Content-Based Recommender Systems Taxonomy

• Autorzy: Papadakis Harris , Papagrigoriou Antonis , Kosmas Eleftherios , Panagiotakis Costas , Markaki Smaragda , Fragopoulou Paraskevi

Identyfikatory

DOI

10.2478/fcds-2023-0009

• Języki publikacji: EN

Abstrakty

EN

In the era of internet access, recommender systems try to alleviate the difficulty consumers face while trying to find items (e.g. services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of content-based recommender systems, and not only in one part of it. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a content-based recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of content-based recommender systems according to their ability to efficiently handle well-known drawbacks.

Słowa kluczowe

EN

content-based systems; recommendation systems; survey; taxonomy

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2023
• Tom: Vol. 48, No. 2
• Strony: 211--241
• Opis fizyczny: Bibliogr. 89 poz., rys., tab.

Twórcy

autor

Papadakis Harris

• Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004 Heraklion, Greece

autor

Papagrigoriou Antonis

• Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004 Heraklion, Greece

autor

Kosmas Eleftherios

• Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004 Heraklion, Greece

autor

Panagiotakis Costas

• Department of Management Science and Technology, Hellenic Mediterranean University, 72100 Agios Nikolaos, Greece

autor

Markaki Smaragda

• Department of Management Science and Technology, Hellenic Mediterranean University, 72100 Agios Nikolaos, Greece

