A novel recommendation model of location-based advertising: Context-Aware Collaborative Filtering using GA approach

被引:64
作者
Dao, Tuan Hung [2 ]
Jeong, Seung Ryul [1 ]
Ahn, Hyunchul [1 ]
机构
[1] Kookmin Univ, Sch Management Informat Syst, Seoul 136702, South Korea
[2] Kookmin Univ, Grad Sch Business Informat Technol, Seoul 136702, South Korea
基金
新加坡国家研究基金会;
关键词
Recommendation model; Context-awareness; Collaborative filtering; Genetic algorithm; Location-based advertising; PERSONALIZATION; INFORMATION; SYSTEMS;
D O I
10.1016/j.eswa.2011.09.070
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recommender systems are the efficient and most used tools that prevail over the information overload problem, provide users with the most appropriate content by considering their personal preferences (mostly, ratings). In addition to these preferences, taking into account the interaction context of users will improve the relevancy of the recommendation process. However, only a few prior studies have tried to adopt context-awareness to the recommendation model. Although a number of studies have developed recommendation models using collaborative filtering (CF), few of them have tried to adopt both CF and other artificial intelligence techniques, such as genetic algorithm (GA), as a tool to improve recommendation results. In this paper, we propose a new recommendation model, which we termed Context-Aware Collaborative Filtering using genetic algorithm (CACF-GA), for location-based advertising (LBA) based on both user's preferences and interaction's context. We first defined discrete contexts, and then applied the concept of "context similarity" to conventional CF to create the context-aware recommendation model. The context similarity between two contexts is designed to be optimized using GA. We collect real-world data from mobile users, build a LBA recommendation model using CACF-GA, and then perform an empirical test to validate the usefulness of CACF-GA. Experiments show our proposed model provides the most accurate prediction results compared to comparative ones. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:3731 / 3739
页数:9
相关论文
共 29 条
[1]  
ABBAR S, 2009, VLDB 09 PERSDB WORKS
[2]   Incorporating contextual information in recommender systems using a multidimensional approach [J].
Adomavicius, G ;
Sankaranarayanan, R ;
Sen, S ;
Tuzhilin, A .
ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2005, 23 (01) :103-145
[3]  
Anand SS, 2005, LECT NOTES ARTIF INT, V3169, P1
[4]   WORK AND OR FUN - MEASURING HEDONIC AND UTILITARIAN SHOPPING VALUE [J].
BABIN, BJ ;
DARDEN, WR ;
GRIFFIN, M .
JOURNAL OF CONSUMER RESEARCH, 1994, 20 (04) :644-656
[5]   Fab: Content-based, collaborative recommendation [J].
Balabanovic, M ;
Shoham, Y .
COMMUNICATIONS OF THE ACM, 1997, 40 (03) :66-72
[6]  
BALTRUNAS L, 2009, P 2009 WORKSH CONT A
[7]  
Billsus D., 1998, Proceedings of the Fifteenth International Conference on Machine Learning', ICML'98, P46
[8]  
Breese J. S., 2013, P 14 C UNC ART INT
[9]   A benefit congruency framework of sales promotion effectiveness [J].
Chandon, P ;
Wansink, B ;
Laurent, G .
JOURNAL OF MARKETING, 2000, 64 (04) :65-81
[10]   Context-aware collaborative filtering system: Predicting the user's preference in the ubiquitous computing environment [J].
Chen, A .
LOCATION- AND CONTEXT-AWARENESS, PROCEEDINGS, 2005, 3479 :244-253