A collaborative filtering method based on artificial immune network
A system is seriously required for helping users to find their path on the shopping and entertainment web sites where the amounts of on-line information vastly increase. Therefore, recommender systems, new type of internet based software tool, appeared, and became an appealing subject for researcher...
Saved in:
| Published in | Expert systems with applications Vol. 36; no. 4; pp. 8324 - 8332 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.05.2009
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4174 1873-6793 |
| DOI | 10.1016/j.eswa.2008.10.029 |
Cover
| Abstract | A system is seriously required for helping users to find their path on the shopping and entertainment web sites where the amounts of on-line information vastly increase. Therefore, recommender systems, new type of internet based software tool, appeared, and became an appealing subject for researchers. Collaborative filtering (CF) technique based on user is the one of the method widely used by recommender systems but they have some problems for waiting to be developed solutions that are more efficient. One of these mainly problems is data sparsity. While the number of products is increase, the ratio of common rated products is decrease so calculating the computations of neighbourhood become difficult. The other one is scalability which is the performance problem of the existing algorithms on the datasets has large amounts of information.
In this article, we tackle these two questions: (1) how the data sparsity can be reduced ? (2) How to make recommendation algorithms more scalable? We present an approach to addressing the both of these problems at the same time by using a new CF model, constructed based on the Artificial Immune Network Algorithm (aiNet). It is chosen because aiNet is capable of reducing sparsity and providing the scalability of dataset via describing data structure, including their spatial distribution and cluster inter-relations. The new user-item ratings dataset reduced by applying aiNet (aiNetDS) given more stable results and produced predictions more quickly than the raw user-item ratings dataset (rawDS). Besides, the effects of using clustering for forming the neighbourhoods to the system performance are investigated. For this, both of these dataset are clustered by using k-means algorithm and then these cluster partitions are used as neighbourhoods. As a result, it has been shown that the clustered aiNetDS is given more accurate and quick results than the others are. |
|---|---|
| AbstractList | A system is seriously required for helping users to find their path on the shopping and entertainment web sites where the amounts of on-line information vastly increase. Therefore, recommender systems, new type of internet based software tool, appeared, and became an appealing subject for researchers. Collaborative filtering (CF) technique based on user is the one of the method widely used by recommender systems but they have some problems for waiting to be developed solutions that are more efficient. One of these mainly problems is data sparsity. While the number of products is increase, the ratio of common rated products is decrease so calculating the computations of neighbourhood become difficult. The other one is scalability which is the performance problem of the existing algorithms on the datasets has large amounts of information. In this article, we tackle these two questions: (1) how the data