autor

Fragopoulou Paraskevi

• Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004 Heraklion, Greece

Bibliografia

• [1] Adomavicius G. and Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734-749, June 2005.
• [2] Agarwal D. and Chen B.-C. flda: matrix factorization through latent Dirichlet allocation. In WSDM, pages 91-100. ACM, 2010.
• [3] Aggarwal C. C. Recommender Systems: The Textbook. Springer Publishing Company, Incorporated, 1st edition, 2016.
• [4] Aggarwal C. C. et al. Recommender systems. Springer, 2016.
• [5] Bauman K., Liu B., and Tuzhilin A. Estimating customer reviews in recommender systems using sentiment analysis methods. In Conference on Information Systems and Technology (CIST), 2015.
• [6] Begelman G., Keller P., and Smadja F. Automated tag clustering: Improving search and exploration in the tag space. Proceedings of the 15th International Conference on World Wide Web, 2006.
• [7] Bergamaschi S., Po L., and Sorrentino S. Comparing topic models for a movie recommendation system. In WEBIST (2), pages 172-183. SciTePress, 2014.
• [8] Blei D. and McAuliffe J. Supervised topic models. In Advances in Neural Information Processing Systems, 2007.
• [9] Blei D. M., Ng A. Y., Jordan M. I., and Lafferty J. Latent dirichlet allocation. Journal of Machine Learning Research, 3: 993-1022, 2003.
• [10] Brin S. and Page L. The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30, pages 1-7, 1998.
• [11] C. A. Thompson M. H. G. and Langley P. A personalized system for conversational recommendations. Proceedings of the Journal of Artificial Intelligence Research 21.1, pages 393-428, 2004.
• [12] Catherine R. and Cohen W. Personalized recommendations using knowledge graphs: A probabilistic logic programming approach. In Proceedings of the 10th ACM Conference on Recommender Systems, RecSys '16, pages 325-332, New York, NY, USA, 2016. ACM.
• [13] Christopher D. Manning H. S., Prabhakar Raghavan. Scoring, term weighting and the vector space model. Introduction to Information Retrieval, Cambridge University Press, 2008.
• [14] Daniel B. and J. P. M. User modeling for adaptive news access. User Modelling and User-Adapted Interaction, 10: 147-180, 2000.
• [15] de Gemmis M., Lops P., Semeraro G., and Basile P. Integrating tags in a semantic content-based recommender. In Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys'08, pages 163-170, New York, NY, USA, 2008. ACM.
• [16] Deerwester S., Dumais S., Furnas G., Landauer T., and Harshman R. Indexing by latent semantic analysis. Journal of the American Society for Information Science 41, pages 391-407, 1990.
• [17] Ehrlinger L. and Wöß W. Towards a definition of knowledge graphs. In SEMAN-TiCS (Posters, Demos, SuCCESS), 2016.
• [18] Eirinaki M., Gao J., Varlamis I., and Tserpes K. Recommender systems for largescale social networks: A review of challenges and solutions. Future Generation Comp. Syst., 78: 413-418, 2018.
• [19] Elahi M., Ricci F., and Rubens N. A survey of active learning in collaborative filtering recommender systems. Computer Science Review, 20: 29-50, 2016.
• [20] F. Fouss A. Pirotte J. R. and Saerens M. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering, 19(3), pages 355-369, 2007.
• [21] Farinella T., Bergamaschi S., and Po L. A non-intrusive movie recommendation system. In On the Move to Meaningful Internet Systems: OTM 2012, pages 736-751, Berlin, Heidelberg, 2012. Springer Berlin Heidelberg.
• [22] Farrugia J. Model-theoretic semantics for the web. In Hencsey G., White B., Chen Y. R., Kovacs L., and Lawrence S., editors, Proceedings of the Twelfth International World Wide Web Conference, WWW 2003, Budapest, Hungary, May 20-24, 2003, pages 29-38. ACM, 2003.
• [23] Garner R. An abstract view on syntax with sharing. Journal of Logic and Computation, 22(6): 1427-1452, 09 2011.
• [24] Gori M. and Pucci A. Itemrank: a random-walk based scoring algorithm for recommender engines. IJCAI Conference, pages 2766-2771, 2007.
• [25] Grad-Gyenge L., Filzmoser P., and Werthner H. Recommendations on a knowledge graph. In 1st International Workshop on Machine Learning Methods for Recommender Systems, 05 2015.
• [26] Griffths T. L., Steyvers M., and Tenenbaum J. Topics in semantic representation. Psychological Review, in press, 2007.
• [27] He Y. A bayesian modeling approach to multi-dimensional sentiment distributions prediction. In Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining, WISDOM'12, pages 1:1-1:8, New York, NY, USA, 2012. ACM.
• [28] Horrocks I. and Patel-Schneider P. F. Three theses of representation in the semantic web. In Hencsey G., White B., Chen Y. R., Kovacs L., and Lawrence S., editors, Proceedings of the Twelfth International World Wide Web Conference, WWW 2003, Budapest, Hungary, May 20-24, 2003, pages 39-47. ACM, 2003.
• [29] Hotho A., Jäschke R., Schmitz C., and Stumme G. Information retrieval in folksonomies: Search and ranking. In The Semantic Web: Research and Applications, pages 411-426, Berlin, Heidelberg, 2006. Springer Berlin Heidelberg.
• [30] Huang Z., Chung W., Ong T.-H., and Chen H. A graph-based recommender system for digital library. Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries, pages 65-73, 2002.
• [31] Iaquinta L., D. Gemmis M., Lops P., Semeraro G., Filannino M., and Molino P. Introducing serendipity in a content-based recommender system. In 2008 Eighth International Conference on Hybrid Intelligent Systems, pages 168-173, Sep. 2008.
