Matrix and tensor factorization techniques for recommender systems:
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| Hauptverfasser: | , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[Cham]
Springer
[2016]
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| Schriftenreihe: | SpringerBriefs in computer science
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| Online-Zugang: | BTU01 FHA01 FHI01 FHM01 FHN01 FHR01 FKE01 FRO01 HTW01 UBG01 UBM01 UBR01 UBT01 UBW01 UER01 UPA01 Volltext Inhaltsverzeichnis |
| Beschreibung: | 1 Online-Ressource (VI, 102 Seiten, 51 illus., 22 illus. in color) |
| ISBN: | 9783319413570 |
| ISSN: | 2191-5768 |
| DOI: | 10.1007/978-3-319-41357-0 |
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Datensatz im Suchindex
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| adam_text | Titel: Matrix and tensor factorization techniques for recommender systems
Autor: Symeonidis, Panagiotis
Jahr: 2016
Panagiotis Symeonidis • Andreas Zioupos Matrix and Tensor Factorization Techniques for Recommender Systems Ô Springer
Contents Part I Matrix Factorization Techniques 1 Introduction......................................... 3 1.1 Recommender Systems............................... 3 1.2 Recommender Systems in Social Media................... 5 1.3 Matrix Factorization................................. 6 1.4 Tensor Factorization................................ 8 1.5 Mathematical Background and Notation................... 10 1.6 Book Outline..................................... 13 References........................................... 14 2 Related Work on Matrix Factorization...................... 19 2.1 Dimensionality Reduction on Matrices.................... 19 2.2 Eigenvalue Decomposition............................ 20 2.3 Nonnegative Matrix Factorization....................... 22 2.4 Latent Semantic Indexing............................. 24 2.5 Probabilistic Latent Semantic Indexing.................... 25 2.6 CUR Matrix Decomposition........................... 26 2.7 Other Matrix Decomposition Methods.................... 29 References........................................... 30 3 Performing SVD on Matrices and Its Extensions............... 33 3.1 Singular Value Decomposition (SVD).................... 33 3.1.1 Applying the SVD and Preserving the Largest Singular Values..................................... 37 3.1.2 Generating the Neighborhood of Users/Items.......... 38 3.1.3 Generating the Recommendation List................ 39 3.1.4 Inserting a Test User in the c-Dimensional Space....... 39 3.2 From SVD to UV Decomposition....................... 40 3.2.1 Objective Function Formulation................... 43 3.2.2 Avoiding Overfitting with Regularization............. 44 3.2.3 Incremental Computation of UV Decomposition........ 45 v
vi Contents 3.2.4 The UV Decomposition Algorithm................. 50 3.2.5 Fusing Friendship into the Objective Function......... 51 3.2.6 Inserting New Data in the Initial Matrix.............. 53 References........................................... 57 4 Experimental Evaluation on Matrix Decomposition Methods...... 59 4.1 Data Sets........................................ 59 4.2 Sensitivity Analysis of the UV Decomposition Algorithm....... 60 4.2.1 Tuning of the k Latent Feature Space............... 60 4.2.2 Tuning of Parameter /?.......................... 61 4.2.3 Optimizing Algorithm’s Parameters................. 62 4.3 Comparison to Other Decomposition Methods............... 63 References........................................... 65 Part II Tensor Factorization Techniques 5 Related Work on Tensor Factorization...................... 69 5.1 Preliminary Knowledge of Tensors...................... 69 5.2 Tucker Decomposition and HOSVD..................... 72 5.3 AlsHOSVD...................................... 73 5.4 Parallel Factor Analysis (PARAFAC)..................... 74 5.5 Pairwise Interaction Tensor Factorization (PITF)............. 75 5.6 PCLAF and RPCLAF Algorithms....................... 76 5.7 Other Tensor Decomposition Methods.................... 78 References........................................... 79 6 HOSVD on Tensors and Its Extensions...................... 81 6.1 Algorithm’s Outline................................. 81 6.2 HOSVD in STSs................................... 82 6.2.1 Handling the Sparsity Problem.................... 85 6.2.2 Inserting New Users, Tags, or Items................ 85 6.2.3 Update by Folding-in........................... 86 6.2.4 Update by Incremental SVD...................... 88 6.3 Limitations and Extensions of HOSVD................... 89 6.3.1 Combining HOSVD with a Content-Based Method...... 90 6.3.2 Combining HOSVD with a Clustering Method......... 91
