A common representation space for User-Item …nlp.uned.es/~acastellanos/documentos/Presentacion-DC2015.pdf09/02/2016 A common representation space for User-Item Modelling and Recommendation

A common representation space for User-Item Modelling and

RecommendationÁngel Castellanos González

[email protected]

AdvisorsAna García SerranoJuan Cigarrán Recuero

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09/02/2016 2A common representation space for User-Item Modelling and Recommendation

Recommender systems: Obvious motivation

“We are leaving the age of information and entering the age of recommendation”Chris Anderson in “The Long Tail” (2006)


Recommendation as prediction


Recommendation vs. Prediction

Prediction Item RatingsRecommendation Item Recommendation

Is Prediction == Recommendation ?


Prediction != Recommendation [Cremonesi et al, 2010]

• User-Item Representation Gap [Yan et al, 2012]

[Lü et al, 2012]

No ratingsInfer recommendations• Data analysis and exploration to extract meaningful patterns


New Scenarios00’s Today

RS accurately approximate ratings• Predictive techniques

But RS do not accurately infer new recommendations• Do RS actually infer user preferences?• User-Item representation gap

New scenarios New requirements• Predictive Exploratory technique


Lessons Learned

Proposal

Knowledge Data Modelling based onFormal Concept Analysis to enable aCommon Representation Space for

top-N Recommendation



• Knowledge-based• Automatic Data Analysis

User Preferences

Items related to the user preferences

Data Modelling


OverviewText-based Representations• Unstructured

– Provide some structure [Jeon et al, 2013; Gattiker, 2014]

• Ambiguity

Knowledge-based Representations• Higher abstraction level [Romeo et al, 2015; Franco-Salvador et al,

2014]

• From text to unambiguous-concept-based representations– Semantic-based: DBpedia– Linguistic-based: Wordnet


KB representationsProblem• Noisy & redundant information [Gangemi et al, 2001]

– “Expresivity lack”– “Too generic domains”

Solution• Knowledge Organization [Bentivogli et al, 2004; Chen & Chen, 2012]

– Build an extra-layer that • Better organizes the data• Infer relationships Identify most valuable information

Our Proposal: FCA


FCA Overview[Wille, 1992]

Extent Intent

C1 {Doc1, Doc2, Doc3, Doc4} {∅}

C2 {Doc1, Doc2, Doc3} {P}

C3 {Doc1, Doc4} {Y}

C4 {Doc1} {P, Y, PY}

C5 {Doc2} {P, NP}

C6 {Doc3} {P, AP}

C7 {∅} {P, NP, AP, Y, PY}

P NP AP Y PY

Doc1 X X X

Doc2 X X

Doc3 X X

Doc4 X


State of the ArtFCA for Knowledge Organization• Ontologies [Cimiano et al, 2005]

• Linguistic Resources [Lee et al, 2010; Falk & Gardent, 2014]

• Dbpedia [Kirchberg et al, 2012]

Our Contributions• Takes into account the whole knowledge domain

– Instead of Query Results or Experimental Corpus

• Applied to a specific task– Recommendation



Example

MAIN AIMIdentify specific item featuresMore accurate data representation

ExperimentsTopic Detection Task @ Replab 2013• Detect topics on a Twitter stream

– Clustering task– The better the tweet representation, the better the topic detection

• Different ways to represent the tweets– Textual Baseline– Applying knowledge-based resources: DBPedia– Our FCA-based proposal: FCA + KB resources

• Topic Detection: State-of-the-Art methodology– HAC + Jaccard as similarity measure


Data Modelling: Experiments


Data Modelling: Results


Recommendationtowards a common representation space


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Extracted from Amatrian et al. Data MiningMethods for Recommender Systems in Recommender Systems Handbook (2011)

A common representation space for User-Item Modelling and Recommendation

Content-based Recommendation

User & Items share representation space• User profiles = Item aggregation• Straightforward recommendation = Find closest items• Avoid user-item representation gap

Methodologies• Ontology-based [Shoval et al, 2008]

• Graph-based [Yan et al, 2012]

• Semantic-based [Cantador, 2008]


Common Space for Recommendation

Extracted from [Cantador, 2008]