• [32] Illig J., Hotho A., Jaschke R., and Stumme G. A comparison of content-based tag recommendations in folksonomy systems. Knowledge Processing and Data Analysis, pages 136-149, 2011.
• [33] Isinkaye F., Folajimi Y., and Ojokoh B. Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3): 261-273, 2015.
• [34] Joachims T. A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. Proceedings of ICML-97, 14th International Conference on Machine Learning, Morgan Kaufmann Publishers, San Francisco, US, Nashville, US, pages 143-151, 1997.
• [35] Jones K. S. A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28: 11-21, 1972.
• [36] Julien Subercaze F. L., Christophe Gravier. Hashgraph an expressive and scalable twitter users profile for recommendation. EEE/WIC/ACM International Conference on Web Intelligence (WI'13), pages 101-108, 2013.
• [37] Khusro S., Ali Z., and Ullah I. Recommender Systems: Issues, Challenges, and Research Opportunities, pages 1179-1189. Springer Singapore, Singapore, 2016.
• [38] Kleinberg J. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), pages 604-632, 1999.
• [39] Kumar B. and Sharma N. Approaches, issues and challenges in recommender systems: A systematic review. Indian Journal of Science and Technology, 9(47), 2016.
• [40] Lau A., Tsui E., and Lee W. An ontology-based similarity measurement for problem-based case reasoning. Expert Systems with Applications, 36(3), Part 2: 6574-6579, 2009.
• [41] Leung C. Sentiment analysis of product reviews. In Encyclopedia of Data Warehousing and Mining, Second Edition (4 Volumes), pages 1794-1799. IGI Global, 2009.
• [42] Lin J. On the dirichlet distribution by jiayu lin. Submitted to the Department of Mathematics and Statistics of Queen's University Kingston, Ontario, Canada in conformity with the requirements for the degree of Master of Science, 2016.
• [43] Marinho L. B., Nanopoulos A., Schmidt-Thieme L., Jäschke R., Hotho A., Stumme G., and Symeonidis P. Chapter 19, Social Tagging Recommender Systems, chapter Folksonomies as Hypergraphs, pages 615-644. Springer US, Boston, MA, 2011.
• [44] Mladenic D. Text-learning and related intelligent agents: a survey. IEEE Intelligent Systems and their Applications, 14(4): 44-54, July 1999.
• [45] Nguyen T. T., Hui P.-M., Harper F. M., Terveen L., and Konstan J. A. Exploring the filter bubble: The effect of using recommender systems on content diversity. In Proceedings of the 23rd International Conference on World Wide Web, WWW'14, pages 677-686, New York, NY, USA, 2014. ACM.
• [46] Oramas S., Ostuni V. C., Noia T. D., Serra X., and Sciascio E. D. Sound and music recommendation with knowledge graphs. ACM Transactions on Intelligent Systems and Technology (TIST), 8(2): 1-21, 2016.
• [47] Ovsjanikov M. and Chen Y. Topic modeling for personalized recommendation of volatile items. In Balcazar J. L., Bonchi F., Gionis A., and Sebag M., editors, ECML/PKDD (2), volume 6322 of Lecture Notes in Computer Science, pages 483-498. Springer, 2010.
• [48] Panagiotakis C., Papadakis H., Papagrigoriou A., and Fragopoulou P. Improving recommender systems via a dual training error based correction approach. Expert Systems with Applications, page 115386, 2021.
• [49] Pang B., Lee L., and Vaithyanathan S. Thumbs up? sentiment classification using machine learning techniques. In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10, EMNLP'02, page 79-86, USA, 2002. Association for Computational Linguistics.
• [50] Papadakis H., Panagiotakis C., and Fragopoulou P. Scor: A synthetic coordinate based recommender system. Expert Systems with Applications, 79: 8-19, 2017.
• [51] Park L. A. F. and Ramamohanarao K. An analysis of latent semantic term self-correlation. ACM Trans. Inf. Syst., 27, 2009.
• [52] Pasquale Lops M. D. G. and Semeraro G. Content-based recommender systems: State of the art and trends. In Recommender Systems Handbook, pages 73-106. Springer-Verlag, Berlin, Heidelberg, 2010.
• [53] Paulheim H. Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web, 8: 489-508, 12 2016.
• [54] Popescu A.-M. and Etzioni O. Extracting product features and opinions from reviews. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT'05, pages 339-346, Stroudsburg, PA, USA, 2005. Association for Computational Linguistics.
• [55] R. M. and P. T. TextRank: Bringing order into texts. Proceedings of EMNLP-04 and the 2004 Conference on Empirical Methods in Natural Language Processing, 2004.
• [56] Rao V., V R. K., and Padmanabhan V. Divide and transfer: Understanding latent factors for recommendation tasks. In RecSysKTL, volume 1887 of CEUR Workshop Proceedings, pages 1-8. CEUR-WS.org, 2017.
• [57] Rendle S., Balby Marinho L., Nanopoulos A., and Schmidt-Thieme L. Learning optimal ranking with tensor factorization for tag recommendation. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'09, pages 727-736, New York, NY, USA, 2009. ACM.
• [58] R. Manjula A. C. Content based filtering techniques in recommendation system using user preferences. International Journal of Innovations in Engineering and Technology, 7, 2016.
• [59] Robertson S. Understanding inverse document frequency: On theoretical arguments for idf. Journal of Documentation, 60, 2004.
• [60] Salton G., Wong A., and Yang C. A vector space model for automatic indexing. Communications of the ACM, vol. 18, no. 11, pages 613-620, 1975.
• [61] Salton G. Y. C. S. On the specification of term values in automatic indexing. Journal of Documentation, 29: 351-372, 1973.
