Abstract
Recommender systems are software tools and techniques which aim at suggesting new items that may be of interest to a user. This dissertation is focused on four problems in recommender systems domain. The first one is context-awareness, i.e. how to obtain relevant contextual information, how to model user preferences in a context and use them to make predictions. The second one is multi-domain recommendation, which aim at suggesting items from many domains using user ratings from all of them. The third one is new user cold-start problem which occurs when a new user registers into a recommender system. He will not receive interesting recommendations just because the system does not know his preferences yet. The last problem is the need of generating explanations on how recommender system works. Two contextual user models and appropriate recommendation algorithms were proposed. First model is based on existing SIM method. The ontology-based contextual pre-filtering technique associated with the model allows dynamic generalization of contextual parameters values and generation of multi-domain recommendations. The second model consists of contextual conditional preferences. The post-filtering method associated with it, which is called re-rankCCP, allows to generate explanations for users and contextual recommendations in new user cold-start situations as well as in typical scenarios.
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- Category:
- Thesis, nostrification
- Type:
- praca doktorska pracowników zatrudnionych w PG oraz studentów studium doktoranckiego
- Language:
- English
- Publication year:
- 2018
- Bibliography: test
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- Context as a box (from [10]). . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 open in new tab
- 2.2 The magic box (from [94]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 open in new tab
- Push and pop (from [11]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
- Shifting (from [11]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 open in new tab
- Classification of ontologies by the level of language formalization (from [108]). 10 open in new tab
- Structured-Interpretation Model (from [106]). . . . . . . . . . . . . . . . . . . 13 open in new tab
- An example of SIM ontology (based on [106]). . . . . . . . . . . . . . . . . . . 14
- An example of SIM ontology (from [38]). . . . . . . . . . . . . . . . . . . . . . 14 open in new tab
- Partial Definition of CONON upper ontology (from [107]). . . . . . . . . . . . 15
- The SOUPA ontology (from [22]). . . . . . . . . . . . . . . . . . . . . . . . . . 16
- Examples of context taxonomies (from [44]). . . . . . . . . . . . . . . . . . . . 16 open in new tab
- The PRISSMA vocabulary (from [23]). . . . . . . . . . . . . . . . . . . . . . . 17 open in new tab
- Concepts and relations of RSCtx representing the time dimension (from [68]). 17 S in BPR method (from [90]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 open in new tab
- Graphical model representation of LDA (from [13]). . . . . . . . . . . . . . . 30
- The CP-Net for choosing an outfit for a formal evening (from [16]). . . . . . . 34
- An example of the Contextual Ontological User Profile. . . . . . . . . . . . . 42
- The schema of the Ontology-based Contextual Pre-filtering Approach. . . . . 43 open in new tab
- Post-filtering with re-rankCCP algorithm. . . . . . . . . . . . . . . . . . . . . 59 open in new tab
- Chart for choosing the number of clusters for director variable. . . . . . . . 63 open in new tab
- Boxplots for unalikeability analysis for LDOS-CoMoDa dataset (user level). . . . 69 open in new tab
- Boxplots for unalikeability analysis for Unibz-STS dataset (user level). . . . . 70 open in new tab
- Boxplots for unalikeability analysis for Restaurant & consumer dataset (user level). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 open in new tab
- The Class Structure of the LibRec Library (from [42]). . . . . . . . . . . . . . 73 open in new tab
- The Class Structure of the CARSKit Library (from [112]). . . . . . . . . . . . 74 7.7 Values of MAE and RMSE for different methods applied on Restaurant & consumer dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 open in new tab
- Values of MAE for different methods applied on LDOS-CoMoDa dataset. . . . . 76 open in new tab
- Values of RMSE for different methods applied on LDOS-CoMoDa dataset. . . . 76 7.10 Values of MAE for different methods applied on Unibz-STS dataset. . . . . . 77 open in new tab
- Values of RMSE for different methods applied on Unibz-STS dataset. . . . . 77 open in new tab
- Values of MAE for different methods applied on MovieTweetings dataset. . . 78 open in new tab
- Values of RMSE for different methods applied on MovieTweetings dataset. . 78 open in new tab
- Values of MAE for different methods applied on ConcertTweets dataset with numerical rating scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 open in new tab