References........................................... 93 7 Experimental Evaluation on Tensor Decomposition Methods...... 95 7.1 Data Sets........................................ 95 7.2 Experimental Protocol and Evaluation Metrics............... 96 7.3 Sensitivity Analysis of the HOSVD Algorithm.............. 96 7.4 Comparison of HOSVD with Other Tensor Decomposition Methods in STSs................................... 98 References........................................... 99 8 Conclusions and Future Work............................ 101
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| owner | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-Aug4 DE-898 DE-BY-UBR DE-861 DE-523 DE-859 DE-473 DE-BY-UBG DE-29 DE-20 DE-92 DE-573 DE-M347 DE-703 DE-739 DE-634 |
| owner_facet | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-Aug4 DE-898 DE-BY-UBR DE-861 DE-523 DE-859 DE-473 DE-BY-UBG DE-29 DE-20 DE-92 DE-573 DE-M347 DE-703 DE-739 DE-634 |
| physical | 1 Online-Ressource (VI, 102 Seiten, 51 illus., 22 illus. in color) |
| psigel | ZDB-2-SCS ZDB-2-SCS_2016 |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Springer |
| record_format | marc |
| series2 | SpringerBriefs in computer science |
| spelling | Symeonidis, Panagiotis Verfasser aut Matrix and tensor factorization techniques for recommender systems Panagiotis Symeonidis, Andreas Zioupos [Cham] Springer [2016] 1 Online-Ressource (VI, 102 Seiten, 51 illus., 22 illus. in color) txt rdacontent c rdamedia cr rdacarrier SpringerBriefs in computer science 2191-5768 Computer science Computer science / Mathematics Information storage and retrieval Artificial intelligence Computer mathematics Computer Science Information Storage and Retrieval Mathematical Applications in Computer Science Mathematics of Computing Artificial Intelligence (incl. Robotics) Informatik Künstliche Intelligenz Mathematik Tensor (DE-588)4184723-4 gnd rswk-swf Matrizenzerlegung (DE-588)4376303-0 gnd rswk-swf Empfehlungssystem (DE-588)7511891-9 gnd rswk-swf Matrizenzerlegung (DE-588)4376303-0 s Tensor (DE-588)4184723-4 s Empfehlungssystem (DE-588)7511891-9 s DE-604 Zioupos, Andreas Verfasser aut Erscheint auch als Druck-Ausgabe 978-3-319-41356-3 https://doi.org/10.1007/978-3-319-41357-0 Verlag URL des Erstveröffentlichers Volltext HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029430901&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Symeonidis, Panagiotis Zioupos, Andreas Matrix and tensor factorization techniques for recommender systems Computer science Computer science / Mathematics Information storage and retrieval Artificial intelligence Computer mathematics Computer Science Information Storage and Retrieval Mathematical Applications in Computer Science Mathematics of Computing Artificial Intelligence (incl. Robotics) Informatik Künstliche Intelligenz Mathematik Tensor (DE-588)4184723-4 gnd Matrizenzerlegung (DE-588)4376303-0 gnd Empfehlungssystem (DE-588)7511891-9 gnd |
| subject_GND | (DE-588)4184723-4 (DE-588)4376303-0 (DE-588)7511891-9 |
| title | Matrix and tensor factorization techniques for recommender systems |
| title_auth | Matrix and tensor factorization techniques for recommender systems |
| title_exact_search | Matrix and tensor factorization techniques for recommender systems |
| title_full | Matrix and tensor factorization techniques for recommender systems Panagiotis Symeonidis, Andreas Zioupos |
| title_fullStr | Matrix and tensor factorization techniques for recommender systems Panagiotis Symeonidis, Andreas Zioupos |
| title_full_unstemmed | Matrix and tensor factorization techniques for recommender systems Panagiotis Symeonidis, Andreas Zioupos |
| title_short | Matrix and tensor factorization techniques for recommender systems |
| title_sort | matrix and tensor factorization techniques for recommender systems |
| topic | Computer science Computer science / Mathematics Information storage and retrieval Artificial intelligence Computer mathematics Computer Science Information Storage and Retrieval Mathematical Applications in Computer Science Mathematics of Computing Artificial Intelligence (incl. Robotics) Informatik Künstliche Intelligenz Mathematik Tensor (DE-588)4184723-4 gnd Matrizenzerlegung (DE-588)4376303-0 gnd Empfehlungssystem (DE-588)7511891-9 gnd |
| topic_facet | Computer science Computer science / Mathematics Information storage and retrieval Artificial intelligence Computer mathematics Computer Science Information Storage and Retrieval Mathematical Applications in Computer Science Mathematics of Computing Artificial Intelligence (incl. Robotics) Informatik Künstliche Intelligenz Mathematik Tensor Matrizenzerlegung Empfehlungssystem |
| url | https://doi.org/10.1007/978-3-319-41357-0 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029430901&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT symeonidispanagiotis matrixandtensorfactorizationtechniquesforrecommendersystems AT ziouposandreas matrixandtensorfactorizationtechniquesforrecommendersystems |