A deep learning-based hybrid model for recommendation generation and ranking

A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastica...

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Autores:
Sivaramakrishnan, N.
Subramaniyaswamy, V.
amelec, viloria
Vijayakumar, V.
Senthilselvan, N.
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/6456
Acceso en línea:
https://hdl.handle.net/11323/6456
https://doi.org/10.1007/s00521-020-04844-4(0123456789().,-volV)(0123456789(). ,- volV)
https://repositorio.cuc.edu.co/
Palabra clave:
Deep learning
Optimization
Side information
Hybrid model
Recommendation system
Collaborative filtering
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_045a8a1c6545a2b55eec586d6fac591e
oai_identifier_str oai:repositorio.cuc.edu.co:11323/6456
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv A deep learning-based hybrid model for recommendation generation and ranking
title A deep learning-based hybrid model for recommendation generation and ranking
spellingShingle A deep learning-based hybrid model for recommendation generation and ranking
Deep learning
Optimization
Side information
Hybrid model
Recommendation system
Collaborative filtering
title_short A deep learning-based hybrid model for recommendation generation and ranking
title_full A deep learning-based hybrid model for recommendation generation and ranking
title_fullStr A deep learning-based hybrid model for recommendation generation and ranking
title_full_unstemmed A deep learning-based hybrid model for recommendation generation and ranking
title_sort A deep learning-based hybrid model for recommendation generation and ranking
dc.creator.fl_str_mv Sivaramakrishnan, N.
Subramaniyaswamy, V.
amelec, viloria
Vijayakumar, V.
Senthilselvan, N.
dc.contributor.author.spa.fl_str_mv Sivaramakrishnan, N.
Subramaniyaswamy, V.
amelec, viloria
Vijayakumar, V.
Senthilselvan, N.
dc.subject.spa.fl_str_mv Deep learning
Optimization
Side information
Hybrid model
Recommendation system
Collaborative filtering
topic Deep learning
Optimization
Side information
Hybrid model
Recommendation system
Collaborative filtering
description A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional way of performing matrix factorization (MF) and also applying deep learning (DL) techniques in recent years. By using DL in the recommender system, we can overcome the difficulties of collaborative filtering. DL now focuses mainly on modeling content descriptions, but those models ignore the main factor of user–item interaction. In the proposed hybrid Bayesian stacked auto-denoising encoder (HBSADE) model, it recognizes the latent interests of the user and analyzes contextual reviews that are performed through the MF method. The objective of the model is to identify the user’s point of interest, recommending products/services based on the user’s latent interests. The proposed two-stage novel hybrid deep learning-based collaborative filtering method explores the user’s point of interest, captures the communications between items and users and provides better recommendations in a personalized way. We used a multilayer neural network to manipulate the nonlinearities between the user and item communication from data. Experiments were to prove that our HBSADE outperforms existing methodologies over Amazon-b and Book-Crossing datasets.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-07-04T17:00:32Z
dc.date.available.none.fl_str_mv 2020-07-04T17:00:32Z
dc.date.issued.none.fl_str_mv 2020
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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https://doi.org/10.1007/s00521-020-04844-4(0123456789().,-volV)(0123456789(). ,- volV)
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dc.relation.references.spa.fl_str_mv 1. Hu QY, Zhao ZL, Wang CD, Lai JH (2017) An item orientated recommendation algorithm from the multi-view perspective. Neurocomputing 269:261–272
2. Hu QY, Huang L, Wang CD, Chao HY (2019) Item orientated recommendation by multi-view intact space learning with overlapping. Knowl-Based Syst 164:358–370
3. Zhang W, Zou H, Luo L, Liu Q, Wu W, Xiao W (2016) Predicting potential side effects of drugs by recommender methods and ensemble learning. Neurocomputing 173:979–987
4. Logesh R, Subramaniyaswamy V, Malathi D, Sivaramakrishnan N, Vijayakumar V (2019) Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Comput Appl. https://doi. org/10.1007/s00521-018-3891-5
5. Balabanovic M, Shoham Y (1997) Content-based, collaborative recommendation. Commun ACM 40(3):66–72
6. Sarwar BM, Karypis G, Konstan JA, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285–295
7. Zhao ZL, Wang CD, Lai JH (2016) AUI&GIV: recommendation with asymmetric user influence and global importance value. PLoS ONE 11(2):e0147944
8. Xue HJ, Dai X, Zhang J, Huang S, Chen J (2017) Deep matrix factorization models for recommender systems. In: IJCAI, pp 3203–3209
9. Burke RD (2002) Hybrid recommender systems: survey and experiments. User Model User-Adap Inter 12(4):331–370
10. Salakhutdinov R, Mnih A (2007) Probabilistic matrix factorization. In: NIPS, pp 1257–1264
11. Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In: KDD, pp 1235–1244
12. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408
13. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37. https:// doi.org/10.1109/MC.2009.263
