Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-10T14:19:59.415Z Has data issue: false hasContentIssue false

SongRecommend: From summarization to recommendation

Published online by Cambridge University Press:  28 September 2012

SWATI TATA
Affiliation:
Versay Solutions, Chicago, IL, USA e-mail: swtata@gmail.com
BARBARA DI EUGENIO
Affiliation:
Department of Computer Science, University of Illinois, Chicago, IL, USA e-mail: bdieugen@uic.edu

Abstract

In recent years, the availability of too much information has become a fact of life for anybody connected with the Internet. The same is true for music: because of the penetration of portable devices and the availability of millions of tracks on the web, individual music collections have become unwieldy. Users need tools to help search their own song collections, and to recommend songs they may be interested in. Whereas recommendation systems have been developed for a variety of products, a music recommendation system presents special challenges, including the ability to recommend individual songs, as opposed to entire albums, even if only full album reviews are available on-line. SongRecommend, our music recommendation system, combines information extraction and generation techniques to produce summaries of reviews of individual songs from album reviews. We present a number of evaluations for SongRecommend: intrinsic evaluations of the extraction components, and of the informativeness of the summaries; and a user study of the impact of the song review summaries on users’ decision-making processes. When presented with the summary, users were able to make quicker decisions, and their choices were more varied. Whereas the smaller size of the summary has an impact on time-on-task, users do not appear to choose a specific recommendation only based on number of words. Our work demonstrates that state-of-the-art techniques in Natural Language Processing can be integrated into an effective end-to-end system.

Type
Articles
Copyright
Copyright © Cambridge University Press 2012 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bangalore, S., and Rambow, O. 2000. Corpus-based lexical choice in natural language generation. In Proceedings of ACL 2000, the 38th Annual Meeting of the Association for Computational Linguistics, Hong Kong, pp. 464–71.Google Scholar
Barzilay, R., and McKeown, K. 2005. Sentence fusion for multidocument news summarization. Computational Linguistics 31 (3): 297328.CrossRefGoogle Scholar
Belz, A., Kow, E., Viethen, J., and Gatt, A. 2010. Generating referring expressions in context: the GREC shared task evaluation challenges. In Krahmer, E. and Theune, M. (eds.), Empirical Methods in Natural Language Generation, pp. 294327. Lecture Notes in Computer Science, Vol. 5980. Berlin, Germany: Springer.CrossRefGoogle Scholar
Benamara, F., Cesarano, C., Picariello, A., Reforgiato, D., and Subrahmanian, V. 2007. Sentiment analysis: adjectives and adverbs are better than adjectives alone. Proceedings of the International Conference on Weblogs and Social Media (ICWSM), Boulder, CO, USA.Google Scholar
Bruce, R., and Wiebe, J. 1999. Recognizing subjectivity: a case study of manual tagging. Natural Language Engineering 5 (2): 187205.CrossRefGoogle Scholar
Cano, P., Koppenberger, M., and Wack, N. 2005. An industrial-strength content-based music recommendation system. In Proceedings of the 28th Annual International ACM SIGIR Conference (SIGIR 2005), Salvador, Brazil, pp. 673–73.Google Scholar
Carenini, G., Ng, R., and Pauls, A. 2006. Multi-document summarization of evaluative text. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2006), Trento, Italy, pp. 305–12.Google Scholar
Carletta, J. 1996. Assessing agreement on classification tasks: the Kappa statistic. Computational Linguistics 22 (2): 249–54.Google Scholar