- Values of RMSE for different methods applied on ConcertTweets dataset with numerical rating scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 open in new tab
- Values of MAE for different methods applied on ConcertTweets dataset with descriptive rating scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 open in new tab
- Values of RMSE for different methods applied on ConcertTweets dataset with descriptive rating scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 open in new tab
- Connections between context types in COUP for multi-domain recommendations. 88 List of Tables open in new tab
- The ALC language constructors. . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.1 Exemplary catalog of movies. . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2 Exemplary preference profile. . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3 Exemplary matrix of user-item ratings. . . . . . . . . . . . . . . . . . . . . . . 21 4.4 Exemplary product assortment: digital cameras (from [31]). . . . . . . . . . . 23 4.5 Input data requirements of recommendation algorithms (from [56]). . . . . . . 24 4.6 POI ratings in contexts (from [110]). . . . . . . . . . . . . . . . . . . . . . . . 32 4.7 Rating matrix transformed by item splitting (from [110]). . . . . . . . . . . . 32 open in new tab
- An example of a test set. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.9 Ratings predicted by two RSs: RS1 and RS2 for the test set from Tab. 4.8. . 36 5.1 Example for rating prediction with COUP. . . . . . . . . . . . . . . . . . . . 44 5.2 Sample user preferences in the movie domain of Alice, Bob and Carol. . . . . 46 5.3 Sample user preferences in the restaurant domain of Alice, Bob and Carol. . . 47 5.4 Preferences of Alice, Bob and Carol after ontology-based contextual pre-filtering for Alice on Saturady with a friend (both domains). . . . . . . . . . . . . . . 47
- The trip decision example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 open in new tab
- 2 The subset of training set for trip decision example. . . . . . . . . . . . . . . 52 open in new tab
- Sample user profiles of Alice, Bob and Carol. . . . . . . . . . . . . . . . . . . 54
- Contextual parameters from LDOS-CoMoDa dataset. . . . . . . . . . . . . . . . 62 open in new tab
- Basic statistics of four datasets: LDOS-CoMoDa (CoMoDa), Unibz-STS (STS), Restaurant & consumer (RC) and MovieTweetings (MT). . . . . . . . . . . 62
- Contextual parameters from Unibz-STS dataset. . . . . . . . . . . . . . . . . 64 open in new tab
- Contextual parameters from Restaurant & consumer dataset. . . . . . . . . 64
- Statistics on the data contained in ConcertTweets dataset. . . . . . . . . . . 66 open in new tab
- Unalikeability analysis for LDOS-CoMoDa dataset. . . . . . . . . . . . . . . . . 68
- Unalikeability analysis for Unibz-STS dataset. . . . . . . . . . . . . . . . . . . 70
- Unalikeability analysis for Restaurant & consumer dataset. . . . . . . . . . . 71
- Measures for the typical scenario for LDOS-CoMoDa dataset. . . . . . . . . . . 83 open in new tab
- Measures for the typical scenario for MovieTweetings dataset. . . . . . . . . 83 open in new tab
- Measures for the typical scenario for ConcertTweets dataset with numerical rating scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 open in new tab
- Measures for the typical scenario for Restaurant & consumer dataset. . . . . 84 open in new tab
- Measures for the typical scenario for Unibz-STS dataset. . . . . . . . . . . . . 84
- Measures for the new user cold-start scenario for LDOS-CoMoDa dataset. . . . 85 open in new tab
- Measures for the new user cold-start scenario for Unibz-STS dataset. . . . . . 86 open in new tab
- Measures for the new user cold-start scenario for Restaurant & consumer dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 open in new tab
- Measures for the new user cold-start scenario for ConcertTweets dataset. . . 87 open in new tab
- Measures for the new user cold-start scenario for MovieTweetings dataset. . 87 open in new tab
- Results obtained for multi-domain recommendations with ontology-based con- textual pre-filtering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 open in new tab
- The number of respondents and evaluated recommendation list for each version of the questionnaire. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 open in new tab
- The number of respondents and evaluated recommendation list for question- naires that contained explanations. . . . . . . . . . . . . . . . . . . . . . . . . 91 open in new tab
- Results obtained with interactive questionnaires. . . . . . . . . . . . . . . . . 91 Bibliography open in new tab
- P. Adamopoulos and A. Tuzhilin. Estimating the Value of Multi-Dimensional Data Sets in Context-based Recommender Systems. In 8th ACM Conference on Recommender Systems (RecSys 2014), 2014.