14. Andreas M (2017) Matrix factorization techniques for recommender systems. Ph.D. thesis, The University of Aegean
15. Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on machine learning, pp 791–798
16. Gao J, Pantel P, Gamon M, He X, Deng L (2014) Modeling interestingness with deep neural networks. In: Proceedings of the conference on empirical methods natural language process, pp 2–13
17. Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the ninth ACM international conference on web search and data mining, pp 153–162
18. Kim D, Park C, Oh J, Lee S, Yu H (2016) Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM conference on recommender systems, pp 233–240
19. Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv (CSUR) 52(1):1–38
20. Miikkulainen R, Liang J, Meyerson E, Rawal A, Fink D, Francon O, Raju B, Shahrzad H, Navruzyan A, Duffy N, Hodjat B (2019) Evolving deep neural networks. Artificial intelligence in the age of neural networks and brain computing. Academic Press, London, pp 293–312
21. Van den Oord A, Dieleman S, Schrauwen B (2013) Deep contentbased music recommendation. In: Advances in neural information processing systems, pp 2643–2651
22. Bebis G, Michael G (1994) Feed-forward neural networks. IEEE Potentials 13(4):27–31
23. Zhang W, Du Y, Yoshida T, Yang Y (2019) DeepRec: a deep neural network approach to recommendation with item embedding and weighted loss function. Inf Sci 470:121–140
24. He X, Chua TS (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 355–364
25. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the conference on uncertainty in artificial intelligence, pp 452–461
26. Krestel R, Fankhauser P, Nejdl W (2009) Latent Dirichlet allocation for tag recommendation. In: Proceedings of the third ACM conference on recommender systems, pp 61–68
27. Ren X, Song M, Haihong E, Song J (2017) Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation. Neurocomputing 241(7):38–55
28. He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173–182
29. Sedhain S, Menon AK, Sanner S, Xie L (2015) Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on World Wide Web, pp 111–112
30. Wu X, Yuan X, Duan C, Wu J (2019) A novel collaborative filtering algorithm of machine learning by integrating restricted Boltzmann machine and trust information. Neural Comput Appl 31(9):4685–4692
31. Li S, Kawale J, Fu Y (2015) Deep collaborative filtering via marginalized denoising auto-encoder. In: Proceedings of the 24th ACM international on conference on information and knowledge management, pp 811–820
32. Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th international conference on machine learning, pp 880–887
33. Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1235–1244
34. Zhou W, Li J, Zhang M, Wang Y, Shah F (2018) Deep learning modeling for top-N recommendation with interests exploring. IEEE Access 6:51440–51455
35. Viloria A, Li J, Guiliany JG, de la Hoz B (2020) Predictive model for detecting customer’s purchasing behavior using data mining. In: Proceedings of 6th international conference on big data and cloud computing challenges, pp 45–54
36. Maind SB, Wankar P (2014) Research paper on basic of artificial neural network. Int J Recent Innov rends Comput Commun 2(1):96–100
37. Abedini F, Menhaj MB, Keyvanpour MR (2019) An MLP-based representation of neural tensor networks for the RDF data models. Neural Comput Appl 31(2):1135–1144
38. Hemanth DJ, Deperlioglu O, Kose U (2020) An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Comput Appl 32(3):707–721
39. Bell R, Volinsky C (2010) Matrix factorization for recommender systems. Presentation at UMBC
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spelling Sivaramakrishnan, N.Subramaniyaswamy, V.amelec, viloriaVijayakumar, V.Senthilselvan, N.2020-07-04T17:00:32Z2020-07-04T17:00:32Z2020https://hdl.handle.net/11323/6456https://doi.org/10.1007/s00521-020-04844-4(0123456789().,-volV)(0123456789(). ,- volV)Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional way of performing matrix factorization (MF) and also applying deep learning (DL) techniques in recent years. By using DL in the recommender system, we can overcome the difficulties of collaborative filtering. DL now focuses mainly on modeling content descriptions, but those models ignore the main factor of user–item interaction. In the proposed hybrid Bayesian stacked auto-denoising encoder (HBSADE) model, it recognizes the latent interests of the user and analyzes contextual reviews that are performed through the MF method. The objective of the model is to identify the user’s point of interest, recommending products/services based on the user’s latent interests. The proposed two-stage novel hybrid deep learning-based collaborative filtering method explores the user’s point of interest, captures the communications between items and users and provides better recommendations in a personalized way. We used a multilayer neural network to manipulate the nonlinearities between the user and item communication from data. Experiments were to prove that our HBSADE outperforms existing methodologies over Amazon-b and Book-Crossing datasets.Sivaramakrishnan, N.Subramaniyaswamy, V.