Serendipitous Personalized Ranking for Top-N Recommendation

Serendipitous recommendation has benefitted both e-retailers and users. It tends to suggest items which are both unexpected and useful to users. These items are not only profitable to the retailers but also surprisingly suitable to consumers' tastes. However, due to the imbalance in observed da...

Full description

Saved in:
Bibliographic Details
Published in:2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Vol. 1; pp. 258 - 265
Main Authors: Lu, Qiuxia, Chen, Tianqi, Zhang, Weinan, Yang, Diyi, Yu, Yong
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2012
Subjects:
Accuracy
Collaborative filtering
Hands
Logistics
Mathematical models
Matrix Factorization
Measurement
Optimization
Predictive models
Recommender systems
Serendipity
Training
ISBN:9781467360579, 1467360570
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Serendipitous recommendation has benefitted both e-retailers and users. It tends to suggest items which are both unexpected and useful to users. These items are not only profitable to the retailers but also surprisingly suitable to consumers' tastes. However, due to the imbalance in observed data for popular and tail items, existing collaborative filtering methods fail to give satisfactory serendipitous recommendations. To solve this problem, we propose a simple and effective method, called serendipitous personalized ranking. The experimental results demonstrate that our method significantly improves both accuracy and serendipity for top-N recommendation compared to traditional personalized ranking methods in various settings.
ISBN:9781467360579
1467360570
DOI:10.1109/WI-IAT.2012.135