sparsity can be reduced ? (2) How to make recommendation algorithms more scalable? We present an approach to addressing the both of these problems at the same time by using a new CF model, constructed based on the Artificial Immune Network Algorithm (aiNet). It is chosen because aiNet is capable of reducing sparsity and providing the scalability of dataset via describing data structure, including their spatial distribution and cluster inter-relations. The new user-item ratings dataset reduced by applying aiNet (aiNetDS) given more stable results and produced predictions more quickly than the raw user-item ratings dataset (rawDS). Besides, the effects of using clustering for forming the neighbourhoods to the system performance are investigated. For this, both of these dataset are clustered by using k-means algorithm and then these cluster partitions are used as neighbourhoods. As a result, it has been shown that the clustered aiNetDS is given more accurate and quick results than the others are. A system is seriously required for helping users to find their path on the shopping and entertainment web sites where the amounts of on-line information vastly increase. Therefore, recommender systems, new type of internet based software tool, appeared, and became an appealing subject for researchers. Collaborative filtering (CF) technique based on user is the one of the method widely used by recommender systems but they have some problems for waiting to be developed solutions that are more efficient. One of these mainly problems is data sparsity. While the number of products is increase, the ratio of common rated products is decrease so calculating the computations of neighbourhood become difficult. The other one is scalability which is the performance problem of the existing algorithms on the datasets has large amounts of information. In this article, we tackle these two questions: (1) how the data sparsity can be reduced ? (2) How to make recommendation algorithms more scalable? We present an approach to addressing the both of these problems at the same time by using a new CF model, constructed based on the Artificial Immune Network Algorithm (aiNet). It is chosen because aiNet is capable of reducing sparsity and providing the scalability of dataset via describing data structure, including their spatial distribution and cluster inter-relations. The new user-item ratings dataset reduced by applying aiNet (aiNetDS) given more stable results and produced predictions more quickly than the raw user-item ratings dataset (rawDS). Besides, the effects of using clustering for forming the neighbourhoods to the system performance are investigated. For this, both of these dataset are clustered by using k-means algorithm and then these cluster partitions are used as neighbourhoods. As a result, it has been shown that the clustered aiNetDS is given more accurate and quick results than the others are. |
| Author | Merve Acilar, A. Arslan, Ahmet |
| Author_xml | – sequence: 1 givenname: A. surname: Merve Acilar fullname: Merve Acilar, A. email: msakiroglu@selcuk.edu.tr, msakiroglu@hotmail.com – sequence: 2 givenname: Ahmet surname: Arslan fullname: Arslan, Ahmet |
| BookMark | eNp9kD1PwzAQhi1UJErhDzB5Ykuw46SOJZaq4kuqxAKz5TpncEnsYrut-Pc4KhNDp5Nevc_p7rlEE-cdIHRDSUkJnd9tSogHVVaEtDkoSSXO0JS2nBVzLtgETYloeFFTXl-gyxg3hFBOCJ-i5QJr3_dq7YNKdg_Y2D5BsO4DD5A-fYfXKkKHvcMqJGustqrHdhh2DrCDdPDh6wqdG9VHuP6bM_T--PC2fC5Wr08vy8Wq0EzwVLS1Uoxz1RBoGmNMvTZ1zaqmokYxwvOhnNVNK5qu7SrBRCuYFooxQoSCzmg2Q7fHvdvgv3cQkxxs1JCvd-B3UeaqmNOG5WJ1LOrgYwxg5DbYQYUfSYkcfcmNHH3J0deYZV8Zav9B2qbsxLsUlO1Po_dHFPL3ewtBRm3BaehsAJ1k5-0p_BeDQIhe |