- P. Adamopoulos and A. Tuzhilin. On unexpectedness in recommender systems: Or how to better expect the unexpected. ACM Trans. Intell. Syst. Technol., 5(4):54:1-54:32, Dec. 2014. open in new tab
- G. Adomavicius, B. Mobasher, F. Ricci, and A. Tuzhilin. Context-aware recommender systems. AI Magazine, 32(3):67-80, 2011. open in new tab
- G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. In IEEE Trans. on Knowledge and Data Engineering, volume 17(6), pages 734-749, 2005. open in new tab
- G. Adomavicius and A. Tuzhilin. Handbook on recommender systems. chapter Context- Aware Recommender Systems, pages 217-256. Springer, 2011. open in new tab
- C. C. Aggarwal. Recommender Systems: The Textbook. Springer Publishing Company, Incorporated, 1st edition, 2016. open in new tab
- F. Baader, D. Calvanese, D. L. McGuinness, D. Nardi, and P. F. Patel-Schneider, editors. The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York, NY, USA, 2003.
- L. Baltrunas and X. Amatriain. Towards time-dependant recommendation based on implicit feedback. In Proceedings of 1st Workshop on Context-Aware Recommender Systems, 2009.
- L. Baltrunas and F. Ricci. Experimental evaluation of context-dependent collaborative filtering using item splitting. User Model. User-Adapt. Interact., 24(1-2):7-34, 2014. open in new tab
- M. Benerecetti, P. Bouquet, and C. Ghidini. Contextual reasoning distilled. Philosoph- ical Foundations of Artificial Intelligence. A special issue of the journal of Experimental and Theoretical AI (JETAI), 12(3):279-305, 2000. open in new tab
- M. Benerecetti, P. Bouquet, and C. Ghidini. On the Dimensions of Context Dependence: Partiality, Approximation, and Perspective, pages 59-72. Springer Berlin Heidelberg, Berlin, Heidelberg, 2001. open in new tab
- C. Bettini, O. Brdiczka, K. Henricksen, J. Indulska, D. Nicklas, A. Ranganathan, and D. Riboni. A survey of context modelling and reasoning techniques. Pervasive and Mo- bile Computing, 6(2):161 -180, 2010. Context Modelling, Reasoning and Management. open in new tab
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993-1022, Mar. 2003.
- C. Bolchini, C. A. Curino, E. Quintarelli, F. A. Schreiber, and L. Tanca. A data-oriented survey of context models. SIGMOD Rec., 36(4):19-26, Dec. 2007. open in new tab
- W. Borst. Construction of Engineering Ontologies for Knowledge Sharing and Reuse. PhD thesis, 9 1997.
- C. Boutilier, R. I. Brafman, C. Domshlak, H. H. Hoos, and D. Poole. Cp-nets: A tool for representing and reasoning with conditional ceteris paribus preference statements. Journal of Artificial Intelligence Research, 21:135-191, 2004. open in new tab
- M. Braunhofer, M. Elahi, F. Ricci, and T. Schievenin. Context-aware points of interest suggestion with dynamic weather data management. In Z. Xiang and I. Tussyadiah, editors, Information and Communication Technologies in Tourism 2014, pages 87-100. Springer International Publishing, 2013. open in new tab
- I. Cantador, A. Bellogín, and P. Castells. Ontology-based personalised and context- aware recommendations of news items. In Proc. of the 2008 IEEE/WIC/ACM Int. Conf. on Web Intelligence and Intelligent Agent Technology -Volume 01, WI-IAT '08, pages 562-565, Washington, DC, USA, 2008. IEEE Computer Society. open in new tab
- I. Cantador and P. Cremonesi. Tutorial on cross-domain recommender systems. In Proceedings of the 8th ACM Conference on Recommender Systems, RecSys '14, pages 401-402, New York, NY, USA, 2014. ACM. open in new tab
- P. Castells and S. Vargas. Novelty and diversity metrics for recommender systems: Choice, discovery and relevance. In In Proceedings of International Workshop on Di- versity in Document Retrieval (DDR), pages 29-37, 2011.
