Towards metadata-aware algorithms for recommender systems:
Gespeichert in:
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Hildesheim
Universität Hildesheim
2008
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| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis Abstract |
| Beschreibung: | 118 Seiten Illustrationen, Diagramme |
Internformat
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| 245 | 1 | 0 | |a Towards metadata-aware algorithms for recommender systems |c Karen H. L. Tso-Sutter |
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Datensatz im Suchindex
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| adam_text | Contents
Contents xi
1 Introduction 1
1.1 Motivation ...................................................... 3
1.2 Contributions.................................................. 3
1.3 Published Work................................................... 4
1.4 Chapter Overview................................................. 6
2 Recommender Systems 7
2.1 Recommender Systems Techniques................................. 10
2.1.1 Collaborative Filtering................................... 10
2.1.2 Content-Based Filtering................................... 14
2.1.3 Hybrid Filtering Algorithms............................... 15
2.1.4 Other RS Techniques....................................... 15
2.2 Datasets for Recommender Systems ............................... 16
2.2.1 Datasets for Attribute-Aware RS........................... 16
2.2.1.1 Attribute Data.................................... 18
2.2.2 Datasets for Tag-Aware RS................................. 20
3 Related Work 23
3.1 Attribute-Aware RS.............................................. 23
3.1.1 Content-Boosted CF........................................ 24
3.1.2 PLSA with attributes...................................... 26
3.1.2.1 PLSA - Aspect Model............................... 26
3.1.2.2 Three-way Aspect Model............................ 30
3.1.2.3 JointPLSA......................................... 31
3.1.3 Kim and Li’s Approach..................................... 32
3.2 Tag-Aware RS ................................................... 34
IV
CONTENTS
4 Metadata-Aware RS Approaches: Attribute Driven 36
4.1 CF-Based Attribute-Aware Models................................... 36
4.1.1 Sequential CBF and CF (Adapted Content-Boosted)............. 37
4.1.2 Joint Weighting of CF and CBF............................... 39
4.1.3 Item-Based CF with Attribute Similarity (Item-AttrSim CF) . . 41
4.1.4 Item-Based CF with Attribute-Extended (Item-AttrExt CF) . . 43
4.2 Probabilistic Attribute-Aware Models ............................. 45
4.2.1 Three-way Aspect Model - Item Prediction Problem............ 45
4.2.2 JointPLSA - Item Prediction Problem......................... 49
4.2.3 PLSA Extended with Attributes (PLSA AttrExt)................ 53
5 Metadata-Aware RS Approaches: Tag Driven 56
5.1 Decomposition of Tag-Aware Data................................... 57
5.2 Tag Extension CF (TagExt CF)...................................... 58
5.3 Tag Similarities CF (TagSim CF)................................... 64
6 Fusion: Combining User-Based and Item-Based Models 68
6.1 Fusion for the Predicting Rating Problem.......................... 68
6.2 Generic Fusion for the Item Prediction Problem.................... 70
6.3 Metadata-Aware RS Fusion.......................................... 72
6.3.1 Variations of Attribute-Aware RS Fusion .................... 74
6.3.2 Variations of Tag-Aware RS Fusion........................... 74
6.4 Stacking........................................................ 74 7
7 Empirical Analysis 76
7.1 Datasets........................................................... 76
7.1.1 Datasets for Attributes-Aware RS ........................... 76
7.1.1.1 MovieLens........................................... 77
7.1.1.2 Netflix ............................................ 77
7.1.2 Datasets for Tag-Aware RS................................... 78
7.1.2.1 Last.fm............................................. 78
7.1.2.2 BibSonomy........................................... 81
7.2 Methods in Comparison.............................................. 83
7.2.1 Attribute-Aware RS Algorithms .............................. 84
7.2.2 Tag-Aware RS Algorithms..................................... 85
7.3 Evaluation Protocol and Settings................................... 86
7.4 Attribute-Aware Experiment Results................................. 88
7.4.1 Results for MovieLens Dataset............................... 88
7.4.2 Results for Netflix2M Dataset............................... 90
v
CONTENTS
7.5 Tag-Aware Experiment Results................................... 91
7.5.1 Results for Last.fm Dataset.............................. 91
7.5.2 Results for BibSonomy Dataset............................ 94
7.6 Tags vs. Attributes............................................ 95
7.7 Impact of Metadata............................................. 95
7.7.1 New-Item Protocol........................................ 96
7.7.1.1 Results of New-Item Protocol..................... 97
7.7.2 Random Metadata.......................................... 99
7.7.2.1 Results of the Impact of Random Tags.............100
7.8 Summary........................................................101
8 Conclusion 107
8.1 Summary........................................................107
8.2 Discussion and Outlook.........................................108
References 118
vi
Abstract
One of the main functionalities of recommender systems (RS) is to suggest
items that users might be interested in. Hence, RS have been employed
in many e-commerce websites urging to raise sales by recommending items
that could initiate consumer’s willingness to make a purchase. Thus, the
interest in ameliorating recommendation quality and the effort in research-
ing RS algorithms to suggest better recommendations never stop growing.