| CitedBy_id | crossref_primary_10_1016_j_eswa_2021_114917 crossref_primary_10_1016_j_eswa_2011_09_094 crossref_primary_10_1109_ACCESS_2019_2891544 crossref_primary_10_1142_S0219622010003919 crossref_primary_10_1016_j_asoc_2012_11_046 crossref_primary_10_3233_JIFS_191225 crossref_primary_10_1108_DTA_04_2022_0172 crossref_primary_10_2753_JEC1086_4415160104 crossref_primary_10_52547_jsdp_18_4_89 crossref_primary_10_1007_s00607_015_0448_7 crossref_primary_10_1080_01969722_2020_1871229 crossref_primary_10_1016_j_procs_2020_01_006 crossref_primary_10_3233_KES220013 crossref_primary_10_3233_IDT_200217 crossref_primary_10_1016_j_eswa_2019_112906 crossref_primary_10_1016_j_asoc_2021_107552 crossref_primary_10_1109_ACCESS_2024_3496318 crossref_primary_10_2197_ipsjjip_30_361 crossref_primary_10_1007_s42979_023_02207_z crossref_primary_10_1016_j_ipm_2013_02_004 crossref_primary_10_1016_j_eswa_2012_02_038 crossref_primary_10_1007_s11047_010_9228_7 crossref_primary_10_1016_j_procs_2023_12_212 crossref_primary_10_5391_JKIIS_2011_21_2_224 crossref_primary_10_52547_jsdp_19_1_1 crossref_primary_10_1007_s10462_015_9443_9 crossref_primary_10_1016_j_chb_2016_11_010 crossref_primary_10_1007_s00500_016_2212_0 crossref_primary_10_1088_1757_899X_909_1_012072 crossref_primary_10_11627_jksie_2024_47_2_155 crossref_primary_10_1016_j_aei_2015_04_005 crossref_primary_10_1016_j_neucom_2024_128932 crossref_primary_10_1016_j_compind_2018_05_004 crossref_primary_10_1016_j_knosys_2013_03_012 crossref_primary_10_1007_s12652_020_01997_x crossref_primary_10_1109_TKDE_2016_2615039 crossref_primary_10_1016_j_asoc_2018_07_001 crossref_primary_10_2139_ssrn_3365525 crossref_primary_10_3233_IFS_151559 crossref_primary_10_1007_s00521_011_0674_7 crossref_primary_10_1007_s10586_017_1505_0 crossref_primary_10_32628_CSEIT1952312 crossref_primary_10_25046_aj030630 crossref_primary_10_1016_j_future_2018_04_025 crossref_primary_10_1080_17517575_2017_1293301 crossref_primary_10_1016_j_eij_2015_06_005 crossref_primary_10_4018_IJWP_2018070105 |
| Cites_doi | 10.1007/978-3-540-72830-6_27 10.21236/ADA439541 10.1016/j.ins.2007.07.024 10.1145/358916.358995 10.1007/s10852-004-5336-7 10.2139/ssrn.2832023 10.1016/j.ipm.2006.07.005 10.1109/ITI.2006.1708508 10.1016/S0957-4174(03)00138-6 10.1016/j.eswa.2004.10.015 10.1023/A:1006544522159 10.1145/138859.138867 10.1016/j.im.2004.01.008 10.1016/j.eswa.2006.10.005 10.1109/TSMCA.2003.818877 10.1145/192844.192905 10.1145/963770.963775 10.1016/j.eswa.2004.08.013 10.1023/A:1011419012209 |
| ContentType | Journal Article |
| Copyright | 2008 Elsevier Ltd |
| Copyright_xml | – notice: 2008 Elsevier Ltd |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.eswa.2008.10.029 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-6793 |
| EndPage | 8332 |
| ExternalDocumentID | 10_1016_j_eswa_2008_10_029 S0957417408007100 |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 29G 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AAAKG AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AAXUO AAYFN ABBOA ABFNM ABKBG ABMAC ABMVD ABUCO ABXDB ABYKQ ACDAQ ACGFS ACHRH ACNNM ACNTT ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HAMUX HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 LY1 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SDS SES SET SEW SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 WUQ XPP ZMT ~G- AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AFXIZ AGCQF AGQPQ AGRNS AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP BNPGV CITATION SSH 7SC 8FD EFKBS JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c397t-84aa377a50e55fff4bf4432521fa3076797345895d8d2939893c9a33009aedfc3 |