- J. Cendrowska. PRISM: an algorithm for inducing modular rules. International Journal of Man-Machine Studies, 27(4):349-370, 1987. open in new tab
- H. Chen, T. Finin, and A. Joshi. Ontologies for Agents: Theory and Experiences, chapter The SOUPA Ontology for Pervasive Computing, pages 233-258. Birkhäuser Basel, Basel, 2005. open in new tab
- L. Costabello. Context-Aware Access Control and Presentation for Linked Data, chapter A Declarative Model for Mobile Context, pages 21-32. 2013. open in new tab
- P. Cremonesi, A. Tripodi, and R. Turrin. Cross-domain recommender systems. In 2011 IEEE 11th International Conference on Data Mining Workshops, pages 496-503, Dec 2011. open in new tab
- M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst., 22(1):143-177, Jan. 2004. open in new tab
- A. K. Dey. Understanding and using context. Personal Ubiquitous Comput., 5(1):4-7, Jan. 2001. open in new tab
- A. K. Dey and G. D. Abowd. Towards a better understanding of context and context- awarness. In Proc. of Conference on Human Factors in Computing Systems, pages 304-307, 2000.
- S. Dooms, T. De Pessemier, and L. Martens. Movietweetings: a movie rating dataset collected from twitter. In Workshop on Crowdsourcing and Human Computation for Recommender Systems, CrowdRec at RecSys 2013, 2013.
- M. Elahi, M. Braunhofer, F. Ricci, and M. Tkalcic. Personality-Based Active Learning for Collaborative Filtering Recommender Systems, pages 360-371. Springer Interna- tional Publishing, Cham, 2013. open in new tab
- A. Felfernig, G. Friedrich, D. Jannach, and M. Zanker. Developing constraint-based recommenders. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recom- mender Systems Handbook, pages 187-215. Springer US, Boston, MA, 2011. open in new tab
- A. Felfernig, M. Mairitsch, M. Mandl, M. Schubert, and E. Teppan. Utility-Based Repair of Inconsistent Requirements, pages 162-171. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009. open in new tab
- M. Fernández-López, A. Gómez-Pérez, and N. Juristo. Methontology: from ontological art towards ontological engineering. In Proc. Symposium on Ontological Engineering of AAAI, 1997.
- I. Fernández-Tobías, I. Cantador, M. Kaminskas, and F. Ricci. Cross-domain recom- mender systems: A survey of the state of the art. In Proceedings of the 2nd Spanish Conference on Information Retrieval, pages 187-198, 01 2012. open in new tab
- F. Giunchiglia and P. Bouquet. Introduction to contextual reasoning. an artificial intelligence perspective. In B. Kokinov, editor, Perspectives on Cognitive Science, pages 138-159. NBU Press, 1997.
- K. Goczyła. Ontologie w systemach informatycznych. EXIT, 2011.
- K. Goczyła and A. Karpus. Problemy oceny jakości ontologii. Studia Informatica, 34(2A):173-184, 2013.
