) and xCLiMF (Extended Collaborative Less-is-More Filtering

ranking-oriented) algorithms

and PMF learns improved latent features of users and items in URA

so that URA improves the performance of ranking recommendation.Experimental results showed that our proposed URA Algorithm outperformed PMF and xCLiMF algorithms over evaluation metrics NDCG and ERR

and that the complexity of URA is shown to be linear with the number of observed ratings.URA is suitable for big data processing in the field of internet information recommendation.

关键词

Keywords

references

Views

562

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Self-Supervised Learning for Alleviating Popularity Bias in Recommender Systems

Cross Domain Rating Prediction Based on Aspect and Capsule Network

Task Recommendation Method Based on Collaborative Ranking in Mobile Crowd Sensing

Research on Privacy-Preserving Two-Party Collaborative Filtering Recommendation

A Study on the Ranking Method of Search Engines

Related Author

XIONG Qing-yu

WEN Jun-hao

GAO Min

ZHANG Shuai

TANG Xu

YUAN Fu-yong

LIANG Shun-pan

LIU Wei

Related Institution

Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education

School of Big Data & Software Engineering, Chongqing University

College of Information Science and Engineering,Yanshan University

College of Computer Science and Information Engineering, Harbin Normal University

School of Computer Science and Technology, Harbin University of Science and Technology

⁰