| IEDL.DBID | AIKHN |
| ISSN | 0957-4174 |
| IngestDate | Fri Sep 05 03:51:19 EDT 2025 Tue Jul 01 03:12:00 EDT 2025 Thu Apr 24 23:01:26 EDT 2025 Fri Feb 23 02:30:20 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | aiNet Collaborative filtering Sparsity Scalability Recommender systems Artificial immune system |
| Language | English |
| License | https://www.elsevier.com/tdm/userlicense/1.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c397t-84aa377a50e55fff4bf4432521fa3076797345895d8d2939893c9a33009aedfc3 |
| Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| PQID | 33096153 |
| PQPubID | 23500 |
| PageCount | 9 |
| ParticipantIDs | proquest_miscellaneous_33096153 crossref_primary_10_1016_j_eswa_2008_10_029 crossref_citationtrail_10_1016_j_eswa_2008_10_029 elsevier_sciencedirect_doi_10_1016_j_eswa_2008_10_029 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | May 2009 2009-05-00 20090501 |
| PublicationDateYYYYMMDD | 2009-05-01 |
| PublicationDate_xml | – month: 05 year: 2009 text: May 2009 |
| PublicationDecade | 2000 |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2009 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | (pp. 473–480). Pennock, D. M., Horvitz, E., Lawrence, S., & Lee Giles, C. (2000). Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach. In Sarwar, B. (2001). Sparsity, scalability, and distribution in recommender systems. PhD thesis, University of Minnesota. Goldberg, Roeder, Gupta, Perkins (bib13) 2000; 4 Huang, Chen, Zeng (bib16) 2004; 22 Jerne (bib17) 1974 Billsus, D., & Pazzini, M. J. (1998). Learning collaborative information filters. In Nottingham, UK. Cho, Cho, Kim (bib7) 2005; 28 Cheung, Tsui, Liu (bib6) 2004; 34 Morrison, T. (2003). Similarity measure building for website recommendation within an artificial immune system. Ph.D. Thesis, University of Nottingham. Aickelin, U., & Chen, Q. (2004). On affinity measures for artificial immune system movie recommenders. In de Castro, Von Zuben (bib11) 2001 Cho, Kim (bib8) 2004; 26 Resnick P., Lacovou N., Suchak M., Bergstrom P., & Riedl J. (pp. 241–250). Philadelphia, PA. Claypool, M., Gokhale, A., & Miranda, T. (1999). Combining content-based and collaborative filters in an online newspaper. In Li, Lu, Xuefeng (bib19) 2005; 28 Sarwar, B. M., Karypis, G., Kontsan, J. A. & Riedl, J. T. (2000). Application of dimensionality reduction in recommender system – A case study. In ACM WebKDD 2000 web mining for e-commerce workshop (pp. 82–90). ACM Press. Sakiroglu, A. M. (2005). A recommender system based on artificial immune for web sites. Master thesis, University of Selcuk. Li, Myaeng, Kim (bib20) 2007; 43 (pp. 43–52). 1994). GroupLens: An open architecture for collaborative filtering of netnews. In Li, Q., & Kim, M. K. (2003). Clustering approach for hybrid recommender system, In Liu, Ke, Lee, Lee (bib21) 2008; 34 (pp. 367–372). Cavtat, Croatia. . Sobecki, Szczepański (bib33) 2007; 4496/2007 Morgan Kaufmann Publishers Inc. (pp. 46–54, 93). Goldberg, Nichols, Oki, Terry (bib12) 1992; 35 de Castro, Timmis (bib10) 2002 Pazzini (bib26) 1999; 13 Schafer, Konstan, Riedl (bib32) 2001 Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative fitlering. In Ahn (bib1) 2008; 178 Han, Kamber (bib14) 2001 Herlocker, J., Konstan, J. A., & Riedl, J. (2000). Explaining collaborative filtering recommendations. In Liu, Shih (bib22) 2005; 42 Cayzer, Aickelin (bib5) 2005; 4 Mihaljevic, B., Cvitas, A., & Zagar, M. (2006). Recommender system model based on artificial immune system. In (pp. 175–186). 