- K. Goczyła, A. Waloszek, and W. Waloszek. Contextualization of a dl knowledge base. Proc. of the 20th International Workshop on Description Logics DL'07, pages 291-298, 2007. open in new tab
- K. Goczyła, A. Waloszek, W. Waloszek, and T. Zawadzka. Modularized knowledge bases using contexts, conglomerates and a query language. Intelligent Tools for Building a Scientific Information Platform, 390:179-201, 2012. open in new tab
- A. Gómez-Pérez, M. Fernández-López, and O. Corcho. Ontological Engineering with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. Advanced Information and Knowledge Processing. Springer, 1st edition, 2004. open in new tab
- T. R. Gruber. A translation approach to portable ontology specifications. Knowl. Acquis., 5(2):199-220, June 1993. open in new tab
- R. Guha. Contexts: A Formalization and Some Applications. PhD thesis, Stanford, CA, USA, 1992. UMI Order No. GAX92-17827.
- G. Guo, J. Zhang, Z. Sun, and N. Yorke-Smith. Librec: A java library for recommender systems. In A. I. Cristea, J. Masthoff, A. Said, and N. Tintarev, editors, Posters, Demos, Late-breaking Results and Workshop Proceedings of the 23rd Conference on User Modeling, Adaptation, and Personalization (UMAP 2015), Dublin, Ireland, June 29 -July 3, 2015., volume 1388 of CEUR Workshop Proceedings. CEUR-WS.org, 2015.
- S. O. Hansson. What is ceteris paribus preference? J. Philosophical Logic, 25(3):307- 332, 1996. open in new tab
- A. Hawalah and M. Fasli. Utilizing contextual ontological user profiles for personalized recommendations. Expert Systems with Applications, 41(10):4777 -4797, 2014. open in new tab
- P. Hitzler, M. Krötzsch, B. Parsia, P. F. Patel-Schneider, and S. Rudolph, editors. OWL 2 Web Ontology Language: Primer. W3C Recommendation, 27 October 2009. Available at http://www.w3.org/TR/owl2-primer/. open in new tab
- J. R. Hobbs and F. Pan. Time ontology in owl. World Wide Web Consortium, Working Draft WD-owl-time-20060927, September 2006.
- Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM '08, pages 263-272, Washington, DC, USA, 2008. IEEE Computer Society. open in new tab
- L. Iaquinta, M. de Gemmis, P. Lops, G. Semeraro, and P. Molino. E-Commerce, chapter Can a recommender system induce serendipitous encounters?, pages 1-17. IN-TECH, Vienna, 2009. open in new tab
- H. Imran, M. Belghis-Zadeh, T.-W. Chang, Kinshuk, and S. Graf. A Rule-Based Rec- ommender System to Suggest Learning Tasks, pages 672-673. Springer International Publishing, Cham, 2014. open in new tab
- P. Jaccard. Lois de distribution florale dans la zone alpine. Bulletin de la Société vaudoise des sciences naturelles, 38:69-130, 01 1902.
- P. Jaccard. The distribution of flora in the alpine zone. New Phytologist, 11:37 -50, 02 1912. open in new tab
- D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. Recommender Systems: An Introduction. Cambridge University Press, New York, NY, USA, 1st edition, 2010. open in new tab
- D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. Recommender Systems: An Introduction, chapter Explanations in recommender systems, pages 143-165. Cambridge University Press, New York, NY, USA, 1st edition, 2010. open in new tab
- D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. Recommender Systems: An In- troduction, chapter Collaborative recommendation, pages 13-50. Cambridge University Press, New York, NY, USA, 1st edition, 2010. open in new tab
- D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. Recommender Systems: An Introduction, chapter Knowledge-based recommendation, pages 81-123. Cambridge University Press, New York, NY, USA, 1st edition, 2010. open in new tab
- D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. Recommender Systems: An Introduction, chapter Hybrid recommendation approaches, pages 124-142. Cambridge University Press, New York, NY, USA, 1st edition, 2010. open in new tab
- K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst., 20(4):422-446, Oct. 2002. open in new tab
- S. Kabbur, X. Ning, and G. Karypis. Fism: Factored item similarity models for top-n recommender systems. In Proceedings of the 19th ACM SIGKDD International Confer- ence on Knowledge Discovery and Data Mining, KDD '13, pages 659-667, New York, NY, USA, 2013. ACM. open in new tab
- G. D. Kader and M. Perry. Variability for categorical variables. Journal of Statistics Education, 15(2), 2007. open in new tab
- D. Kaplan. On the logic of demonstratives. Journal of Philosophical Logic, 8(1):81-98, 1978. open in new tab
- A. Karpus. Jakość w inżynierii ontologii. In I Podkarpacka Konferencja Naukowa Doktorantów, pages 7-16. Oficyna Wydawnicza Politechniki Rzeszowskiej, 2014.
