Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach
Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who interacted with or rated the target item (collaborative filtering). To combine these two filtering approaches, current mo...
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| Vydáno v: | Knowledge-based systems Ročník 136; s. 37 - 45 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Amsterdam
Elsevier B.V
15.11.2017
Elsevier Science Ltd |
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who interacted with or rated the target item (collaborative filtering). To combine these two filtering approaches, current model-based hybrid recommendation systems typically require extensive feature engineering to construct a user profile. Statistical Relational Learning (SRL) provides a straightforward way to combine the two approaches through its ability to directly represent the probabilistic dependencies among the attributes of related objects. However, due to the large scale of the data used in real world recommendation systems, little research exists on applying SRL models to hybrid recommendation systems, and essentially none of that research has been applied to real big-data-scale systems. In this paper, we proposed a way to adapt the state-of-the-art in SRL approaches to construct a real hybrid job recommendation system. Furthermore, in order to satisfy a common requirement in recommendation systems (i.e. that false positives are more undesirable and therefore should be penalized more harshly than false negatives), our approach can also allow tuning the trade-off between the precision and recall of the system in a principled way. Our experimental results demonstrate the efficiency of our proposed approach as well as its improved performance on recommendation precision. |
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| AbstractList | Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who interacted with or rated the target item (collaborative filtering). To combine these two filtering approaches, current model-based hybrid recommendation systems typically require extensive feature engineering to construct a user profile. Statistical Relational Learning (SRL) provides a straightforward way to combine the two approaches through its ability to directly represent the probabilistic dependencies among the attributes of related objects. However, due to the large scale of the data used in real world recommendation systems, little research exists on applying SRL models to hybrid recommendation systems, and essentially none of that research has been applied to real big-data-scale systems. In this paper, we proposed a way to adapt the state-of-the-art in SRL approaches to construct a real hybrid job recommendation system. Furthermore, in order to satisfy a common requirement in recommendation systems (i.e. that false positives are more undesirable and therefore should be penalized more harshly than false negatives), our approach can also allow tuning the trade-off between the precision and recall of the system in a principled way. Our experimental results demonstrate the efficiency of our proposed approach as well as its improved performance on recommendation precision. |
| Author | AlJadda, Khalifeh Grainger, Trey Natarajan, Sriraam Yang, Shuo Korayem, Mohammed |
| Author_xml | – sequence: 1 givenname: Shuo surname: Yang fullname: Yang, Shuo email: shuoyang@indiana.edu organization: Indiana University, Bloomington, IN, USA – sequence: 2 givenname: Mohammed surname: Korayem fullname: Korayem, Mohammed organization: CareerBuilder, Norcross, GA, USA – sequence: 3 givenname: Khalifeh surname: AlJadda fullname: AlJadda, Khalifeh organization: CareerBuilder, Norcross, GA, USA – sequence: 4 givenname: Trey surname: Grainger fullname: Grainger, Trey organization: CareerBuilder, Norcross, GA, USA – sequence: 5 givenname: Sriraam surname: Natarajan fullname: Natarajan, Sriraam organization: Indiana University, Bloomington, IN, USA |
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| Cites_doi | 10.1007/978-3-319-19033-4_34 10.2307/41703509 10.1155/2009/421425 10.1145/2987538.2987544 10.2139/ssrn.906513 10.1016/j.dss.2015.03.008 10.1109/TKDE.2005.99 10.1002/int.20495 10.1016/S0004-3702(98)00034-4 10.1145/245108.245124 10.1214/aos/1013203451 10.7551/mitpress/7432.001.0001 10.1016/j.dss.2016.05.002 10.1023/A:1007369909943 10.1007/s10994-011-5244-9 10.1016/j.ijar.2010.04.001 |