10.1016/j.eswa.2008.10.029_bib27 10.1016/j.eswa.2008.10.029_bib25 10.1016/j.eswa.2008.10.029_bib28 Goldberg (10.1016/j.eswa.2008.10.029_bib13) 2000; 4 10.1016/j.eswa.2008.10.029_bib29 Han (10.1016/j.eswa.2008.10.029_bib14) 2001 de Castro (10.1016/j.eswa.2008.10.029_bib10) 2002 10.1016/j.eswa.2008.10.029_bib23 Ahn (10.1016/j.eswa.2008.10.029_bib1) 2008; 178 10.1016/j.eswa.2008.10.029_bib4 10.1016/j.eswa.2008.10.029_bib3 10.1016/j.eswa.2008.10.029_bib2 Liu (10.1016/j.eswa.2008.10.029_bib21) 2008; 34 Cheung (10.1016/j.eswa.2008.10.029_bib6) 2004; 34 Huang (10.1016/j.eswa.2008.10.029_bib16) 2004; 22 Cayzer (10.1016/j.eswa.2008.10.029_bib5) 2005; 4 Li (10.1016/j.eswa.2008.10.029_bib20) 2007; 43 Cho (10.1016/j.eswa.2008.10.029_bib7) 2005; 28 10.1016/j.eswa.2008.10.029_bib15 Sobecki (10.1016/j.eswa.2008.10.029_bib33) 2007; 4496/2007 de Castro (10.1016/j.eswa.2008.10.029_bib11) 2001 10.1016/j.eswa.2008.10.029_bib18 10.1016/j.eswa.2008.10.029_bib30 Schafer (10.1016/j.eswa.2008.10.029_bib32) 2001 Goldberg (10.1016/j.eswa.2008.10.029_bib12) 1992; 35 10.1016/j.eswa.2008.10.029_bib9 10.1016/j.eswa.2008.10.029_bib31 Li (10.1016/j.eswa.2008.10.029_bib19) 2005; 28 Liu (10.1016/j.eswa.2008.10.029_bib22) 2005; 42 Cho (10.1016/j.eswa.2008.10.029_bib8) 2004; 26 Pazzini (10.1016/j.eswa.2008.10.029_bib26) 1999; 13 Jerne (10.1016/j.eswa.2008.10.029_bib17) 1974 |
| References_xml | – volume: 34 start-page: 143 year: 2004 end-page: 148 ident: bib6 article-title: Extended latent class models for collaborative filtering publication-title: IEEE Transactions on Systems Man and Cybernetics – Part A: Systems and Humans – reference: Pennock, D. M., Horvitz, E., Lawrence, S., & Lee Giles, C. (2000). Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach. In – reference: (pp. 175–186). – reference: Aickelin, U., & Chen, Q. (2004). On affinity measures for artificial immune system movie recommenders. In – reference: Herlocker, J., Konstan, J. A., & Riedl, J. (2000). Explaining collaborative filtering recommendations. In – volume: 26 start-page: 233 year: 2004 end-page: 246 ident: bib8 article-title: Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce publication-title: Expert System with Applications – reference: Resnick P., Lacovou N., Suchak M., Bergstrom P., & Riedl J. – reference: , Morgan Kaufmann Publishers Inc. (pp. 46–54, 93). – volume: 28 start-page: 67 year: 2005 end-page: 77 ident: bib19 article-title: A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce publication-title: Expert System with Applications – volume: 22 start-page: 116 year: 2004 end-page: 142 ident: bib16 article-title: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering publication-title: ACM Transactions on Information Systems – volume: 34 start-page: 700 year: 2008 end-page: 716 ident: bib21 article-title: Knowledge maps for composite e-services: A mining-based system platform coupling with recommendations publication-title: Expert Systems with Applications – reference: Sarwar, B. (2001). Sparsity, scalability, and distribution in recommender systems. PhD thesis, University of Minnesota. – year: 2001 ident: bib32 article-title: E-Commerce recommendation applications – reference: Claypool, M., Gokhale, A., & Miranda, T. (1999). Combining