- A. Karpus. A context in recommender systems. In Zagadnienia Aktualnie Poruszane Przez Młodych Naukowców, volume 6, pages 367-371. Creativetime, 2016.
- A. Karpus, T. di Noia, and K. Goczyla. Top k recommendations using contextual con- ditional preferences model. In M. Ganzha, L. A. Maciaszek, and M. Paprzycki, editors, Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, Prague, Czech Republic, September 3-6, 2017., pages 19-28, 2017. open in new tab
- A. Karpus, T. di Noia, P. Tomeo, and K. Goczyla. Rating prediction with contex- tual conditional preferences. In A. L. N. Fred, J. L. G. Dietz, D. Aveiro, K. Liu, J. Bernardino, and J. Filipe, editors, Proceedings of the 8th International Joint Con- ference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) -Volume 1: KDIR, Porto -Portugal, November 9 -11, 2016, pages 419-424. SciTePress, 2016. open in new tab
- A. Karpus, T. di Noia, P. Tomeo, and K. Goczyła. Using contextual conditional prefer- ences for recommendation tasks: a case study in the movie domain. Studia Informatica, 37(1):7-18, 2016. open in new tab
- A. Karpus and K. Goczyła. A context-aware recommender system based on an onto- logical user profile. In ICT Young, 2014.
- A. Karpus and K. Goczyła. A multi-domain hybrid recommender systems based on a dynamic contextual ontological user profile. In Doctoral Consortium -DC3K, (IC3K 2014), pages 83-87. INSTICC, SciTePress, 2014. open in new tab
- A. Karpus, I. Vagliano, K. Goczyła, and M. Morisio. An ontology-based contextual pre-filtering technique for recommender systems. In 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pages 411-420, Sept 2016. open in new tab
- A. Karpus, I. Vagliano, and K. Goczyła. Serendipitous recommendations through ontology-based contextual pre-filtering. In S. Kozielski, D. Mrozek, P. Kasprowski, B. Małysiak-Mrozek, and D. Kostrzewa, editors, Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representa- tion: 13th International Conference, BDAS 2017, Ustroń, Poland, May 30 -June 2, 2017, Proceedings, pages 246-259, Cham, 2017. Springer International Publishing. open in new tab
- J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl. Grouplens: Applying collaborative filtering to usenet news. Commun. ACM, 40(3):77- 87, Mar. 1997. open in new tab
- Y. Koren. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining, KDD '08, pages 426-434, New York, NY, USA, 2008. ACM. open in new tab
- Y. Koren. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09, pages 447-456, New York, NY, USA, 2009. ACM. open in new tab
- Y. Koren and R. Bell. Advances in Collaborative Filtering, pages 145-186. Springer US, Boston, MA, 2011. open in new tab
- A. Kosir, A. Odic, M. Kunaver, M. Tkalcic, and J. F. Tasic. Database for contextual personalization. Elektrotehniski vestnik [English print ed.], 78(5):270-274, 2011.
