A Personalized Product Recommendation Algorithm Based on Preference and Intention Learning

2006 ◽  
Author(s):  
Yutao Guo ◽  
J.P. Muller
Keyword(s):  
Recommendation Algorithm ◽  
Product Recommendation

Product Recommendation Algorithm for Score Prediction Based on Joint Feature Vector Extraction

2021 ◽  
Author(s):  
Jian-hua LI ◽  
Chen-xi ZHANG ◽  
Chun-li LEI ◽  
Hong ZHANG ◽  
Lin-long CHEN
Keyword(s):  
Feature Vector ◽  
Recommendation Algorithm ◽  
Product Recommendation

Research on Heterogeneous Product Recommendation Algorithm Based on Item Similarity

2020 ◽  
Vol 10 (02) ◽  
pp. 111-117
Author(s):  
景明 陈
Keyword(s):  
Recommendation Algorithm ◽  
Product Recommendation

An utility range-based similar product recommendation algorithm for collaborative companies

2004 ◽  
Vol 27 (4) ◽  
pp. 549-557 ◽  
Author(s):  
Sang Hyun Choi ◽  
Yoon Ho Cho
Keyword(s):  
Recommendation Algorithm ◽  
Product Recommendation ◽  
Similar Product

Combining Item Rating Similarity and Item Classification Similarity for Better Recommendation Quality

2012 ◽  
Vol 461 ◽  
pp. 289-292
Author(s):  
Kai Zhou
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Fundamental Problem ◽  
The Other ◽  
Recommendation Algorithm ◽  
Data Sparsity ◽  
Filtering Method ◽  
Product Recommendation ◽  
Sparsity Problem

Recommender systems are becoming increasingly popular, and collaborative filtering method is one of the most important technologies in recommender systems. The ability of recommender systems to make correct predictions is fundamentally determined by the quality and fittingness of the collaborative filtering that implements them. It is currently mainly used for business purposes such as product recommendation. Collaborative filtering has two types. One is user based collaborative filtering using the similarity between users to predict and the other is item based collaborative filtering using the similarity between items. Although both of them are successfully applied in wide regions, they suffer from a fundamental problem of data sparsity. This paper gives a personalized collaborative filtering recommendation algorithm combining the item rating similarity and the item classification similarity. This method can alleviate the data sparsity problem in the recommender systems

Improved Collaborative Filtering Recommendation Algorithm Based on Weibo Content

2020 ◽  
Author(s):  
Juntao Xue ◽  
Ruohan Ma ◽  
Yunfeng Zhao ◽  
Junjie Hei
Keyword(s):  
Collaborative Filtering ◽  
Recommendation Algorithm

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

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