content-based and collaborative filters in an online newspaper. In – reference: Sakiroglu, A. M. (2005). A recommender system based on artificial immune for web sites. Master thesis, University of Selcuk. – volume: 13 start-page: 393 year: 1999 end-page: 408 ident: bib26 article-title: A framework for collaborative, content based and demografic filtering publication-title: Artificial Intelligence Review – volume: 42 start-page: 387 year: 2005 end-page: 400 ident: bib22 article-title: Integrating AHP and data mining for product recommendation based on customer lifetime value publication-title: Information & Management – volume: 4 start-page: 133 year: 2000 end-page: 151 ident: bib13 article-title: Eigentaste: A constant time collaborative filtering algorithm publication-title: Information Retrieval – reference: Li, Q., & Kim, M. K. (2003). Clustering approach for hybrid recommender system, In – year: 2002 ident: bib10 article-title: Artificial immune systems: A new computational intelligence approach – start-page: 39 year: 1974 ident: bib17 article-title: Clonal selection in a lymphocyte network publication-title: Cellular selection and regulation in the immune response – volume: 43 start-page: 473 year: 2007 end-page: 487 ident: bib20 article-title: A probabilistic music recommender considering user opinions and audio features publication-title: Information Processing and Management – reference: (pp. 367–372). Cavtat, Croatia. – volume: 4496/2007 start-page: 258 year: 2007 end-page: 266 ident: bib33 article-title: Wiki-news interface agent based on AIS methods publication-title: Lecture Notes in Computer Science – reference: Mihaljevic, B., Cvitas, A., & Zagar, M. (2006). Recommender system model based on artificial immune system. In – reference: . – reference: Sarwar, B. M., Karypis, G., Kontsan, J. A. & Riedl, J. T. (2000). Application of dimensionality reduction in recommender system – A case study. In ACM WebKDD 2000 web mining for e-commerce workshop (pp. 82–90). ACM Press. – reference: (pp. 473–480). – reference: (pp. 43–52). – reference: 1994). GroupLens: An open architecture for collaborative filtering of netnews. In – reference: Billsus, D., & Pazzini, M. J. (1998). Learning collaborative information filters. In – start-page: 231 year: 2001 end-page: 259 ident: bib11 article-title: aiNet: An artificial immune network for data analysis publication-title: Data mining: a heuristic approach – volume: 4 start-page: 181 year: 2005 end-page: 198 ident: bib5 article-title: A recommender system based on idiotypic artificial immune networks publication-title: Journal of Mathematical Modelling and Algorithms – reference: (pp. 241–250). Philadelphia, PA. – reference: Morrison, T. (2003). Similarity measure building for website recommendation within an artificial immune system. Ph.D. Thesis, University of Nottingham. – reference: , Nottingham, UK. – volume: 35 start-page: 12 year: 1992 ident: bib12 article-title: Using collaborative filtering to weave an information tapestry publication-title: Communications of the ACM – reference: Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative fitlering. In – year: 2001 ident: bib14 article-title: Data mining: Concepts and techniques – volume: 178 start-page: 37 year: 2008 end-page: 51 ident: bib1 article-title: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem publication-title: Information Sciences – volume: 28 start-page: 359 year: 2005 end-page: 369 ident: bib7 article-title: Mining changes in customer buying behaviour for collaborative recommendations publication-title: Expert System with Applications – volume: 4496/2007 start-page: 258 year: 2007 ident: 10.1016/j.eswa.2008.10.029_bib33 article-title: Wiki-news interface agent based on AIS methods publication-title: Lecture Notes in Computer Science doi: 10.1007/978-3-540-72830-6_27 – ident: 10.1016/j.eswa.2008.10.029_bib3 – ident: 10.1016/j.eswa.2008.10.029_bib29 – ident: 10.1016/j.eswa.2008.10.029_bib27 – ident: 10.1016/j.eswa.2008.10.029_bib31 doi: 10.21236/ADA439541 – year: 2002 ident: 10.1016/j.eswa.2008.10.029_bib10 – volume: 178 start-page: 37 year: 2008 ident: 10.1016/j.eswa.2008.10.029_bib1 article-title: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem publication-title: Information Sciences doi: 10.1016/j.ins.2007.07.024 – ident: 10.1016/j.eswa.2008.10.029_bib15 doi: 10.1145/358916.358995 – volume: 4 start-page: 181 issue: 2 year: 2005 ident: 10.1016/j.eswa.2008.10.029_bib5 article-title: A recommender system based on idiotypic artificial immune networks publication-title: Journal of Mathematical Modelling and Algorithms doi: 10.1007/s10852-004-5336-7 – ident: 10.1016/j.eswa.2008.10.029_bib9 – ident: 10.1016/j.eswa.2008.10.029_bib2 doi: 10.2139/ssrn.2832023 – start-page: 231 year: 2001 ident: 10.1016/j.eswa.2008.10.029_bib11 article-title: aiNet: An artificial immune network for data analysis – volume: 43 start-page: 473 year: 2007 ident: 10.1016/j.eswa.2008.10.029_bib20 article-title: A probabilistic music recommender considering user opinions and audio features publication-title: Information Processing and Management doi: 10.1016/j.ipm.2006.07.005 – ident: 10.1016/j.eswa.2008.10.029_bib23 doi: 10.1109/ITI.2006.1708508 – volume: 26 start-page: 233 year: 2004 ident: 10.1016/j.eswa.2008.10.029_bib8 article-title: Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce publication-title: Expert System with Applications doi: 10.1016/S0957-4174(03)00138-6 – ident: 10.1016/j.eswa.2008.10.029_bib25 – start-page: 39 year: 1974 ident: 10.1016/j.eswa.2008.10.029_bib17 article-title: Clonal selection in a lymphocyte network – year: 2001 ident: 10.1016/j.eswa.2008.10.029_bib32 – volume: 28 start-page: 359 year: 2005 ident: 10.1016/j.eswa.2008.10.029_bib7 article-title: Mining changes in customer buying behaviour for collaborative recommendations publication-title: Expert System with Applications doi: 10.1016/j.eswa.2004.10.015 – volume: 13 start-page: 393 issue: 5–6 year: 1999 ident: 10.1016/j.eswa.2008.10.029_bib26 article-title: A framework for collaborative, content based and demografic filtering publication-title: Artificial Intelligence Review doi: 10.1023/A:1006544522159 – ident: 10.1016/j.eswa.2008.10.029_bib30 – volume: 35 start-page: 12 year: 1992 ident: 10.1016/j.eswa.2008.10.029_bib12 article-title: Using collaborative filtering to weave an information tapestry publication-title: Communications of the ACM doi: 10.1145/138859.138867 – volume: 42 start-page: 387 year: 2005 ident: 10.1016/j.eswa.2008.10.029_bib22 article-title: Integrating AHP and data mining for product recommendation based on customer lifetime value publication-title: Information & Management doi: 10.1016/j.im.2004.01.008 – ident: 10.1016/j.eswa.2008.10.029_bib4 – volume: 34 start-page: 700 year: 2008 ident: 10.1016/j.eswa.2008.10.029_bib21 article-title: Knowledge maps for composite e-services: A mining-based system platform coupling with recommendations publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2006.10.005 – ident: 10.1016/j.eswa.2008.10.029_bib18 – year: 2001 ident: 10.1016/j.eswa.2008.10.029_bib14 – volume: 34 start-page: 143 issue: 1 year: 2004 ident: 10.1016/j.eswa.2008.10.029_bib6 article-title: Extended latent class models for collaborative filtering publication-title: IEEE Transactions on Systems Man and Cybernetics – Part A: Systems and Humans doi: 10.1109/TSMCA.2003.818877 – ident: 10.1016/j.eswa.2008.10.029_bib28 doi: 10.1145/192844.192905 – volume: 22 start-page: 116 issue: 1 year: 2004 ident: 10.1016/j.eswa.2008.10.029_bib16 article-title: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering publication-title: ACM Transactions on Information Systems doi: 10.1145/963770.963775 – volume: 28 start-page: 67 year: 2005 ident: 10.1016/j.eswa.2008.10.029_bib19 article-title: A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce publication-title: Expert System with Applications doi: 10.1016/j.eswa.2004.08.013 – volume: 4 start-page: 133 issue: 2 year: 2000 ident: 10.1016/j.eswa.2008.10.029_bib13 article-title: Eigentaste: A constant time collaborative filtering algorithm publication-title: Information Retrieval doi: 10.1023/A:1011419012209 |
| SSID | ssj0017007 |
| Score | 2.2413387 |
| Snippet | A system is seriously required for helping users to find their path on the shopping and entertainment web sites where the amounts of on-line information vastly... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 8324 |
| SubjectTerms | aiNet Artificial immune system Collaborative filtering Recommender systems Scalability Sparsity |
| Title | A collaborative filtering method based on artificial immune network |
| URI | https://dx.doi.org/10.1016/j.eswa.2008.10.029 https://www.proquest.com/docview/33096153 |
| Volume | 36 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwED71sbDwRpRH8cCGQpPYTuKxqqgKiC5QqVvkJLZUhNKKtrDx2znHTgUIMbBadhSfz3efdd_dAVzSOA40DZUXoYKYB4rwkjAvvIxlseJJkNOqbsHDOBpN2N2UTxswqHNhDK3S2X5r0ytr7UZ6Tpq9xWzWe0RwgO4wZgbzmBo1TWiHVES8Be3-7f1ovAkmxL7Nmsb5nlngcmcszUst36WlVBqSV4U0f_VPPyx15X6Gu7DtcCPp21_bg4Yq92Gn7slA3BU9gEGffDnaN0X0zMTD0UER2yyaGL9VkHlJzDZt_QgyM1kiipSWE34Ik-HN02DkuUYJXo5wYuUlTEoUuuS-4lxrzTLNGA3RM2uJdziKRUwZTwQvkgLdu0CMkgtJKeIrqQqd0yNolfNSHQOhzC_CMMj9oIhYxkQWBNLnOkl0gS81xTsQ1OJJc1dF3DSzeElruthzakRq21viGIq0A1ebNQtbQ-PP2byWevpNE1I08n-uu6iPKMUrYuIeslTz9TLFjQqDa0_--eVT2LIxJENzPIPW6nWtzhGKrLIuNK8_gq5TuE8HuNu2 |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwED6VdoCFN6K86oENRU1iO4-xqqha-lhopW6Rk9hSEUor2sLf5xw7CBDqwGrZUXw-3_dZ9wK4p2HoKepLJ0AF0Q-U2In8LHdSloaSR15Gy7oF40nQn7GnOZ_XoFvlwuiwSmv7jU0vrbUdaVtptleLRfsZyQHCYcg059E1avagwTi-9urQ6AyG_cmXMyF0TdY0znf0Aps7Y8K85PpDmJBKHeRVMs0_8emXpS7hp3cMh5Y3ko75tROoyeIUjqqeDMRe0TPodsi3o32XRC20PxwBiphm0UTjVk6WBdHbNPUjyEJniUhSmJjwc5j1HqfdvmMbJTgZ0omNEzEhUOiCu5JzpRRLFWPUR2RWAu9wEMYhZTyKeR7lCO8xcpQsFpQivxIyVxm9gHqxLOQlEMrc3Pe9zPXygKUsTj1PuFxFkcrxpSZ5E7xKPElmq4jrZhavSRUu9pJokZr2ljiGIm3Cw9ealamhsXM2r6Se_NCEBI38znWt6ogSvCLa7yEKudyuE9xorHnt1T-_3IL9_nQ8SkaDyfAaDow_SYc83kB987aVt0hLNumdVbtPTpLdpQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+collaborative+filtering+method+based+on+artificial+immune+network&rft.jtitle=Expert+systems+with+applications&rft.au=Merve+Acilar%2C+A.&rft.au=Arslan%2C+Ahmet&rft.date=2009-05-01&rft.pub=Elsevier+Ltd&rft.issn=0957-4174&rft.eissn=1873-6793&rft.volume=36&rft.issue=4&rft.spage=8324&rft.epage=8332&rft_id=info:doi/10.1016%2Fj.eswa.2008.10.029&rft.externalDocID=S0957417408007100 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |