research-article

Metalearning and Recommender Systems

Authors:

Andr C.P.L.F. de CarvalhoAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 423, Issue C

Pages 128 - 144

https://doi.org/10.1016/j.ins.2017.09.050

Published: 01 January 2018 Publication History

Abstract

The problem of information overload motivated the appearance of Recommender Systems. From the several open problems in this area, the decision of which is the best recommendation algorithm for a specific problem is one of the most important and less studied. The current trend to solve this problem is the experimental evaluation of several recommendation algorithms in a handful of datasets. However, these studies require an extensive amount of computational resources, particularly processing time. To avoid these drawbacks, researchers have investigated the use of Metalearning to select the best recommendation algorithms in different scopes. Such studies allow to understand the relationships between data characteristics and the relative performance of recommendation algorithms, which can be used to select the best algorithm(s) for a new problem. The contributions of this study are two-fold: 1) to identify and discuss the key concepts of algorithm selection for recommendation algorithms via a systematic literature review and 2) to perform an experimental study on the Metalearning approaches reviewed in order to identify the most promising concepts for automatic selection of recommendation algorithms.

References

[1]

G. Adomavicius, R. Sankaranarayanan, S. Sen, A. Tuzhilin, Incorporating contextual information in recommender systems using a multidimensional approach, ACM Trans. Inf. Syst., 23 (2005) 103-145.

Digital Library

[2]

G. Adomavicius, J. Zhang, Impact of data characteristics on recommender systems performance, ACM Trans. Manag. Inf. Syst., 3 (2012) 1-17.

Digital Library

[3]

T. Bertin-Mahieux, D.P.W. Ellis, B. Whitman, P. Lamere, The million song dataset, 2011.

[4]

Y. Blanco-Fernndez, J. Pazos-Arias, A. Gil-solla, M. Ramos-Cabrer, Providing entertainment by content-based filtering and semantic reasoning in intelligent recommender systems, IEEE Trans. Consum. Electron., 54 (2008) 727-735.

Digital Library

[5]

J. Bobadilla, F. Ortega, A. Hernando, A. Gutirrez, Recommender systems survey, Knowl. Based Syst., 46 (2013) 109-132.

Digital Library

[6]

P. Brazdil, C. Giraud-Carrier, C. Soares, R. Vilalta, Metalearning: Applications to Data Mining, Springer, 2009.

Digital Library

[7]

R. Burke, Hybrid web recommender systems, The adaptive web (2007) 377-408.

[8]

P.G. Campos, F. Dez, I. Cantador, Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols, User Model User-Adapt. Interact., 24 (2013) 67-119.

Digital Library

[9]

T. Cunha, R.J.F. Rossetti, C. Soares, Analysing collaborative filtering algorithms in a multi-agent environment, 2014.

[10]

T. Cunha, C. Soares, A.C. de Carvalho, Selecting collaborative filtering algorithms using metalearning, 2016.

[11]

J. Demar, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res., 7 (2006) 1-30.

Digital Library

[12]

S. Dooms, T. De Pessemier, L. Martens, MovieTweetings: a Movie Rating Dataset Collected From Twitter, 2013.

[13]

M. Ekstrand, J. Riedl, When recommenders fail: predicting recommender failure for algorithm selection and combination, ACM Conf. Recommend. Syst. (2012) 233-236.

[14]

Z. Gantner, S. Rendle, C. Freudenthaler, L. Schmidt-Thieme, MyMediaLite: A Free Recommender System Library, 2011.

[15]

K. Goldberg, T. Roeder, D. Gupta, C. Perkins, Eigentaste: a constant time collaborative filtering algorithm, Inf. Retr., 4 (2001) 133-151.

Digital Library

[16]

J. Griffith, C. ORiordan, H. Sorensen, Investigations into user rating information and predictive accuracy in a collaborative filtering domain, 2012.

[17]

GroupLens, MovieLens datasets, 2016.

[18]

J.L. Herlocker, J.a. Konstan, L.G. Terveen, J.T. Riedl, Evaluating collaborative filtering recommender systems, ACM Trans. Inf. Syst., 22 (2004) 5-53.

Digital Library

[19]

Z. Huang, D.D. Zeng, Why does collaborative filtering work? transaction-based recommendation model validation and selection by analyzing bipartite random graphs, INFORMS, 23 (2011) 138-152.