Celma, Ò. 2006. Interaction Design for Recommender Systems. PhD thesis, Universitat Pompeu Fabra, Barcelona, Spain.Google Scholar
Celma, Ò. 2010. Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space. Berlin, Germany: Springer.CrossRefGoogle Scholar
Corman, S., Kuhn, T., McPhee, R., and Dooley, K. 2002. Studying complex discursive systems: centering resonance analysis of organizational communication. Human Communication Research 28 (2): 157206.Google Scholar
Dale, R., and Reiter, E. 1995. Computational Interpretations of the Gricean Maxims in the Generation of Referring Expressions. Cognitive Science 18: 233–63.CrossRefGoogle Scholar
de Marneffe, M.-C., and Manning, C. D. 2008. Stanford typed dependencies manual. http://nlp.stanford.edu/software/dependencies_manual.pdf. (Accessed 16 Sep 2012).Google Scholar
Di Eugenio, B., Moore, J. D., and Paolucci, M. 1997. Learning features that predict cue usage. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL-EACL97), Madrid, Spain, pp. 80–7.CrossRefGoogle Scholar
Ding, X., Liu, B., and Yu, P. 2008. A holistic lexicon-based approach to opinion mining. In Proceedings of the International Conference on Web Search and Web Data Mining, Palo Alto, CA, USA, pp. 231–40.CrossRefGoogle Scholar
Downie, J. S., and Hu, X. 2006. Review mining for music digital libraries: phase II. In Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries, Chapel Hill, NC, USA, pp. 196–97.CrossRefGoogle Scholar
Esuli, A., and Sebastiani, F. 2006. SentiWordNet: a publicly available lexical resource for opinion mining. Proceedings of the 5th Conference on Language Resources and Evaluation (LREC-06), Genova, Italy.Google Scholar
Fellbaum, C. (ed.) 1998. WordNet: An Electronic Lexical Database. Cambridge, MA, USA: MIT Press.CrossRefGoogle Scholar
Gamon, M., Aue, A., Corston-Oliver, S., and Ringger, E. 2005. Pulse: mining customer opinions from free text. In Advances in Intelligent Data Analysis VI, Lecture Notes in Computer Science, Vol. 3646, pp. 121–32. Berlin, Germany: Springer.CrossRefGoogle Scholar
Goel, S., Broder, A., Gabrilovich, E., and Pang, B. 2010. Anatomy of the long tail: ordinary people with extraordinary tastes. In Proceedings of the Third International Conference on Web Search and Web Data Mining, New York, NY, USA, pp. 201–10.CrossRefGoogle Scholar
Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. 1992. Collaborative filtering to weave an information tapestry. Communications of the ACM 35 (12): 6170.CrossRefGoogle Scholar
Harnly, A., Nenkova, A., Passonneau, R., and Rambow, O. 2005. Automation of summary evaluation by the Pyramid method. Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-2005), Borovets, Bulgaria.Google Scholar
Hatzivassiloglou, V., and McKeown, K. R. 1997. Predicting the semantic orientation of adjectives. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL-EACL97), Madrid, Spain, pp. 174–81.CrossRefGoogle Scholar
Hearst, M. A. 1994. Multi-paragraph segmentation of expository text. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics (ACL99), Las Cruces, NM, USA, pp. 916.CrossRefGoogle Scholar
Higashinaka, R., Prasad, R., and Walker, M. 2006. Learning to generate naturalistic utterances using reviews in spoken dialogue systems. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING/ACL 2006), Sidney, Australia, pp. 265–72.Google Scholar
Hu, M., and Liu, B. 2004. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), Seattle, WA, USA, pp. 168–77.Google Scholar
Jin, W., Ho, H., and Srihari, R. 2009. OpinionMiner: a novel machine learning system for web opinion mining and extraction. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), Paris, France, pp. 1195–204.CrossRefGoogle Scholar
Klein, D., and Manning, C. D. 2002. Fast exact inference with a factored model for natural language parsing. In Proceedings of Advances in Neural Information Processing Systems 15 (NIPS 2002), Vancouver, Canada, pp. 310.Google Scholar