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Vijayakumar, V.Senthilselvan, N.engCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Deep learningOptimizationSide informationHybrid modelRecommendation systemCollaborative filteringA deep learning-based hybrid model for recommendation generation and rankingArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Hu QY, Zhao ZL, Wang CD, Lai JH (2017) An item orientated recommendation algorithm from the multi-view perspective. Neurocomputing 269:261–2722. Hu QY, Huang L, Wang CD, Chao HY (2019) Item orientated recommendation by multi-view intact space learning with overlapping. Knowl-Based Syst 164:358–3703. Zhang W, Zou H, Luo L, Liu Q, Wu W, Xiao W (2016) Predicting potential side effects of drugs by recommender methods and ensemble learning. Neurocomputing 173:979–9874. Logesh R, Subramaniyaswamy V, Malathi D, Sivaramakrishnan N, Vijayakumar V (2019) Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Comput Appl. https://doi. org/10.1007/s00521-018-3891-55. Balabanovic M, Shoham Y (1997) Content-based, collaborative recommendation. Commun ACM 40(3):66–726. Sarwar BM, Karypis G, Konstan JA, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285–2957. Zhao ZL, Wang CD, Lai JH (2016) AUI&GIV: recommendation with asymmetric user influence and global importance value. PLoS ONE 11(2):e01479448. Xue HJ, Dai X, Zhang J, Huang S, Chen J (2017) Deep matrix factorization models for recommender systems. In: IJCAI, pp 3203–32099. Burke RD (2002) Hybrid recommender systems: survey and experiments. User Model User-Adap Inter 12(4):331–37010. Salakhutdinov R, Mnih A (2007) Probabilistic matrix factorization. In: NIPS, pp 1257–126411. Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In: KDD, pp 1235–124412. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–340813. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37. https:// doi.org/10.1109/MC.2009.26314. Andreas M (2017) Matrix factorization techniques for recommender systems. Ph.D. thesis, The University of Aegean15. Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on machine learning, pp 791–79816. Gao J, Pantel P, Gamon M, He X, Deng L (2014) Modeling interestingness with deep neural networks. In: Proceedings of the conference on empirical methods natural language process, pp 2–1317. Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the ninth ACM international conference on web search and data mining, pp 153–16218. Kim D, Park C, Oh J, Lee S, Yu H (2016) Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM conference on recommender systems, pp 233–24019. Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv (CSUR) 52(1):1–3820. Miikkulainen R, Liang J, Meyerson E, Rawal A, Fink D, Francon O, Raju B, Shahrzad H, Navruzyan A, Duffy N, Hodjat B (2019) Evolving deep neural networks. Artificial intelligence in the age of neural networks and brain computing. Academic Press, London, pp 293–31221. Van den Oord A, Dieleman S, Schrauwen B (2013) Deep contentbased music recommendation. In: Advances in neural information processing systems, pp 2643–265122. Bebis G, Michael G (1994) Feed-forward neural networks. IEEE Potentials 13(4):27–3123. Zhang W, Du Y, Yoshida T, Yang Y (2019) DeepRec: a deep neural network approach to recommendation with item embedding and weighted loss function. Inf Sci 470:121–14024. He X, Chua TS (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 355–36425. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the conference on uncertainty in artificial intelligence, pp 452–46126. Krestel R, Fankhauser P, Nejdl W (2009) Latent Dirichlet allocation for tag recommendation. In: Proceedings of the third ACM conference on recommender systems, pp 61–6827. Ren X, Song M, Haihong E, Song J (2017) Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation. Neurocomputing 241(7):38–5528. He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173–18229. Sedhain S, Menon AK, Sanner S, Xie L (2015) Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on World Wide Web, pp 111–11230. Wu X, Yuan X, Duan C, Wu J (2019) A novel collaborative filtering algorithm of machine learning by integrating restricted Boltzmann machine and trust information. Neural Comput Appl 31(9):4685–469231. Li S, Kawale J, Fu Y (2015) Deep collaborative filtering via marginalized denoising auto-encoder. In: Proceedings of the 24th ACM international on conference on information and knowledge management, pp 811–82032. Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th international conference on machine learning, pp 880–88733. Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1235–124434. Zhou W, Li J, Zhang M, Wang Y, Shah F (2018) Deep learning modeling for top-N recommendation with interests exploring. IEEE Access 6:51440–5145535. Viloria A, Li J, Guiliany JG, de la Hoz B (2020) Predictive model for detecting customer’s purchasing behavior using data mining. In: Proceedings of 6th international conference on big data and cloud computing challenges, pp 45–5436. Maind SB, Wankar P (2014) Research paper on basic of artificial neural network. Int J Recent Innov rends Comput Commun 2(1):96–10037. Abedini F, Menhaj MB, Keyvanpour MR (2019) An MLP-based representation of neural tensor networks for the RDF data models. Neural Comput Appl 31(2):1135–114438. Hemanth DJ, Deperlioglu O, Kose U (2020) An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Comput Appl 32(3):707–72139. Bell R, Volinsky C (2010) Matrix factorization for recommender systems. 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