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| Keywords | Cost-sensitive learning Collaborative filtering Statistical Relational Learning Content-based filtering Recommendation system |
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| References | Neville, Jensen (bib0037) 2007; 8 Sutton, McAllester, Singh, Mansour (bib0040) 2000 Gao, Qi, Liu, Liu (bib0022) 2007; vol.1 Karwath, Kersting, Landwehr (bib0039) 2008 Getoor, Taskar (bib0006) 2007 Lu, El Helou, Gillet (bib0033) 2013 Malherbe, Diaby, Cataldi, Viennet, Aufaure (bib0030) 2014 Balabanović, Shoham (bib0026) 1997; 40 Yang, Khot, Kersting, Kunapuli, Hauser, Natarajan (bib0009) 2014 Guo, Jerbi, O’Mahony (bib0027) 2014 Si, Jin (bib0018) 2003 AlJadda, Korayem, Grainger, Russell (bib0002) 2014 Natarajan, Joshi, Tadepalli, Kristian, Shavlik (bib0041) 2011 Wang, Zhang, Lu (bib0015) 2016; 87 O.M. Salazar, J.C. Jaramillo, D.A. Ovalle, J.A. Guzmán, A Case-Based Multi-Agent and Recommendation Environment to Improve the E-Recruitment Process, Springer International Publishing, Cham, pp. 389–397. Javed, Luo, McNair, Jacob, Zhao, Kang (bib0043) 2015 Blockeel, Raedt (bib0042) 1998; 101 Adomavicius, Tuzhilin (bib0010) 2005; 17 De Campos, Fernández-Luna, Huete, Rueda-Morales (bib0025) 2010; 51 Resnick, Iacovou, Suchak, Bergstrom, Riedl (bib0016) 1994 Sahoo, Singh, Mukhopadhyay (bib0019) 2012; 36 Shambour, Lu (bib0024) 2011; 26 Diaby, Viennet, Launay (bib0031) 2013 Friedman (bib0038) 2001; 29 Getoor, Sahami (bib0020) 1999 Salakhutdinov, Mnih, Hinton (bib0017) 2007 Natarajan, Khot, Kersting, Gutmann, Shavlik (bib0008) 2012; 86 Rao, Yu, Ravikumar, Dhillon (bib0014) 2015 Salton (bib0011) 1989 Basilico, Hofmann (bib0005) 2004 Basu, Hirsh, Cohen (bib0003) 1998 Su, Khoshgoftaar (bib0013) 2009; 2009 Hoxha, Rettinger (bib0035) 2013 Z. Huang, D.D. Zeng, H. Chen, A unified recommendation framework based on probabilistic relational models, Available at SSRN 906513 (2005). Lu, Wu, Mao, Wang, Zhang (bib0001) 2015; 74 Rocchio (bib0012) 1971 Breese, Heckerman, Kadie (bib0004) 1998 Pazzani, Billsus (bib0007) 1997; 27 Kok, Sumner, Richardson, Singla, Poon, Lowd, Wang, Domingos (bib0044) 2009 A. Pacuk, P. Sankowski, K. Wegrzycki, A. Witkowski, P. Wygocki, Recsys challenge 2016: job recommendations based on preselection of offers and gradient boosting, CoRR abs/1612.00959(2016). Newton, Greiner (bib0021) 2004 Almalis, Tsihrintzis, Karagiannis, Strati (bib0028) 2015 Hong, Zheng, Wang (bib0032) 2013 Perlich, Provost (bib0036) 2003 AlJadda (10.1016/j.knosys.2017.08.017_bib0002) 2014 10.1016/j.knosys.2017.08.017_bib0034 Neville (10.1016/j.knosys.2017.08.017_bib0037) 2007; 8 Blockeel (10.1016/j.knosys.2017.08.017_bib0042) 1998; 101 Rocchio (10.1016/j.knosys.2017.08.017_bib0012) 1971 Breese (10.1016/j.knosys.2017.08.017_bib0004) 1998 Getoor (10.1016/j.knosys.2017.08.017_bib0020) 1999 Lu (10.1016/j.knosys.2017.08.017_bib0001) 2015; 74 Guo (10.1016/j.knosys.2017.08.017_bib0027) 2014 Friedman (10.1016/j.knosys.2017.08.017_bib0038) 2001; 29 Balabanović (10.1016/j.knosys.2017.08.017_bib0026) 1997; 40 Lu (10.1016/j.knosys.2017.08.017_bib0033) 2013 Resnick (10.1016/j.knosys.2017.08.017_bib0016) 1994 Adomavicius (10.1016/j.knosys.2017.08.017_bib0010) 2005; 17 Su (10.1016/j.knosys.2017.08.017_bib0013) 2009; 2009 Almalis (10.1016/j.knosys.2017.08.017_bib0028) 2015 Natarajan (10.1016/j.knosys.2017.08.017_bib0041) 2011 10.1016/j.knosys.2017.08.017_bib0023 Gao (10.1016/j.knosys.2017.08.017_bib0022) 2007; vol.1 Salton (10.1016/j.knosys.2017.08.017_bib0011) 1989 Javed (10.1016/j.knosys.2017.08.017_bib0043) 2015 Getoor (10.1016/j.knosys.2017.08.017_bib0006) 2007 Yang (10.1016/j.knosys.2017.08.017_bib0009) 2014 De Campos (10.1016/j.knosys.2017.08.017_bib0025) 2010; 51 Newton (10.1016/j.knosys.2017.08.017_bib0021) 2004 Basu (10.1016/j.knosys.2017.08.017_bib0003) 1998 Diaby (10.1016/j.knosys.2017.08.017_bib0031) 2013 Hong (10.1016/j.knosys.2017.08.017_bib0032) 2013 Shambour (10.1016/j.knosys.2017.08.017_bib0024) 2011; 26 Kok (10.1016/j.knosys.2017.08.017_bib0044) 2009 Pazzani (10.1016/j.knosys.2017.08.017_bib0007) 