A feature-based regression algorithm for cold-start recommendation

被引：4

作者：

Xu, Xiujuan ^{[1
]}

Zhu Lizhong ^{[1
]}

Zhao Xiaowei ^{[1
]}

Xu Zhenzhen ^{[1
]}

Liu Yu ^{[1
]}

机构：

[1] Dalian Univ Technol, Software Sch, Econ & Technol Dev Dist, Tuqiang St 321, Dalian, Peoples R China

来源：

JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING | 2014年 / 31卷 / 01期

关键词：

cold-start problems; user and item effects; baseline estimates; collaborative filtering; content-based filtering;

D O I：

10.1080/21681015.2013.879394

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Recommender systems are widely used to help user select relevant online information. A key challenge of recommender systems is to provide high-quality recommendations for cold-start users or cold-start items. We propose a feature-based regression algorithm with baseline estimates to cope with three types of cold-start problems: cold-start system, cold-start users, and cold-start items. We consider all available information of users and items to solve the cold-start problems and take into account the user and item effects that exist in collaborative filtering systems. Compared to some existing algorithms, our algorithm is effective on the 100 k MovieLens data-set for cold-start recommendation.

引用

页码：17 / 26

页数：10

共 20 条

[1] Bell RM, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P95
[2] A collaborative filtering approach to mitigate the new user cold start problem
Bobadilla, Jesus
Ortega, Fernando
Hernando, Antonio
Bernal, Jesus
[J]. KNOWLEDGE-BASED SYSTEMS, 2012, 26 : 225 - 238
[3] Du Huseyin Polat andWenliang, 2005, SVD BASED COLLABORAT, P791
[4] Gantner Z., 2010, Proceedings 2010 10th IEEE International Conference on Data Mining (ICDM 2010), P176, DOI 10.1109/ICDM.2010.129
[5] USING COLLABORATIVE FILTERING TO WEAVE AN INFORMATION TAPESTRY
GOLDBERG, D
NICHOLS, D
OKI, BM
TERRY, D
[J]. COMMUNICATIONS OF THE ACM, 1992, 35 (12) : 61 - 70
[6] Good N, 1999, SIXTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-99)/ELEVENTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE (IAAI-99), P439
[7] An algorithmic framework for performing collaborative filtering
Herlocker, JL
Konstan, JA
Borchers, A
Riedl, J
[J]. SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, 1999, : 230 - 237
[8] Jamali M, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P397
[9] Koren Y., 2008, PROC 14 ACM SIGKDD I, P426
[10] Factor in the Neighbors: Scalable and Accurate Collaborative Filtering
Koren, Yehuda
[J]. ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2010, 4 (01)

← 1 2 →