Common space based on the FCA-KB-representation

Item = {feature1… featuren}User = {item1…itemn} = {feature1… featuren…feature1’…featurem’}

Formal context• Objects = User and Items• Attributes = Item KB-features

Formal Concepts = User preference• Items fulfilling the preference• Features describing the preference• Users related to the preference


Our proposal

Preference = I like Star Wars

Users = Star Wars fans

Items = Those fulfilling the preference

Features = Descriptors


Example

Recommendation Algorithm


Automatically Inferred by exploring the data

Automatic linking among users and items

Lattice representation• Partial order relationship among user preferences• Coarse- and fine-grained recommendation• Easy visualization


Common-representation-space

Experimentation


Data Modelling• RepLab 2013• MediaEval 2014• ImageCLEF 2011-2013

Recommendation• pLista Challenge 2014• ESWC-LOD-2014• LDOS-CoMoDa Dataset 2015

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Previous experimentation


Likes on Items gathered from Facebook profiles• DBpedia endpoints• 3 domains

– Music: 6,372 items and 850k likes– Movies: 5,389 items and 640k likes– Books: 3,225 items and 11.6k likes

• Task: Top-N recommendations• Evaluation

– Evaluation platform: http://dee020.poliba.it:8181/eswc2015lodrecsys/

– F-measure @ 10

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ESWC LOD RecSys 2015


http://dee020.poliba.it:8181/eswc2015lodrecsys/

CF-baseline• State-of-the-art collaborative filtering recommendation

CB-baseline• Jaccard Similarity to the DBpedia features

Common-space-KB-Representation

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Experiments


Approach Precision Recall F(P,R) CF-baseline 0.0849 0.1230 0.1005 CB-baseline 0.0659 0.0948 0.0778 FCA-KB Representation 0.1005 0.1532 0.1249

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Results


Conclusions and Future Work

(up to now)


We have created an accurate modelling step…• Automatic• Unsupervised• Data-driven• Knowledge Representation

that enables top-N Recommendation• Novel approach: common-representation-space• Take advantage of the high-performant modelling• Improves state-of-the-art systems


Cross-channel recommendation• Infer recommendations by using social data

– User profiles = Twitter data– Recommendation = New events and related news

Contextualized recommendation• Use user-item as input to the recommendation

Recommendation-related aspects• Novelty• Diversity

Comparison to• Other common-representation-space-like approaches• Top-performing recommenders in other tasks: e.g., SVD++

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ToDo


[Bentivogli et al, 2004] Bentivogli, L. Forner, P. Magnini, B. Pianta, E.: Revising the wordnetdomains hierarchy: semantics, coverage and balancing. In Proceedings of the Workshop onMultilingual Linguistic Resources. Association for Computational Linguistics, pp. 101–108.(2004)

[du Boucher-Ryan & Bridge, 2006] du Boucher-Ryan, P., Bridge, D.: Collaborative recommendingusing formal concept analysis. Knowledge-Based Systems 19(5), 309–315. (2006)

[Cantador, 2008] Cantador, I.: Exploiting the conceptual space in hybrid recommender systems: asemantic-based approach. PhD thesis, Universidad Autónoma de Madrid. EscuelaPolitécnica Superior. Departamento de Ingeniería Informática, 2008.

[Cimiano et al, 2005] Cimiano, P. Hotho, A. Staab, S.: Learning concept hierarchies from textcorpora using formal concept analysis. Journal of Artificial Intelligence Research, 24 (1):305–339. (2005)

[Cremonesi et al, 2010] Cremonesi, P. Koren, Y. Turrin, R.: Performance of recommenderalgorithms on top-n recommendation tasks. In Proceedings of the 4th ACM conference onRecommender systems (RecSys 2010), pp. 39-46. (2010)

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References


[Chen & Chen, 2012] Chen, S.J. Chen, H.H.: Mapping multilingual lexical semantics for knowledgeorganization systems. The electronic Library 30 (2): 278–294. (2012)

[Franco-Salvador et al, 2014] Franco-Salvador, M. Rosso, P. Navigli, R. A knowledge basedrepresentation for cross-language document retrieval and categorization Proceedings ofEACL 2014, pp. 414-423 (2014)