Kleinbauer, T., Becker, S., and Becker, T. 2007. Combining multiple information layers for the automatic generation of indicative meeting abstracts. In Proceedings of the Eleventh European Workshop on Natural Language Generation (ENLG 07), Schloss Dagstuhl, Germany, pp. 151–54.Google Scholar
Krahmer, E., Erk, S., and Verleg, A. 2003. Graph-based generation of referring expressions. Computational Linguistics 29 (1): 5372.CrossRefGoogle Scholar
Le Roux, F., Elkunchwar, R., Ghai, V., Gao, Y., and Lu, J. 2007. A course recommender system using multiple criteria decision making method. Proceedings of the International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007), Chengdu, China.Google Scholar
Levy, M., and Bosteels, K. 2010. Music recommendation and the long tail. Proceedings of the First Workshop on Music Recommendation and Discovery (at ACM RecSys) (WOMRAD 2010), Barcelona, Spain.Google Scholar
Lin, C.-Y. 2004. ROUGE: a package for automatic evaluation of summaries. In Marie-Francine Moens, S. S. (ed.), Proceedings of the Workshop Text Summarization Branches Out (at ACL 2004), Barcelona, Spain, pp. 7481.Google Scholar
Mairesse, F., and Walker, M. 2010. Towards personality-based user adaptation: psychologically informed stylistic language generation. User Modeling and User-Adapted Interaction 20 (3): 227–78.CrossRefGoogle Scholar
Mani, I., and Maybury, M. T. 1999. Automatic Summarization. Boston, MA, USA: The MIT Press.Google Scholar
McRoy, S., Channarukul, S., and Ali, S. 2003. An augmented template-based approach to text realization. Natural Language Engineering 9 (4): 381420.CrossRefGoogle Scholar
Miller, B., Albert, I., Lam, S., Konstan, J., and Riedl, J. 2003. MovieLens unplugged: experiences with an occasionally connected recommender system. In Proceedings of the 8th International Conference on Intelligent User Interfaces (IUI 2003), Miami, FL, USA, pp. 263–66.Google Scholar
Miller, G. A., Chodorow, M., Landes, S., Leacock, C., and Thomas, R. G. 1994. Using a semantic concordance for sense identification. In Proceedings of the Workshop on Human Language Technology (HLT '94), Plainsboro, NJ, USA, pp. 240–43.CrossRefGoogle Scholar
Minnen, G., Carroll, J., and Pearce, D. 2000. Robust, applied morphological generation. In Proceedings of the 1st International Natural Language Generation Conference (INLG 2000), Mitzpe Ramon, Israel, pp. 201–8.Google Scholar
Mitchell, T. 1997. Machine Learning. Burr Ridge, NJ, USA: McGraw Hill.Google Scholar
Nastase, V. 2008. Topic-driven multi-document summarization with encyclopedic knowledge and spreading activation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2008), Edinburgh, Scotland, pp. 763–72.Google Scholar
Nenkova, A., and Passonneau, R. 2004. Evaluating content selection in summarization: the Pyramid method. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL 2004), Boston, MA, USA, pp. 145–52.Google Scholar
Nguyen, P., Mahajan, M., and Zweig, G. 2007. Summarization of multiple user reviews in the restaurant domain. Technical Report MSR-TR-2007-126. Microsoft, Redmond, WA, USA.Google Scholar
Popescu, A., and Etzioni, O. 2005. Extracting product features and opinions from reviews. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), Vancouver, BC, Canada, pp. 339–46.Google Scholar
Raimond, Y., Giasson, F., Jacobson, K., Fazekas, G., Gängler, T., and Reinhardt, S. 2010. Music ontology specification. Specification document. http://musicontology.com/. (Accessed 16 Sep 2012).Google Scholar
Ramshaw, L., and Marcus, M. 1995. Text chunking using transformation-based learning. In Proceedings of the Third ACL Workshop on Very Large Corpora, Cambridge, MA, USA, pp. 8294.Google Scholar
Rich, E. 1979. User modeling via stereotypes. Cognitive Science 3 (4): 329–54.Google Scholar
