Browse > Article
http://dx.doi.org/10.13088/jiis.2018.24.2.085

A Hybrid Recommender System based on Collaborative Filtering with Selective Use of Overall and Multicriteria Ratings  

Ku, Min Jung (Seoksan Corporation)
Ahn, Hyunchul (Graduate School of Business IT, Kookmin University)
Publication Information
Journal of Intelligence and Information Systems / v.24, no.2, 2018 , pp. 85-109 More about this Journal
Abstract
Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.
Keywords
Recommender system; Hybrid recommender system; Multicriteria ratings; Collective filtering; Selective use;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Adomavicius, G., R. Sankaranarayanan, S. Sen, and A. Tuzhilin, "Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach," ACM Transactions on Information Systems, Vol. 23, No. 1(2005), 103-145.   DOI
2 Adomavicius, G. and Y. Kwon, "New recommendation techniques for multicriteria rating systems," IEEE Intelligent Systems, Vol. 22(2007), 48-55.   DOI
3 Adomavicius, G., N. Manouselis, and Y. Kwon, "Multi-criteria Recommender Systems," Recommender Systems Handbook. Springer(2011), 769-803.
4 Ahn, H., I. Han, and K. -J. Kim, "The Product Recommender System Combining Association Rules and Classification Models: The Case of G Internet Shopping Mall," Information Systems Review, Vol. 8, No. 1(2006), 181-201.
5 Ahn, S. -M., I. H. Kim, B. Choi, Y. Cho, E. Kim, and M. -K. Kim, "Understanding the Performance of Collaborative Filtering Recommendation through Social Network Analysis," Journal of Society for e-Business Studies, Vol. 17, No. 2(2012), 129-147.   DOI
6 Ahn, H., "Improvement of a Context-aware Recommender System through User's Emotional State Prediction," Journal of Information Technology Applications & Management, Vol. 21, No. 4(2014), 203-223.
7 Balabanovic, M. and Y. Shohm, "Fab : Content-Based, Collaborative Recommendation," Communications of the ACM, Vol. 40, No. 3(1997), 66-72.   DOI
8 Breese, J. S., D. Heckerman, and C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering", Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, UAI-98(1998), 43-52.
9 Choi, S., K. -Y. Kwahk, and H. Ahn, "Enhancing Predictive Accuracy of Collaborative Filtering Algorithms using the Network Analysis of Trust Relationship among Users", Journal of Intelligence and Information Systems, Vol. 22, No. 3(2016), 113-127.   DOI
10 Haubl, G. and V. Trifts, "Consumer Decision Making in Online Shopping Environments : The Effects of Interactive Decision Aids," Management Science, Vol. 19, No. 1(2000), 4-21.
11 Heo, Y. -K., J. -S. Oh, P. Paudel, P. Thapa, H. -J. Jeon, M. -A, Jeong, S. -R. Lee, "Density Based system for recommendation of Hybrid POI," The Institute of Electronics Engineers of Korea Summer Conference (2015), 1318-1322.
12 Herlocker, J. L., J. A. Konstan, A. Borchers, and J. Riedl, "An Algorithm Framework for Performing Collaborative Filtering," Proceedings of the 22nd Annual International ACMSIGIR Conference on Research and Development in information Retrieval(1999), 230-237.
13 Kim, K. -J., and H. Ahn, "Development of Web-based Intelligent Recommender Systems using Advanced Data Mining Techniques", Journal of Information Technology Applications and Management, Vol. 12, No. 3(2005), 42-56.
14 Jannach, D., Z. Karakaya, and F. Gedikli, "Accuracy Improvements for Multi-criteria Recommender Systems", Proceedings of the 13th ACM Conference on Electronic Commerce(2012), 674-689.
