ABSTRACT
Reading is an important activity for individuals. Content-based recommendation systems are, typically, used to recommend scientific papers or news, where search is driven by topic. Literary reading or reading for leisure differs from scientific reading, because users search books not only for their topic but also by author or writing style. Choosing a new book to read can be tricky and recommendation systems can make it easy by selecting books that the user will like. In this paper we study recommendation through writing style and the influence of negative examples in user preferences. Our experiments were conducted in a hybrid set-up that combines a collaborative filtering algorithm with stylometric relevance feedback. Using the LitRec data set, we demonstrate that writing style influences book selection; that book content, characterized with writing style, can be used to improve collaborative filtering results; and that negative examples do not improve final predictions.
- G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6):734--749, 2005. Google ScholarDigital Library
- M. Balabanović and Y. Shoham. Fab: content-based, collaborative recommendation. Commun. ACM, 40(3):66--72, Mar. 1997. Google ScholarDigital Library
- D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, Mar. 2003. Google ScholarCross Ref
- R. Burke. Hybrid web recommender systems. In P. Brusilovsky, A. Kobsa, and W. Nejdl, editors, The adaptive web, pages 377--408. Springer-Verlag, Berlin, Heidelberg, 2007. Google ScholarDigital Library
- B. Chikhaoui, M. Chiazzaro, and S. Wang. An improved hybrid recommender system by combining predictions. In Proc. of the 2011 IEEE WS of International Conf. on Advanced Information Networking and App., WAINA '11, pages 644--649, Washington, DC, USA, 2011. IEEE Computer Society. Google ScholarDigital Library
- A. Gunawardana and C. Meek. A unified approach to building hybrid recommender systems. In Proc. of the third ACM conference on Recommender systems, RecSys '09, pages 117--124, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- J. Kamps. The impact of author ranking in a library catalogue. In Proc. of the 4th ACM workshop on Online books, complementary social media and crowdsourcing, BooksOnline '11, pages 35--40, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- M. Lee, P. Choi, and Y. Woo. A hybrid recommender system combining collaborative filtering with neural network. In Proc. of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH '02, pages 531--534, London, UK, UK, 2002. Springer-Verlag. Google ScholarDigital Library
- Q. Li and B. Kim. An approach for combining content-based and collaborative filters. In Proc. of the sixth international workshop on Information retrieval with Asian languages - Volume 11, AsianIR '03, pages 17--24, Stroudsburg, PA, USA, 2003. Association for Computational Linguistics. Google ScholarDigital Library
- P. Lops, M. Gemmis, and G. Semeraro. Content-based recommender systems: State of the art and trends. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, pages 73--105. Springer US, 2011.Google ScholarCross Ref
- R. Mooney and L. Roy. Content-based book recommending using learning for text categorization. In Proc. of the fifth ACM conference on Digital libraries, DL '00, pages 195--204, New York, NY, USA, 2000. ACM. Google ScholarDigital Library
- M. Pazzani. A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev., 13(5--6):393--408, Dec. 1999. Google ScholarDigital Library
- J. Rocchio. Relevance Feedback in Information Retrieval, pages 313--323. Prentice-Hall, 1971.Google Scholar
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, WWW '01, pages 285--295, New York, NY, USA, 2001. ACM. Google ScholarDigital Library
- Y. Seroussi, I. Zukerman, and F. Bohnert. Authorship attribution with latent dirichlet allocation. In Proceedings of the Fifteenth Conference on Computational Natural Language Learning, CoNLL '11, pages 181--189, Stroudsburg, PA, USA, 2011. Association for Computational Linguistics. Google ScholarDigital Library
- E. Stamatatos. A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol., 60(3):538--556, Mar. 2009. Google ScholarDigital Library
- X. Su and T. Khoshgoftaar. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009:1--20, 2009. Google ScholarDigital Library
- M. Utiyama and M. Yamamoto. Relevance feedback models for recommendation. In Proc. of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP '06, pages 449--456, Stroudsburg, PA, USA, 2006. Association for Computational Linguistics. Google ScholarDigital Library
- P. C. Vaz, D. M. de Matos, B. Martins, and P. Calado. Improving a hybrid literary book recommendation system through author ranking. In Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries, JCDL '12, pages 387--388, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
Index Terms
- Stylometric relevance-feedback towards a hybrid book recommendation algorithm
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Improving a hybrid literary book recommendation system through author ranking
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