1997; 27 Sutton (10.1016/j.knosys.2017.08.017_bib0040) 2000 Karwath (10.1016/j.knosys.2017.08.017_bib0039) 2008 Salakhutdinov (10.1016/j.knosys.2017.08.017_bib0017) 2007 Si (10.1016/j.knosys.2017.08.017_bib0018) 2003 Sahoo (10.1016/j.knosys.2017.08.017_bib0019) 2012; 36 Rao (10.1016/j.knosys.2017.08.017_bib0014) 2015 Wang (10.1016/j.knosys.2017.08.017_bib0015) 2016; 87 10.1016/j.knosys.2017.08.017_bib0029 Perlich (10.1016/j.knosys.2017.08.017_bib0036) 2003 Basilico (10.1016/j.knosys.2017.08.017_bib0005) 2004 Natarajan (10.1016/j.knosys.2017.08.017_bib0008) 2012; 86 Hoxha (10.1016/j.knosys.2017.08.017_bib0035) 2013 Malherbe (10.1016/j.knosys.2017.08.017_bib0030) 2014 |
| References_xml | – volume: 27 start-page: 313 year: 1997 end-page: 331 ident: bib0007 article-title: Learning and revising user profiles: the identification ofinteresting web sites publication-title: Mach. Learn. – start-page: 313 year: 1971 end-page: 323 ident: bib0012 article-title: Relevance Feedback in Information Retrieval publication-title: The SMART Retrieval System: Experiments in Automatic Document Processing – year: 2008 ident: bib0039 article-title: Boosting relational sequence alignments publication-title: ICDM – year: 2000 ident: bib0040 article-title: Policy gradient methods for reinforcement learning with function approximation publication-title: NIPS – year: 1999 ident: bib0020 article-title: Using probabilistic relational models for collaborative filtering publication-title: Workshop on Web Usage Analysis and User Profiling (WEBKDD’99) – start-page: 588 year: 2014 end-page: 595 ident: bib0030 article-title: Field selection for job categorization and recommendation to social network users publication-title: Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on – year: 2004 ident: bib0021 article-title: Hierarchical probabilistic relational models for collaborative filtering publication-title: Workshop on Statistical Relational Learning, 21st International Conference on Machine Learning – volume: vol.1 start-page: 67 year: 2007 end-page: 71 ident: bib0022 article-title: A recommendation algorithm combining user grade-based collaborative filtering and probabilistic relational models publication-title: Fourth International Conference on Fuzzy Systems and Knowledge Discovery – year: 2014 ident: bib0027 article-title: An analysis framework for content-based job recommendation. publication-title: 22nd International Conference on Case-Based Reasoning (ICCBR) – start-page: 1085 year: 2014 end-page: 1090 ident: bib0009 article-title: Learning from imbalanced data in relational domains: A soft margin approach publication-title: 2014 IEEE International Conference on Data Mining, ICDM 2014 – volume: 17 start-page: 734 year: 2005 end-page: 749 ident: bib0010 article-title: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions publication-title: IEEE Trans. Knowl. Data Eng. – start-page: 175 year: 1994 end-page: 186 ident: bib0016 article-title: Grouplens: An Open Architecture for Collaborative Filtering of Netnews – volume: 51 start-page: 785 year: 2010 end-page: 799 ident: bib0025 article-title: Combining content-based and collaborative recommendations: a hybrid approach based on bayesian networks publication-title: Int. J. Approximate Reasoning – start-page: 1499 year: 2013 end-page: 1503 ident: bib0032 article-title: Dynamic user profile-based job recommender system publication-title: Computer Science Education (ICCSE), 2013 8th International Conference on – year: 2004 ident: bib0005 article-title: Unifying collaborative and content-based filtering publication-title: Proceedings of the Twenty-first International Conference on Machine Learning – start-page: 808 year: 2014 end-page: 815 ident: bib0002 article-title: Crowd sourced query augmentation through semantic discovery of domain-specific jargon publication-title: 2014 IEEE International Conference on Big Data – start-page: 714 year: 1998 end-page: 720 ident: bib0003 article-title: Recommendation as classification: using social and content-based information in recommendation publication-title: Fifteenth National Conference on Artificial Intelligence – start-page: 963 year: 2013 end-page: 966 ident: bib0033 article-title: A recommender system for job seeking and recruiting