15 Jeon, B., and H. Ahn, "A Collaborative Filtering System Combined with Users Review Mining : Application to the Recommendation of Smartphone Apps," Journal of Intelligence and Information Systems, Vol. 21, No. 2(2015), 1-18.   DOI
16 Kermany. N. R., and S. H. Alizadeh, "A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques," Electronic Commerce Research and Applications, Vol. 21(2017), 50-64.   DOI
17 Kim, J. K., D. H. Ahn, and Y. H. Cho, "A Personalized Recommender System, WebCF-PT: A Collaborative Filtering using Web Mining and Product Taxonomy", Asia Pacific Journal of Information Systems, Vol. 15, No. 1(2005), 63-79.
18 Kim, K. -J., and H. Ahn, "User-Item Matrix Reduction Technique for Personalized Recommender Systems," Journal of Information Technology Applications & Management, Vol. 16, No.1(2009), 97-113.
19 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.   DOI
20 Konstan, J. A., B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl, "GroupLens : Applying Collaborative Filtering to Usenet News", Communication of the ACM, Vol. 40(1997), 77-87.   DOI
21 Nilashi, M., D. Jannach, O. bin Ibrahim, N. Ithnin, "Clustering-and regression-based multi-criteria collaborative filtering with incremental updates," Information Science, Vol. 293(2015), 235-250.   DOI
22 Li, Q., C. Wang, G. Geng, "Improving personalized services in mobile commerce by a novel multicriteria rating approach," Proceedings of the 17th International Conference on World Wide Web(2008), 1235-1236.
23 Liu, L., N. Mehandjiev, D. -L. Xu, "Multi-criteria service recommendation based on user criteria preferences," Proceedings of the fifth ACM Conference on Recommender Systems(2011), 77-84.
24 Nilashi, M., O. bin Ibrahim, N. Ithnin, "Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system," Knowledge-Based Systems, Vol. 60, (2014b), 82-101.   DOI
25 Park, M. -H, H. -S. Park, S. -B. Cho, "Restaurant Recommendation for Group of People in Mobile Environments Using Probabilistic Multi-criteria Decision Making," Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction, Springer Verlag(2008), 114-122.
26 Park, K.-S. and N. Moon, "Multidimensional Optimization Model of Music Recommender Systems," KIPS Transactions on Computer and Communication Systems, Vol. 19, No. 3(2012), 155-164.
27 Pazzani, M. J., "A framework for collaborative, content-based and demographic filtering', Artificial Intelligence Review, Vol. 13, Nos. 5-6(1999), 393-408.   DOI
28 Sahoo, N., R. Krishnan, G. Duncan, and J. P. Callan, "Collaborative filtering with multicomponent rating for recommender systems," Proceedings of the sixteenth annual workshop on information technologies and systems, Milwaukee, WI(2006).
29 Sarwar, B., G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms', Proceeding of the 10th International Conference on World Wide Web(2001), 285-295.
30 Sahoo, N., R. Krishnan, G. Duncan, and J. Callan, "The Halo Effect in Multi-component Ratings and its Implications for Recommender Systems: The case of Yahoo! Movies," Information Systems Research, Vol.23, No. 1(2012), 231-246.   DOI
31 Nilashi, M., O. bin Ibrahim, N. Ithnin, "Hybrid recommendation approaches for multi-criteria collaborative filtering," Expert Systems with Applications, Vol. 41(2014a), 3879-3900.   DOI
32 Schafer, J. B., J. A. Konstan, and J. Riedl, "E-Commerce Recommendation Applications," Data Mining and Knowledge Discovery, Vol. 5, No. 1-2(2001), 115-153.   DOI
33 Si, L., and R. Jin, "Flexible mixture model for collaborative filtering," Proceedings of the Twentieth International Conference on Machine Learning, ICML(2003), 704-711.
34 Son, J., S. B. Kim, H. Kim, and S. Cho, "Review and Analysis of Recommender systems," Journal of the Korean Institute of Industrial Engineers, Vol. 41, No. 2(2015), 185-208.   DOI
35 Zenebe, A., and A. F. Norcio, "Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems," Fuzzy Sets and Systems, Vol. 160(2009), 76-94.   DOI