• [62] Schein A. I., Popescul A., Ungar L. H., and Pennock D. M. Methods and metrics for cold-start recommendations. In Järvelin K., Beaulieu M., Baeza-Yates R. A., and Myaeng S., editors, SIGIR 2002: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 11-15, 2002, Tampere, Finland, pages 253-260. ACM, 2002.
• [63] Shahabi C. and Chen Y.-S. Web information personalization: Challenges and approaches. In Bianchi-Berthouze N., editor, Databases in Networked Information Systems, pages 5-15, Berlin, Heidelberg, 2003. Springer Berlin Heidelberg.
• [64] Sharma L. and Gera A. A survey of recommendation system research challenges. In International Journal of Engineering Trends and Technology (IJETT), 2013.
• [65] Sharma L. and Gera A. A survey of recommendation system: Research challenges. In International Journal of Engineering Trends and Technology (IJETT), 2013.
• [66] Shepitsen A., Gemmell J., Mobasher B., and Burke R. Personalized recommendation in social tagging systems using hierarchical clustering. In Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys'08, pages 259-266, New York, NY, USA, 2008. ACM.
• [67] Shi Y., Larson M., and Hanjalic A. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Comput. Surv., 47(1):3:1-3:45, May 2014.
• [68] Shimazu H. Expertclerk: Navigating shoppers' buying process with the combination of asking and proposing. International Joint Conferences on Artificial Intelligence, pages 1443-1448, 2001.
• [69] Sun Y., Han J., Yan X., Yu P. S., and Wu T. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. In In VLDB' 11, 2011.
• [70] Symeonidis P., Nanopoulos A., and Manolopoulos Y. Tag recommendations based on tensor dimensionality reduction. In Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys'08, pages 43-50, New York, NY, USA, 2008. ACM.
• [71] Terán L., Mensah A. O., and Estorelli A. A literature review for recommender systems techniques used in microblogs. Expert Systems with Applications, 103: 63-73, 2018.
• [72] Thivakaran T. and Nedunchelian R. Recommendation system for the long tail problem using factorization through latent dirichlet allocation. In Middle-East Journal of Scientific Research 23, 2015.
• [73] Tso-Sutter K. H. L., Marinho L. B., and Schmidt-Thieme L. Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proceedings of the 2008 ACM Symposium on Applied Computing, SAC'08, pages 1995-1999, New York, NY, USA, 2008. ACM.
• [74] Turney P. D. and Pantel P. From frequency to meaning: vector space models of semantics. Journal of artificial intelligence research, vol. 37, no. 1, pages 141-188, 2010.
• [75] Vanderwal T. Off the top: Folksonomy entries. http://www.vanderwal.net, 2007.
• [76] Vig J., Sen S., and Riedl J. Tagsplanations: explaining recommendations using tags. In Proceedings of the 14th International Conference on Intelligent User Interfaces, IUI 2009, Sanibel Island, Florida, USA, February 8-11, 2009, pages 47-56, 2009.
• [77] Wang Q., Mao Z., Wang B., and Guo L. Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29: 2724-2743, 2017.
• [78] Wang X., Wang D., Xu C., He X., Cao Y., and Chua T.-S. Explainable reasoning over knowledge graphs for recommendation. In AAAI, 2018.
• [79] Warnestal P. User evaluation of a conversational recommender system. In Proceedings of the 4th Workshop on Knowledge and Reasoning in Practical Dialogue Systems, 2005.
• [80] Xie W., Dong Q., and Gao H. A probabilistic recommendation method inspired by latent dirichlet allocation model. Mathematical Problems in Engineering, 2014, 2014.
• [81] Xie W., Ouyang Y., Ouyang J., Rong W., and Xiong Z. User occupation aware conditional restricted boltzmann machine based recommendation. In Internet of Things (iThings) and IEEE Green Computing and Communications (Green-Com) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2016 IEEE International Conference on, pages 454-461. IEEE, 2016.
• [82] Yamamoto Y., Kumamoto T., and Nadamoto A. Role of emoticons for multidimensional sentiment analysis of twitter. In Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services, iiWAS'14, pages 107-115, New York, NY, USA, 2014. ACM.
• [83] Yu X., Ren X., Sun Y., Gu Q., Sturt B., Khandelwal U., Norick B., and Han J. Personalized entity recommendation: A heterogeneous information network approach. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM'14, pages 283-292, New York, NY, USA, 2014. ACM.
• [84] Z. S. Syed T. F. and Joshi A. Wikipedia as an ontology for describing documents. Proceedings of the Second International Conference on Weblogs and Social Media, AAAI Press, 2008.
• [85] Zanardi V. and Capra L. Social ranking: Uncovering relevant content using tag-based recommender systems. In Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys'08, pages 51-58, New York, NY, USA, 2008. ACM.
• [86] Zhang F., Yuan N. J., Lian D., Xie X., and Ma W.-Y. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'16, pages 353-362, New York, NY, USA, 2016. ACM.
• [87] Zhang Y., Liu R., and Li A. A novel approach to recommender system based on aspect-level sentiment analysis. 4th National Conference on Electrical, Electronics and Computer Engineering, 2016.
• [88] Ziani A., Azizi N., Schwab D., Aldwairi M., and Chekkai N. Recommender system through sentiment analysis. 2nd International Conference on Automatic Control, Telecommunications and Signals, 2017.
• [89] Zisopoulos C., Karagiannidis S., Demirtsoglou G., and Antaris S. Content-based recommendation systems. 11 2008.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).