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Recommender Systems using Structural Hole and Collaborative Filtering
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Mingun Kim (Department of Management Information Systems, Graduate School, Dongguk University_Seoul)
Kyoung-jae Kim (Business School, Dongguk University_Seoul)
Vol. 20, No. 4, Page: 107 ~ 120
Structural hole; Collaborative filtering; Cluster-indexing; Social network analysis; Recommender system
This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user’s preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data.
In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user’s preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis.
This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users’ preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user’s cluster, and compare the recommendation performances of comparative models.
For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data – UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure – Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses ‘all-but-one’ approach for the CF experiment.
In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.
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구조적 공백과 협업필터링을 이용한 추천시스템
김민건 (동국대학교_서울 일반대학원 경영정보학과)
김경재 (동국대학교_서울 경영대학 경영학부)
구조적 공백; 협업필터링; 군집색인; 사회연결망분석; 추천시스템
본 연구에서는 사회연결망분석기법 중 하나인 구조적 공백 분석 결과를 이용하여 추천과정에 사용자의 정성적이고 감성적인 정보를 반영할 수 있는 협업필터링 기반의 추천시스템을 제안한다. 협업필터링은 추천기술 중 가장 많이 활용되고 있지만 전통적으로 확장성과 희박성 등의 문제점뿐 만 아니라 사용자-상품 매트릭스의 선호도만을 이용하여 추천을 함으로써 사용자의 정성적이고 감성적인 정보를 추천과정에 반영하지 못한다는 한계점이 있다. 본 연구에서 제안하는 추천시스템은 사회연결망분석에서 중심성 분석과 함께 연결망 내의 주요개체를 탐지할 수 있는 구조적 공백 분석을 이용하여 연결망 내의 대표 사용자들을 추출한 후 이들을 중심으로 군집을 형성한 후 각 군집색인 협업필터링을 수행하는 과정을 통해 전통적인 협업필터링에서 반영하지 못했던 정성적, 감성적 정보를 반영한다. 한편, 군집색인 협업필터링을 수행함으로써 추천의 효율성을 높일 수 있는 장점도 있다. 본 연구에서는 실제 사용자들의 상품에 대한 선호도 평가점수와 사용자들의 사회연결망 정보를 수집하여 실험을 수행하고 전통적인 협업필터링과 다양한 형태의 협업필터링과의 추천성과 비교를 통하여 제안하는 시스템의 유용성을 확인한다. 비교모형으로는 전통적인 협업필터링, 임의 군집색인 기반 협업필터링, k평균 군집색인 기반 협업필터링을 이용한 추천시스템이며, 실험 결과, 제안한 모형이 다른 비교모형에 비해 추천성과의 정확도가 가장 우수하였다. 추천성과의 차이에 대한 통계적 유의성 검정 결과, 제안 모형은 전통적인 협업필터링 기반의 추천시스템과는 통계적으로 유의한 성과 차이가 없었으나, 다른 두 모형에 대해서는 통계적으로 유의한 성과의 차이가 있는 것으로 나타났다.
Cite this article
JIIS Style
Kim, M., and K.-j. Kim, "Recommender Systems using Structural Hole and Collaborative Filtering", Journal of Intelligence and Information Systems, Vol. 20, No. 4 (2014), 107~120.

IEEE Style
Mingun Kim, and Kyoung-jae Kim, "Recommender Systems using Structural Hole and Collaborative Filtering", Journal of Intelligence and Information Systems, vol. 20, no. 4, pp. 107~120, 2014.

ACM Style
Kim, M., and Kim, K.-j., 2014. Recommender Systems using Structural Hole and Collaborative Filtering. Journal of Intelligence and Information Systems. 20, 4, 107--120.
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author = {Kim, Mingun and Kim, Kyoung-jae},
title = {Recommender Systems using Structural Hole and Collaborative Filtering},
journal = {Journal of Intelligence and Information Systems},
issue_date = {December 2014},
volume = {20},
number = {4},
month = Dec,
year = {2014},
issn = {2288-4866},
pages = {107--120},
url = { },
doi = {},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Structural hole; Collaborative filtering; Cluster-indexing; Social network analysis; Recommender system },
%0 Journal Article
%1 596
%A Mingun Kim
%A Kyoung-jae Kim
%T Recommender Systems using Structural Hole and Collaborative Filtering
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 20
%N 4
%P 107-120
%D 2014
%I Korea Intelligent Information System Society