website publication-title: Proceedings of the 22Nd International Conference on World Wide Web – reference: Z. Huang, D.D. Zeng, H. Chen, A unified recommendation framework based on probabilistic relational models, Available at SSRN 906513 (2005). – start-page: 1 year: 2015 end-page: 7 ident: bib0028 article-title: Fodra — a new content-based job recommendation algorithm for job seeking and recruiting publication-title: Information, Intelligence, Systems and Applications (IISA), 2015 6th International Conference on – start-page: 704 year: 2003 end-page: 711 ident: bib0018 article-title: Flexible mixture model for collaborative filtering. publication-title: ICML – volume: 8 start-page: 653 year: 2007 end-page: 692 ident: bib0037 article-title: Relational dependency networks publication-title: J. Mach. Learn. Res. – start-page: 43 year: 1998 end-page: 52 ident: bib0004 article-title: Empirical analysis of predictive algorithms for collaborative filtering publication-title: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence – start-page: 167 year: 2003 end-page: 176 ident: bib0036 article-title: Aggregation-based feature invention and relational concept classes publication-title: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – volume: 87 start-page: 80 year: 2016 end-page: 93 ident: bib0015 article-title: Member contribution-based group recommender system publication-title: Decis. Support. Syst. – volume: 26 start-page: 814 year: 2011 end-page: 843 ident: bib0024 article-title: A hybrid trust-enhanced collaborative filtering recommendation approach for personalized government-to-business e-services publication-title: Int. J. Intell. Syst. – volume: 74 start-page: 12 year: 2015 end-page: 32 ident: bib0001 article-title: Recommender system application developments: a survey publication-title: Decis. Support Syst. – start-page: 821 year: 2013 end-page: 828 ident: bib0031 article-title: Toward the next generation of recruitment tools: an online social network-based job recommender system publication-title: Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on – volume: 101 start-page: 285 year: 1998 end-page: 297 ident: bib0042 article-title: Top-down induction of first-order logical decision trees publication-title: Artif. Intell. – year: 2007 ident: bib0006 article-title: Introduction to statistical relational learning publication-title: Adaptive Computation and Machine Learning – start-page: 286 year: 2015 end-page: 293 ident: bib0043 article-title: Carotene: a job title classification system for the online recruitment domain publication-title: IEEE First International Conference on Big Data Computing Service and Applications – volume: 40 start-page: 66 year: 1997 end-page: 72 ident: bib0026 article-title: Fab: content-based, collaborative recommendation publication-title: Commun. ACM – year: 2009 ident: bib0044 article-title: The Alchemy System for Statistical Relational AI publication-title: Technical Report – reference: A. Pacuk, P. Sankowski, K. Wegrzycki, A. Witkowski, P. Wygocki, Recsys challenge 2016: job recommendations based on preselection of offers and gradient boosting, CoRR abs/1612.00959(2016). – start-page: 791 year: 2007 end-page: 798 ident: bib0017 article-title: Restricted boltzmann machines for collaborative filtering publication-title: Proceedings of the 24th International Conference on Machine Learning – year: 1989 ident: bib0011 article-title: Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer – start-page: 133 year: 2013 end-page: 139 ident: bib0035 article-title: First-order probabilistic model for hybrid recommendations publication-title: 12th International Conference on Machine Learning and Applications, ICMLA 2013 – year: 2011 ident: bib0041 article-title: Imitation learning in relational domains: a functional-gradient boosting approach publication-title: IJCAI – volume: 2009 start-page: 4:2 year: 2009 ident: bib0013 article-title: A survey of collaborative filtering techniques publication-title: Adv. in Artif. Intell. – volume: 86 start-page: 25 year: 2012 end-page: 56 ident: bib0008 article-title: Gradient-based boosting for statistical relational learning: the relational dependency network case publication-title: Mach. Learn. – reference: O.M. Salazar, J.C. Jaramillo, D.A. Ovalle, J.A. Guzmán, A Case-Based Multi-Agent and Recommendation Environment to Improve the E-Recruitment Process, Springer International Publishing, Cham, pp. 389–397. – start-page: 2107 year: 2015 end-page: 2115 ident: bib0014 article-title: Collaborative Filtering with Graph Information: Consistency and Scalable Methods publication-title: Advances in Neural Information Processing Systems 28 – volume: 36 start-page: 1329 year: 2012 end-page: 1356 ident: bib0019 article-title: A hidden Markov model for collaborative filtering publication-title: Manag. Inf. Syst. Q. – volume: 29 start-page: 1189 year: 2001 end-page: 1232 ident: bib0038 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann. Stat. – year: 1999 ident: 10.1016/j.knosys.2017.08.017_bib0020 article-title: Using probabilistic relational models for collaborative filtering – ident: 10.1016/j.knosys.2017.08.017_bib0029 doi: 10.1007/978-3-319-19033-4_34 – volume: 8 start-page: 653 year: 2007 ident: 10.1016/j.knosys.2017.08.017_bib0037 article-title: Relational dependency networks publication-title: J. Mach. Learn. Res. – year: 2004 ident: 10.1016/j.knosys.2017.08.017_bib0005 article-title: Unifying collaborative and content-based filtering – year: 2014 ident: 10.1016/j.knosys.2017.08.017_bib0027 article-title: An analysis framework for content-based job recommendation. – volume: 36 start-page: 1329 issue: 4 year: 2012 ident: 10.1016/j.knosys.2017.08.017_bib0019 article-title: A hidden Markov model for collaborative filtering publication-title: Manag. Inf. Syst. Q. doi: 10.2307/41703509 – start-page: 133 year: 2013 ident: 10.1016/j.knosys.2017.08.017_bib0035 article-title: First-order probabilistic model for hybrid recommendations – volume: 2009 start-page: 4:2 year: 2009 ident: 10.1016/j.knosys.2017.08.017_bib0013 article-title: A survey of collaborative filtering techniques publication-title: Adv. in Artif. Intell. doi: 10.1155/2009/421425 – year: 2004 ident: 10.1016/j.knosys.2017.08.017_bib0021 article-title: Hierarchical probabilistic relational models for collaborative filtering – year: 2011 ident: 10.1016/j.knosys.2017.08.017_bib0041 article-title: Imitation learning in relational domains: a functional-gradient boosting approach – ident: 10.1016/j.knosys.2017.08.017_bib0034 doi: 10.1145/2987538.2987544 – ident: 10.1016/j.knosys.2017.08.017_bib0023 doi: 10.2139/ssrn.906513 – volume: 74 start-page: 12 year: 2015 ident: 10.1016/j.knosys.2017.08.017_bib0001 article-title: Recommender system application developments: a survey publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2015.03.008 – volume: 17 start-page: 734 issue: 6 year: 2005 ident: 10.1016/j.knosys.2017.08.017_bib0010 article-title: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2005.99 – volume: 26 start-page: 814 issue: 9 year: 2011 ident: 10.1016/j.knosys.2017.08.017_bib0024 article-title: A hybrid trust-enhanced collaborative filtering recommendation approach for personalized government-to-business e-services publication-title: Int. J. Intell. Syst. doi: 10.1002/int.20495 – year: 2008 ident: 10.1016/j.knosys.2017.08.017_bib0039 article-title: Boosting relational sequence alignments – volume: 101 start-page: 285 year: 1998 ident: 10.1016/j.knosys.2017.08.017_bib0042 article-title: Top-down induction of first-order logical decision trees publication-title: Artif. Intell. doi: 10.1016/S0004-3702(98)00034-4 – start-page: 791 year: 2007 ident: 10.1016/j.knosys.2017.08.017_bib0017 article-title: Restricted boltzmann machines for collaborative filtering – year: 2009 ident: 10.1016/j.knosys.2017.08.017_bib0044 article-title: The Alchemy System for Statistical Relational AI – start-page: 704 year: 2003 ident: 10.1016/j.knosys.2017.08.017_bib0018 article-title: Flexible mixture model for collaborative filtering. – year: 2000 ident: 10.1016/j.knosys.2017.08.017_bib0040 article-title: Policy gradient methods for reinforcement learning with function approximation – start-page: 1085 year: 2014 ident: 10.1016/j.knosys.2017.08.017_bib0009 article-title: Learning from imbalanced data in relational domains: A soft margin approach – year: 1989 ident: 10.1016/j.knosys.2017.08.017_bib0011 – volume: 40 start-page: 66 issue: 3 year: 1997 ident: 10.1016/j.knosys.2017.08.017_bib0026 