Digital Library

[20]

D. Kim, C. Park, J. Oh, H. Yu, Deep hybrid recommender systems via exploiting document context and statistics of items, Inf. Sci., 417 (2017) 72-87.

Digital Library

[21]

Y. Koren, R. Bell, C. Volinsky, Matrix factorization techniques for recommender systems, Computer, 42 (2009) 30-37.

Digital Library

[22]

J. Liu, C. Sui, D. Deng, J. Wang, B. Feng, W. Liu, C. Wu, Representing conditional preference by boosted regression trees for recommendation, Inf. Sci., 327 (2016) 1-20.

Digital Library

[23]

J. Liu, C. Wu, W. Liu, Bayesian probabilistic matrix factorization with social relations and item contents for recommendation, Decis. Support Syst., 55 (2013) 838-850.

[24]

W. Liu, C. Wu, B. Feng, J. Liu, Conditional preference in recommender systems, Expert Syst. Appl., 42 (2015) 774-788.

Digital Library

[25]

L. L, M. Medo, C.H. Yeung, Y.-C. Zhang, Z.-K. Zhang, T. Zhou, Recommender systems, Phys. Rep., 519 (2012) 1-49.

[26]

P. Matuszyk, M. Spiliopoulou, Predicting the performance of collaborative filtering algorithms, 2014.

[27]

J. McAuley, J. Leskovec, Hidden factors and hidden topics: understanding rating dimensions with review text, 2013.

[28]

E.D. Michie, D.J. Spiegelhalter, C.C. Taylor, Machine learning, neural and statistical classification, 1994.

[29]

Netflix, Netflix prize data set (2009). URL:http://archive.ics.uci.edu/ml/datasets/Netflix+Prize.

[30]

F. Ortega, A. Hernando, J. Bobadilla, J.H. Kang, Recommending items to group of users using matrix factorization based collaborative filtering, Inf. Sci., 345 (2016) 313-324.

Digital Library

[31]

F. Pinto, C. Soares, J. Mendes-Moreira, Towards automatic generation of Metafeatures, 2016.

[32]

R.B. Prudncio, T.B. Ludermir, Meta-learning approaches to selecting time series models, Neurocomputing, 61 (2004) 121-137.

Digital Library

[33]

J. Rice, The algorithm selection problem, Adv. Comput., 15 (1976) 65-118.

[34]

M. Richardson, R. Agrawal, P. Domingos, Trust Management for the Semantic Web, pp. 351368.

[35]

A.L.D. Rossi, A.C.P.D.L.F. de Carvalho, C. Soares, B.F. de Souza, MetaStream: a meta-learning based method for periodic algorithm selection in time-changing data, Neurocomputing, 127 (2014) 52-64.

Digital Library

[36]

A. Said, A. Bellogn, Comparative recommender system evaluation: benchmarking recommendation frameworks, 2014.

[37]

B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Analysis of recommendation algorithms for e-commerce, 2000.

[38]

F. Serban, J. Vanschoren, A. Bernstein, A survey of intelligent assistants for data analysis, ACM Comput. Surv., V (2013) 1-35.

Digital Library

[39]

C. Soares, P.B. Brazdil, P. Kuba, A meta-learning method to select the kernel width in support vector regression, Mach. Learn., 54 (2004) 195-209.

Digital Library

[40]

H.E.A. Tinsley, S.D. Brown, Handbook of Applied Multivariate Statistics and Mathematical Modeling, Elsevier Science, 2000.

[41]

J. Vanschoren, Katholieke Universiteit Leuven, 2010.

[42]

H. Wang, Y. Lu, C. Zhai, Latent aspect rating analysis without aspect keyword supervision, 2011.

[43]

R. Wang, A. Liu, F. Yuan, The followee recommendation algorithm based on microblog user interest and characteristic, J. Inf. Comput. Sci., 11 (2014) 1585-1596.

[44]

C. Wu, J. Wang, J. Liu, W. Liu, Recurrent neural network based recommendation for time heterogeneous feedback, Knowl. Based Syst., 109 (2016) 90-103.

Digital Library

[45]

Yahoo!, Webscope datasets, 2016. URL:https://webscope.sandbox.yahoo.com/.

[46]

X. Yang, Y. Guo, Y. Liu, H. Steck, A survey of collaborative filtering based social recommender systems, Comput. Commun., 41 (2014) 1-10.