Saggion, H. 2011. Learning predicate insertion rules for document abstracting. In Gelbukh, A. (ed.), Computational Linguistics and Intelligent Text Processing, pp. 301–12. Lecture Notes in Computer Science, Vol. 6609. Berlin, Germany: Springer.CrossRefGoogle Scholar
Saggion, H., and Funk, A. 2010. Interpreting SentiWordNet for opinion classification. In Calzolari, N., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., and Tapias, D. (eds.), Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), Valletta, Malta.Google Scholar
Saggion, H., and Lapalme, G. 2002. Generating indicative-informative summaries with SumUM. Computational Linguistics 28 (4): 497526.CrossRefGoogle Scholar
Schedl, M., Widmer, G., Pohle, T., and Seyerlehner, K. 2007. Web-based detection of music band members and line-up. Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), Vienna, Austria.Google Scholar
Soubbotin, M., and Soubbotin, S. 2005. Trade-off between factors influencing quality of the summary. Proceedings of the Document Understanding Workshop (DUC 2005), Vancouver, BC, Canada.Google Scholar
SpärckJones, K. Jones, K. 2007. Automatic summarising: the state of the art. Information Processing and Management 43 (6): 1449–81.CrossRefGoogle Scholar
Spärck Jones, K., and Galliers, J. R. 1995. Evaluating Natural Language Processing Systems: An Analysis and Review. Lecture Notes in Computer Science, Vol. 1083. New York, USA: Springer.Google Scholar
Subba, R. 2007. Exploiting event semantics to parse the rhetorical structure of natural language text. In Proceedings of the Doctoral Consortium at NAACL-HLT 2007, the Conference of the North American Chapter for the Association for Computational Linguistics, Rochester, NY, USA, pp. 21–4.Google Scholar
Subba, R. and Di Eugenio, B. 2009. An effective discourse parser that uses rich linguistic information. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Boulder, CO, USA, pp. 566–74.Google Scholar
Tata, S. 2010. SongRecommend: A Music Recommendation System with Fine-Grained Song Reviews. PhD thesis, University of Illinois at Chicago, IL, USA.Google Scholar
Tintarev, N., and Masthoff, J. 2007. Effective explanations of recommendations: user-centered design. In Proceedings of the ACM Conference on Recommender Systems (RecSys’07), Minneapolis, MN, USA, pp. 153–56.Google Scholar
UPN 2008. UPnP Device Architecture Version 1.0. www.upnp.org. (Accessed 16 Sep 2012).Google Scholar
Van Meteren, R., and Van Someren, M. 2000. Using content-based filtering for recommendation. Proceedings of the ECML/MLNet Workshop on Machine Learning and the New Information Age, Barcelona, Spain.Google Scholar
Van Setten, M., Pokraev, S., and Koolwaaij, J. 2004. Context-aware recommendations in the mobile tourist application COMPASS. In Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 515–48. Lecture Notes in Computer Science, Vol. 3137. Berlin, Germany: Springer.Google Scholar
Wasserman, S., and Faust, K. 1994. Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences Series, no. 8. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Wiebe, J. M., Bruce, R. F., and O'Hara, T. P. 1999. Development and use of a gold-standard data set for subjectivity classifications. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL99), College Park, MD, USA, pp. 246–53.Google Scholar
Xie, Z. 2006. Machine Learning in Automatic Text Summarization: From Extracting to Abstracting. PhD thesis, University of Illinois, Chicago, IL, USA.Google Scholar
Xie, Z., Di Eugenio, B., and Nelson, P. C. 2008. From extracting to abstracting: generating quasi-abstractive summaries. In Proceedings of the Sixth International Language Resources and Evaluation (LREC’08), Marrakech, Morocco.Google Scholar
Zhuang, L., Jing, F., Zhu, X., and Zhang, L. 2006. Movie review mining and summarization. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM 2006), Arlington, VA, USA, pp. 4350.CrossRefGoogle Scholar