- R. Krummenacher and T. Strang. Ontology-based context modeling. In In Workshop on Context-Aware Proactive Systems, 2007. open in new tab
- O. Lassila and D. McGuinness. The role of frame-based representation on the semantic web. Technical report, Knowledge Systems Laboratory Report KSL-01-02, Stanford University, Stanford (USA), 2001. open in new tab
- D. Lenat. The dimensions of context space. Technical report, Cycorp, 1998. open in new tab
- V. Maidel, P. Shoval, B. Shapira, and M. Taieb-Maimon. Evaluation of an ontology- content based filtering method for a personalized newspaper. In Proc. of the 2008 ACM Conf. on Recommender Systems, RecSys '08, pages 91-98, New York, NY, USA, 2008. ACM. open in new tab
- A. Maksai, F. Garcin, and B. Faltings. Predicting online performance of news rec- ommender systems through richer evaluation metrics. In Proceedings of the 9th ACM Conference on Recommender Systems, RecSys '15, pages 179-186, New York, NY, USA, 2015. ACM. open in new tab
- J. McCarthy. Notes on formalizing context. In Proceedings of the 13th International Joint Conference on Artifical Intelligence -Volume 1, IJCAI'93, pages 555-560, San Francisco, CA, USA, 1993. Morgan Kaufmann Publishers Inc. open in new tab
- S. E. Middleton, N. R. Shadbolt, and D. C. De Roure. Ontological user profiling in recommender systems. ACM Trans. Inf. Syst., 22(1):54-88, Jan. 2004. open in new tab
- X. Ning and G. Karypis. Slim: Sparse linear methods for top-n recommender systems. In 2011 IEEE 11th International Conference on Data Mining, pages 497-506, Dec 2011. open in new tab
- A. Odic, M. Tkalcic, J. F. Tasic, and A. Kosir. Predicting and detecting the relevant contextual information in a movie-recommender system. Interacting with Computers, 25(1):74-90, 2013.
- W. OWL Working Group. OWL 2 Web Ontology Language: Document Overview. W3C Recommendation, 27 October 2009. Available at http://www.w3.org/TR/ owl2-overview/. open in new tab
- K. Pal and S. Michel. A data mining approach to choosing categorical attributes for ranked lists. In E. Pitoura, S. Maabout, G. Koutrika, A. Marian, L. Tanca, I. Manolescu, and K. Stefanidis, editors, Proceedings of the 19th International Con- ference on Extending Database Technology, EDBT 2016, Bordeaux, France, March 15- 16, 2016, Bordeaux, France, March 15-16, 2016., pages 664-665. OpenProceedings.org, 2016.
- K. Pearson. Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London, 58:240-242, 1895. open in new tab
- M. Perry and G. D. Kader. Variation as unalikeability. Teaching Statistics, 27(2):58-60, 2005. open in new tab
- D. Preuveneers, J. Bergh, D. Wagelaar, A. Georges, P. Rigole, T. Clerckx, Y. Berbers, K. Coninx, V. Jonckers, and K. Bosschere. Ambient Intelligence: Second European Symposium, EUSAI 2004, Eindhoven, The Netherlands, November 8-11, 2004. Proc., chapter Towards an Extensible Context Ontology for Ambient Intelligence, pages 148- 159. Springer Berlin Heidelberg, Berlin, Heidelberg, 2004. open in new tab
- C. Rack, S. Arbanowski, and S. Steglich. Context-aware, Ontology-based Recommen- dations. In SAINT-W '06: Proc. of the Int. Symposium on Applications on Internet Workshops, pages 98-104, Washington, DC, USA, 2006. IEEE Computer Society. open in new tab
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian per- sonalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI '09, pages 452-461, Arlington, Virginia, United States, 2009. AUAI Press.
- F. Ricci, L. Rokach, and B. Shapira. Introduction to recommender systems handbook. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, pages 1-35. Springer US, 2011. open in new tab
- J. Rodríguez, M. Bravo, and R. Guzmán. Multidimensional ontology model to support context-aware systems. In AAAI Workshops, 2013.
- B. Schilit, N. Adams, and R. Want. Context-aware computing applications. In Pro- ceedings of the 1994 First Workshop on Mobile Computing Systems and Applications, WMCSA '94, pages 85-90, Washington, DC, USA, 1994. IEEE Computer Society. open in new tab
- L. Serafini and P. Bouquet. Comparing formal theories of context in ai. Artif. Intell., 155(1-2):41-67, May 2004. open in new tab
- G. Shani and A. Gunawardana. Handbook on recommender systems. chapter Evalu- ating Recommendation Systems, pages 257-298. Springer, 2011. open in new tab
- A. Singhal. Modern information retrieval: a brief overview. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 24(4):35-42, 2001. open in new tab
- M. K. Smith, C. Welty, and D. L. McGuinness. Owl web ontology language guide. World Wide Web Consortium, Recommendation REC-owl-guide-20040210, February 2004.