article-title: Fab: content-based, collaborative recommendation publication-title: Commun. ACM doi: 10.1145/245108.245124 – start-page: 821 year: 2013 ident: 10.1016/j.knosys.2017.08.017_bib0031 article-title: Toward the next generation of recruitment tools: an online social network-based job recommender system – volume: 29 start-page: 1189 year: 2001 ident: 10.1016/j.knosys.2017.08.017_bib0038 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann. Stat. doi: 10.1214/aos/1013203451 – start-page: 175 year: 1994 ident: 10.1016/j.knosys.2017.08.017_bib0016 – year: 2007 ident: 10.1016/j.knosys.2017.08.017_bib0006 article-title: Introduction to statistical relational learning doi: 10.7551/mitpress/7432.001.0001 – start-page: 2107 year: 2015 ident: 10.1016/j.knosys.2017.08.017_bib0014 article-title: Collaborative Filtering with Graph Information: Consistency and Scalable Methods – start-page: 588 year: 2014 ident: 10.1016/j.knosys.2017.08.017_bib0030 article-title: Field selection for job categorization and recommendation to social network users – volume: 87 start-page: 80 year: 2016 ident: 10.1016/j.knosys.2017.08.017_bib0015 article-title: Member contribution-based group recommender system publication-title: Decis. Support. Syst. doi: 10.1016/j.dss.2016.05.002 – start-page: 1 year: 2015 ident: 10.1016/j.knosys.2017.08.017_bib0028 article-title: Fodra — a new content-based job recommendation algorithm for job seeking and recruiting – start-page: 313 year: 1971 ident: 10.1016/j.knosys.2017.08.017_bib0012 article-title: Relevance Feedback in Information Retrieval – start-page: 286 year: 2015 ident: 10.1016/j.knosys.2017.08.017_bib0043 article-title: Carotene: a job title classification system for the online recruitment domain – volume: 27 start-page: 313 issue: 3 year: 1997 ident: 10.1016/j.knosys.2017.08.017_bib0007 article-title: Learning and revising user profiles: the identification ofinteresting web sites publication-title: Mach. Learn. doi: 10.1023/A:1007369909943 – volume: 86 start-page: 25 issue: 1 year: 2012 ident: 10.1016/j.knosys.2017.08.017_bib0008 article-title: Gradient-based boosting for statistical relational learning: the relational dependency network case publication-title: Mach. Learn. doi: 10.1007/s10994-011-5244-9 – start-page: 808 year: 2014 ident: 10.1016/j.knosys.2017.08.017_bib0002 article-title: Crowd sourced query augmentation through semantic discovery of domain-specific jargon – volume: 51 start-page: 785 issue: 7 year: 2010 ident: 10.1016/j.knosys.2017.08.017_bib0025 article-title: Combining content-based and collaborative recommendations: a hybrid approach based on bayesian networks publication-title: Int. J. Approximate Reasoning doi: 10.1016/j.ijar.2010.04.001 – start-page: 714 year: 1998 ident: 10.1016/j.knosys.2017.08.017_bib0003 article-title: Recommendation as classification: using social and content-based information in recommendation – start-page: 167 year: 2003 ident: 10.1016/j.knosys.2017.08.017_bib0036 article-title: Aggregation-based feature invention and relational concept classes – start-page: 43 year: 1998 ident: 10.1016/j.knosys.2017.08.017_bib0004 article-title: Empirical analysis of predictive algorithms for collaborative filtering – volume: vol.1 start-page: 67 year: 2007 ident: 10.1016/j.knosys.2017.08.017_bib0022 article-title: A recommendation algorithm combining user grade-based collaborative filtering and probabilistic relational models – start-page: 1499 year: 2013 ident: 10.1016/j.knosys.2017.08.017_bib0032 article-title: Dynamic user profile-based job recommender system – start-page: 963 year: 2013 ident: 10.1016/j.knosys.2017.08.017_bib0033 article-title: A recommender system for job seeking and recruiting website |
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| SubjectTerms | Artificial intelligence Collaboration Collaborative filtering Content-based filtering Cost-sensitive learning Data management Filtration Human-computer interaction Hybrid systems Job hunting Learning Measures Recommendation system Recommender systems Relational learning Software engineering Statistical analysis Statistical Relational Learning |
| Title | Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach |
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