This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
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.Search in Google Scholar
Agarwal D. and Chen B.-C. flda: matrix factorization through latent dirichlet allocation. In WSDM, pages 91–100. ACM, 2010.Search in Google Scholar
Aggarwal C. C. Recommender Systems: The Textbook. Springer Publishing Company, Incorporated, 1st edition, 2016.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
Bergamaschi S., Po L., and Sorrentino S. Comparing topic models for a movie recommendation system. In WEBIST (2), pages 172–183. SciTePress, 2014.Search in Google Scholar
Blei D. and McAuliffe J. Supervised topic models. In Advances in Neural Information Processing Systems, 2007.Search in Google Scholar
Blei D. M., Ng A. Y., Jordan M. I., and Lafferty J. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, 2003.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
Christopher D. Manning H. S., Prabhakar Raghavan. Scoring, term weighting and the vector space model. Introduction to Information Retrieval, Cambridge University Press, 2008.Search in Google Scholar
Daniel B. and J. P. M. User modeling for adaptive news access. User Modelling and User-Adapted Interaction, 10:147 – 180, 2000.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
Ehrlinger L. and Wöß W. Towards a definition of knowledge graphs. In SEMANTiCS (Posters, Demos, SuCCESS), 2016.Search in Google Scholar
Eirinaki M., Gao J., Varlamis I., and Tserpes K. Recommender systems for large-scale social networks: A review of challenges and solutions. Future Generation Comp. Syst., 78:413–418, 2018.Search in Google Scholar
Elahi M., Ricci F., and Rubens N. A survey of active learning in collaborative filtering recommender systems. Computer Science Review, 20:29–50, 2016.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
Farrugia J. Model-theoretic semantics for the web. In Hencsey G., White B., Chen Y. R., Kovács 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.Search in Google Scholar
Garner R. An abstract view on syntax with sharing. Journal of Logic and Computation, 22(6):1427–1452, 09 2011.Search in Google Scholar
Gori M. and Pucci A. Itemrank: a random-walk based scoring algorithm for recommender engines. IJCAI Conference, pages 2766–2771, 2007.Search in Google Scholar
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.Search in Google Scholar
Griffiths T. L., Steyvers M., and Tenenbaum J. Topics in semantic representation. Psychological Review, in press, 2007.Search in Google Scholar
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.Search in Google Scholar
Horrocks I. and Patel-Schneider P. F. Three theses of representation in the semantic web. In Hencsey G., White B., Chen Y. R., Kovács 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.Search in Google Scholar
Hotho A., Jäschke R., Schmitz C., and Stumme G. Information retrieval in folk-sonomies: Search and ranking. In The Semantic Web: Research and Applications, pages 411–426, Berlin, Heidelberg, 2006. Springer Berlin Heidelberg.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
Isinkaye F., Folajimi Y., and Ojokoh B. Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3):261 – 273, 2015.Search in Google Scholar
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.Search in Google Scholar
Jones K. S. A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28:11–21, 1972.Search in Google Scholar
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.Search in Google Scholar
Khusro S., Ali Z., and Ullah I. Recommender Systems: Issues, Challenges, and Research Opportunities, pages 1179–1189. Springer Singapore, Singapore, 2016.Search in Google Scholar
Kleinberg J. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), pages 604–632, 1999.Search in Google Scholar
Kumar B. and Sharma N. Approaches, issues and challenges in recommender systems: A systematic review. Indian Journal of Science and Technology, 9(47), 2016.Search in Google Scholar
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.Search in Google Scholar
Leung C. Sentiment analysis of product reviews. In Encyclopedia of Data Warehousing and Mining, Second Edition (4 Volumes), pages 1794–1799. IGI Global, 2009.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
Mladenic D. Text-learning and related intelligent agents: a survey. IEEE Intelligent Systems and their Applications, 14(4):44–54, July 1999.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
Papadakis H., Panagiotakis C., and Fragopoulou P. Scor: A synthetic coordinate based recommender system. Expert Systems with Applications, 79:8–19, 2017.Search in Google Scholar
Park L. A. F. and Ramamohanarao K. An analysis of latent semantic term self-correlation. ACM Trans. Inf. Syst., 27, 2009.Search in Google Scholar
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.Search in Google Scholar
Paulheim H. Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web, 8:489–508, 12 2016.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
R.Manjula A. C. Content based filtering techniques in recommendation system using user preferences. International Journal of Innovations in Engineering and Technology, 7, 2016.Search in Google Scholar
Robertson S. Understanding inverse document frequency: On theoretical arguments for idf. Journal of Documentation, 60, 2004.Search in Google Scholar
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.Search in Google Scholar
Salton G. Y. C. S. On the specification of term values in automatic indexing. Journal of Documentation, 29:351–372, 1973.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
Sharma L. and Gera A. A survey of recommendation system research challenges. In International Journal of Engineering Trends and Technology (IJETT), 2013.Search in Google Scholar
Sharma L. and Gera A. A survey of recommendation system: Research challenges. In International Journal of Engineering Trends and Technology (IJETT), 2013.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
Shimazu H. Expertclerk: Navigating shoppers’ buying process with the combination of asking and proposing. International Joint Conferences on Artificial Intelligence, pages 1443–1448, 2001.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
Vanderwal T. Off the top: Folksonomy entries. http://www.vanderwal.net, 2007.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
Wang X., Wang D., Xu C., He X., Cao Y., and Chua T.-S. Explainable reasoning over knowledge graphs for recommendation. In AAAI, 2018.Search in Google Scholar
Warnestal P. User evaluation of a conversational recommender system. In Proceedings of the 4th Workshop on Knowledge and Reasoning in Practical Dialogue Systems, 2005.Search in Google Scholar
Xie W., Dong Q.,, and Gao H. A probabilistic recommendation method inspired by latent dirichlet allocation model. Mathematical Problems in Engineering, 2014, 2014.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
Zisopoulos C., Karagiannidis S., Demirtsoglou G., and Antaris S. Content-based recommendation systems. 11 2008.Search in Google Scholar