Metadata such as content information about the items (attributes) have
typically been used to enrich RS algorithms. Recently, the trend of employ-
ing RS has expanded to other e-communities such as social tagging systems,
inspiring the possibility to exploit tags to enhance RS.
In this thesis, we investigate several research gaps in metadata-aware RS
algorithms. In particular, we examine attribute-aware RS algorithms focus-
ing on the overlooked item prediction problem as well as the new emerging
challenge of tag-aware RS algorithms. Moreover, a fusion mechanism is in-
troduced that aims to alleviate the sparsity problem as well as to profit from
the presence of metadata. Besides, we also explore the impact of metadata
on the overall recommendation quality and on two specific cases when i)
new items and ii) random tags, are injected to the system. Furthermore,
the proposed metadata-aware RS models are evaluated empirically.
|
| any_adam_object | 1 |
| author | Tso-Sutter, Karen H. L. |
| author_GND | (DE-588)14053816X |
| author_facet | Tso-Sutter, Karen H. L. |
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| discipline | Informatik |
| format | Thesis Book |
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| spelling | Tso-Sutter, Karen H. L. Verfasser (DE-588)14053816X aut Towards metadata-aware algorithms for recommender systems Karen H. L. Tso-Sutter Hildesheim Universität Hildesheim 2008 118 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Dissertation University of Hildesheim 2008 Kollaborative Filterung (DE-588)7512839-1 gnd rswk-swf Metadaten (DE-588)4410512-5 gnd rswk-swf Empfehlungssystem (DE-588)7511891-9 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Empfehlungssystem (DE-588)7511891-9 s Kollaborative Filterung (DE-588)7512839-1 s Metadaten (DE-588)4410512-5 s DE-604 B:DE-101 application/pdf http://d-nb.info/999133063/04 Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030505827&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030505827&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Abstract |
| spellingShingle | Tso-Sutter, Karen H. L. Towards metadata-aware algorithms for recommender systems Kollaborative Filterung (DE-588)7512839-1 gnd Metadaten (DE-588)4410512-5 gnd Empfehlungssystem (DE-588)7511891-9 gnd |
| subject_GND | (DE-588)7512839-1 (DE-588)4410512-5 (DE-588)7511891-9 (DE-588)4113937-9 |
| title | Towards metadata-aware algorithms for recommender systems |
| title_auth | Towards metadata-aware algorithms for recommender systems |
| title_exact_search | Towards metadata-aware algorithms for recommender systems |
| title_full | Towards metadata-aware algorithms for recommender systems Karen H. L. Tso-Sutter |
| title_fullStr | Towards metadata-aware algorithms for recommender systems Karen H. L. Tso-Sutter |
| title_full_unstemmed | Towards metadata-aware algorithms for recommender systems Karen H. L. Tso-Sutter |
| title_short | Towards metadata-aware algorithms for recommender systems |
| title_sort | towards metadata aware algorithms for recommender systems |
| topic | Kollaborative Filterung (DE-588)7512839-1 gnd Metadaten (DE-588)4410512-5 gnd Empfehlungssystem (DE-588)7511891-9 gnd |
| topic_facet | Kollaborative Filterung Metadaten Empfehlungssystem Hochschulschrift |
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| work_keys_str_mv | AT tsosutterkarenhl towardsmetadataawarealgorithmsforrecommendersystems |
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