Abstract
Capturing users’ preference that change over time is a great challenge in recommendation systems. What makes a product feature interesting now may become the accepted standard in the future. Social recommender systems that harness knowledge from user expertise and interactions to provide recommendation have great potential in capturing such trending information. In this paper, we model our recommender system using sentiment rich user generated product reviews and temporal information. Specifically we integrate these two resources to formalise a novel aspect-based sentiment ranking that captures temporal distribution of aspect sentiments and so the preferences of the users over time. We demonstrate the utility of our proposed model by conducting a comparative analysis on data extracted from Amazon.com and Cnet. We show that considering the temporal preferences of users leads to better recommendation and that user preferences change over time.
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Notes
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Sentences can be parsed using the Stanford Dependency parser [11].
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References
Chen, Y., Ferrer, X., Wiratunga, N., Plaza, E.: Sentiment and preference guided social recommendation. In: International Conference on Case-Based Reasoning. Accepted in ICCBR ’14 (2014)
Cho, Y., Cho, Y., Kim, S.: Mining changes in customer buying behavior for collaborative recommendations. Expert Syst. Appl. 28, 359–369 (2005)
Ding, X., Liu, B., Yu, P.: A holistic lexicon-based approach to opinion mining. In: Proceedings of International Conference on Web Search and Data Mining (2008)
Ding, Y., Li, X.: Time weight collaborative filtering. In: Proceedings of International Conference on Information and Knowledge Management, CIKM ’05, pp. 485–492 (2005)
Dong, R., Schaal, M., O’Mahony, M., McCarthy, K., Smyth, B.: Opinionated product recommendation. In: International Conference on Case-Based Reasoning (2013)
Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of Language Resources and Evaluation Conference, pp. 417–422 (2006)
Hong, W., Li, L., Li, T.: Product recommendation with temporal dynamics. Expert Syst. Appl. 39, 12398–12406 (2012)
Hu, M., Liu, B.: Mining and summarising customer reviews. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177 (2004)
Kolter, J., Maloof, M.: Dynamic weighted majority: A new ensemble method for tracking concept drift. In: International Conference on Data Mining, pp. 123–130 (2003)
Koren, Y.: Collaborative filtering with temporal dynamics. Commun. Assoc. Comput. Mach. 53, 89–97 (2010)
Marneffe, M., MacCartney, B., Manning, C.: Generating typed dependency parses from phrase structure parses. In: Proceedings of Language Resources and Evaluation Conference (2006)
McCarthy, K., Salem, Y., Smyth, B.: Experience-based critiquing: reusing experiences to improve conversational recommendation. In: International Conference on Case-Based Reasoning (2010)
Moghaddam, S., Ester, M.: Opinion digger: An unsupervised opinion miner from unstructured product reviews. In: Proceedings of International Conference on Information and Knowledge Management (2010)
Moghaddam, S., Ester, M.: On the design of lda models for aspect-based opinion mining. In: Proceedings of International Conference on Information and Knowledge Management (2012)
Muhammad, A., Wiratunga, N., Lothian, R., Glassey, R.: Contextual sentiment analysis in social media using high-coverage lexicon. In: Research and Development in Intelligent. Springer, Berlin (2013)
Popescu, A., Etzioni, O.: Extracting product features and opinions from reviews. In: Natural Language Processing and Text Mining, pp. 9–28. Springer, London (2007)
Turney, P.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of Annual Meeting for Computational Linguistics (2002)
Vasudevan, S., Chakraborti, S.: Mining user trails in critiquing based recommenders. In: Proceedings of International Conference on World Wide Web Companion, pp. 777–780 (2014)
Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: Temporal recommendation on graphs via long- and short-term preference fusion. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 723–732 (2010)
Yitzhaki, S.: Relative deprivation and the Gini coefficient. Q. J. Econ., 321–324 (1979)
Acknowledgments
This research has been partially supported by AGAUR Scholarship (2013FI-B 00034), ACIA mobility scholarship and Project Cognitio TIN2012-38450-C03-03.
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Ferrer, X., Chen, Y.Y., Wiratunga, N., Plaza, E. (2014). Preference and Sentiment Guided Social Recommendations with Temporal Dynamics. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXI. SGAI 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-12069-0_7
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DOI: https://doi.org/10.1007/978-3-319-12069-0_7
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