[Gattiker, 2014] Gattiker, A.: Unstructured text: Test analysis techniques applied to non-testproblems. In 32nd VLSI Test Symposium (VTS), pp. 1–4. (2014)

[Gangemi et al, 2001] Gangemi, A. Guarino, N. Oltramari, A.: Conceptual analysis of lexicaltaxonomies: The case of wordnet top-level. In Proceedings of the international conferenceon Formal Ontology in Information Systems-Volume, pp. 285–296. (2001)

[Ignatov et al, 2013] Ignatov, D., Kaminskaya, A., Bezzubtseva, A., Konstantinov, A., Poelmans, J.:Fca-based models and a prototype data analysis system for crowdsourcing platforms. In:Pfeiffer, H., Ignatov, D., Poelmans, J., Gadiraju, N. (eds.) Conceptual Structures for STEMResearch and Education, Lecture Notes in Computer Science, vol. 7735, pp. 173–192.Springer Berlin Heidelberg (2013)

[Jeon et al, 2013 ] Jeon, S. Khosiawan, Y. Hong, B.: Making a Graph Database from UnstructuredText. In IEEE 16th International Conference on Computational Science and Engineering(CSE), pp. 981–988. (2013)

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References


[Kirchberg et al, 2012] Kirchberg, M. Leonardi, E. Tan, Y. S. Link, S. Ko, R. K. Lee, B. S.: Formalconcept discovery in semantic web data. In Formal Concept Analysis. Springer, pp. 164–179. (2012)

[Li & Murata, 2010] Li, X., Murata, T.: A knowledge-based recommendation model utilizing formalconcept analysis and association. In: Computer and Automation Engineering (ICCAE), 2010The 2nd International Conference on. vol. 4, pp. 221–226 (2010)

[Lee et al, 2010] Lee, M. C. Liu, Z. L. Chen, H. H. Lai, J. B. Lin, Y. T.: FCA based concept constructingand similarity measurement algorithms. In Advanced Information Management andService (IMS), 2010 6th International Conference on. IEEE, 2010, pp. 384–388.(2012)

[Lü et al, 2012] Lü, L. Medo, M. Yeung, C. H. Zhang, Y.-C. Zhang, Z.-K. Zhou, T. Recommendersystems. Physics Reports, 519(1): 1–49. (2012).

[Falk & Gardent, 2014] Falk, I. Gardent, C.: Combining formal concept analysis and translation toassign frames and semantic role sets to French verbs. Annals of Mathematics and ArtificialIntelligence, 70 (1-2): 123–150. (2014)

[Romeo et al, 2015] Romeo, S. Ienco, D. Tagarelli, A. Knowledge-Based Representation forTransductive Multilingual Document Classification Advances in Information Retrieval,Springer: 92-103 (2015)

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References


[Senatore & Pasi, 2013] Senatore, S., Pasi, G.: Lattice navigation for collaborative filtering bymeans of (fuzzy) formal concept analysis. In: Proceedings of the 28th Annual ACMSymposium on Applied Computing. pp. 920–926. SAC ’13, ACM, New York, NY, USA (2013).

[Shoval et al, 2008] Shoval, P. Maidel, V. Shapira, B.: An ontology-content-based filtering method.Institute of Information Theories and Applications FOI ITHEA. (2008)

[Wille, 1992] Wille, R.: Ordered Sets, chap. Restructuring lattice theory: An approach based onhierarchies of concepts, pp. 445-470. Prentice Hall (1992)

[Yan et al, 2012] Yan, R., Lapata, M., Li, X.: Tweet recommendation with graph co-ranking. InProceedings of the 50th Annual Meeting of the Association for Computational Linguistics(ACL ’12), pp. 516–525. (2012)

[Zhou et al, 2005 ] Zhou, B., Hui, S., Chang, K.: A formal concept analysis approach for web usagemining. In: Shi, Z., He, Q. (eds.) Intelligent Information Processing II, IFIP InternationalFederation for Information Processing, vol. 163, pp. 437–441. Springer US (2005)

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References


Thank you for your attendance (& for your feedback)

Angel Castellanos González:[email protected]

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A common representation space for User-Item …nlp.uned.es/~acastellanos/documentos/Presentacion-DC2015.pdf09/02/2016 A common representation space for User-Item Modelling and Recommendation