- B. Smyth. Case-based recommendation. In P. Brusilovsky, A. Kobsa, and W. Nejdl, editors, The Adaptive Web: Methods and Strategies of Web Personalization, pages 342- 376, Berlin, Heidelberg, 2007. Springer Berlin Heidelberg. open in new tab
- B. Smyth and P. McClave. Similarity vs. diversity. In D. W. Aha and I. Watson, editors, Case-Based Reasoning Research and Development: 4th International Conference on Case-Based Reasoning, ICCBR 2001 Vancouver, BC, Canada, July 30 -August 2, 2001 Proceedings, pages 347-361, Berlin, Heidelberg, 2001. Springer Berlin Heidelberg. open in new tab
- H. E. Soper, A. W. Young, B. M. Cave, A. Lee, and K. Pearson. On the distribution of the correlation coefficient in small samples. appendix ii to the papers of "student" and r. a. fisher. a cooperative study. Biometrika, 11(4):328-413, 1917. open in new tab
- J. B. Spira. Overload!: How Too Much Information is Hazardous to your Organization. John Wiley & Sons Inc., 2012. open in new tab
- R. Studer, V. R. Benjamins, and D. Fensel. Knowledge engineering: Principles and methods. Data Knowl. Eng., 25(1-2):161-197, Mar. 1998. open in new tab
- P. Tomeo, T. D. Noia, M. de Gemmis, P. Lops, G. Semeraro, and E. D. Sciascio. Ex- ploiting regression trees as user models for intent-aware multi-attribute diversity. In T. Bogers and M. Koolen, editors, Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender Systems co-located with 9th ACM Conference on Rec- ommender Systems (RecSys 2015), Vienna, Austria, September 16-20, 2015., volume 1448 of CEUR Workshop Proceedings, pages 2-9. CEUR-WS.org, 2015.
- A. Turpin and F. Scholer. User performance versus precision measures for simple search tasks. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '06, pages 11-18, New York, NY, USA, 2006. ACM. open in new tab
- B. Vargas-Govea, G. Gonzalez-Serna, and R. Ponce-Medellin. Effects of relevant con- textual features in the performance of a restaurant recommender system. In Proceedings of 3rd Workshop on Context-Aware Recommender Systems, 2011.
- A. Waloszek. Hierarchiczna kontekstualizacja baz wiedzy. PhD thesis, Gdańsk Univer- sity of Technology, 2010.
- X. H. Wang, D. Q. Zhang, T. Gu, and H. K. Pung. Ontology based context modeling and reasoning using owl. In Proc. of the Second IEEE Annual Conf. on Pervasive Computing and Communications Workshops, PERCOMW '04, pages 18-, Washington, DC, USA, 2004. IEEE Computer Society.
- M. Wooldridge. An Introduction to MultiAgent Systems. Wiley Publishing, 2nd edition, 2009.
- Y. C. Zhang, D. O. Séaghdha, D. Quercia, and T. Jambor. Auralist: Introducing serendipity into music recommendation. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM '12, pages 13-22, New York, NY, USA, 2012. ACM. open in new tab
- Y. Zheng, R. Burke, and B. Mobasher. Splitting approaches for context-aware recom- mendation: An empirical study. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC '14, pages 274-279, New York, NY, USA, 2014. ACM. open in new tab
- Y. Zheng, B. Mobasher, and R. Burke. Cslim: Contextual slim recommendation algo- rithms. In Proceedings of the 8th ACM Conference on Recommender Systems, RecSys '14, pages 301-304, New York, NY, USA, 2014. ACM. open in new tab
- Y. Zheng, B. Mobasher, and R. D. Burke. Carskit: A java-based context-aware rec- ommendation engine. In ICDM Workshops, pages 1668-1671. IEEE Computer Society, 2015. open in new tab
- C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In Proceedings of the 14th International Conference on World Wide Web, WWW '05, pages 22-32, New York, NY, USA, 2005. ACM. open in new tab
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