Digital Library

[47]

Yelp, Yelp Dataset Challenge, 2016. URL:https://www.yelp.com/dataset_challenge.

[48]

R. Zafarani, H. Liu, Social computing data repository at ASU, 2009. URL:http://socialcomputing.asu.edu.

[49]

A. Zapata, V.H. Menndez, M.E. Prieto, C. Romero, Evaluation and selection of group recommendation strategies for collaborative searching of learning objects, Int. J. Hum. Comput. Stud., 76 (2015) 22-39.

Digital Library

[50]

C.-N.C. Ziegler, S.M.S. McNee, J.a.J. Konstan, G. Lausen, Improving recommendation lists through topic diversification, 2005.

Cited By

Wegmeth LVente TBeel J(2024)Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback DatasetsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691718(1163-1167)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691718
Kim YHwangbo HLee HLee W(2024)Sequence aware recommenders for fashion E-commerceElectronic Commerce Research10.1007/s10660-022-09627-824:4(2733-2753)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10660-022-09627-8
Vente T(2023)Advancing Automation of Design Decisions in Recommender System PipelinesProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608886(1355-1360)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608886
Show More Cited By

Recommendations

A new similarity function for selecting neighbors for each target item in collaborative filtering

As one of the collaborative filtering (CF) techniques, memory-based CF technique which recommends items to users based on rating information of like-minded users (called neighbors) has been widely used and has also proven to be useful in many practices ...
Generating Items Recommendations by Fusing Content and User-Item based Collaborative Filtering
Abstract
Nowadays e-commerce has spread all over the world. The e-shops are not similar to the physical shops. The e-shops can have hundreds or thousands of items independent of physical boundaries. The information about all these products is available on ...
Metalearning for Context-aware Filtering: Selection of Tensor Factorization Algorithms
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

This work addresses the problem of selecting Tensor Factorization algorithms for the Context-aware Filtering recommendation task using a metalearning approach. The most important challenge of applying metalearning on new problems is the development of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 423, Issue C

January 2018

399 pages

ISSN:0020-0255

Issue’s Table of Contents

Copyright © Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 January 2018

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

23
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wegmeth LVente TBeel J(2024)Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback DatasetsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691718(1163-1167)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691718
Kim YHwangbo HLee HLee W(2024)Sequence aware recommenders for fashion E-commerceElectronic Commerce Research10.1007/s10660-022-09627-824:4(2733-2753)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10660-022-09627-8
Vente T(2023)Advancing Automation of Design Decisions in Recommender System PipelinesProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608886(1355-1360)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608886
Pipergias Analytis PHager P(2023)Collaborative filtering algorithms are prone to mainstream-taste biasProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608825(750-756)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608825
Yera RRodríguez RMartínez L(2023)Exploring the Automatic Selection of Aggregation Methods in Group RecommendationFuzzy Logic and Technology, and Aggregation Operators10.1007/978-3-031-39965-7_13(149-160)Online publication date: 4-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-39965-7_13
McElfresh DKhandagale SValverde JDickerson JWhite CKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)On the generalizability and predictability of recommender systemsProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3600589(4416-4432)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3600589
Bao LSun XGong DZhang Y(2022)Multisource Heterogeneous User-Generated Contents-Driven Interactive Estimation of Distribution Algorithms for Personalized SearchIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.310957626:5(844-858)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1109/TEVC.2021.3109576
Chen CLai PChen C(2022)ColdGAN: an effective cold-start recommendation system for new users based on generative adversarial networksApplied Intelligence10.1007/s10489-022-04005-153:7(8302-8317)Online publication date: 29-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-022-04005-1
Zheng WYan LGou CWang F(2022)Computational knowledge vision: paradigmatic knowledge based prescriptive learning and reasoning for perception and visionArtificial Intelligence Review10.1007/s10462-022-10166-955:8(5917-5952)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10462-022-10166-9
Theuerkauf RSeyffahrt TPeters R(2021)Improving Recommender Systems by Using Time-Weighted Sentiment AnalysisProceedings of the 5th International Conference on E-Commerce, E-Business and E-Government10.1145/3466029.3466057(15-19)Online publication date: 28-Apr-2021
https://dl.acm.org/doi/10.1145/3466029.3466057
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents