Abstract
This paper presents a collaborative filtering method for hotel recommendation incorporating guest preferences. We used the results of aspect-based sentiment analysis to recommend hotels because whether or not the hotel can be recommended depends on the guest preferences related to the aspects of a hotel. For each aspect of a hotel, we identified the guest preference by using dependency triples extracted from the guest reviews. The triples represent the relationship between aspect and its preference. We calculated transitive association between hotels by using the positive/negative preference on some aspect. Finally, we scored hotels by Markov Random Walk model to explore transitive associations between the hotels. The empirical evaluation showed that aspect-based sentiment analysis improves overall performance. Moreover, we found that it is effective for finding hotels that have never been stayed at but share the same neighborhoods.
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Notes
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We used the clusters that the number of positive and negative words is not equal.
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References
Balabanovic, M., Shoham, Y.: Fab content-based collaborative recommendation. Commun. ACM 40, 66–72 (1997)
Park, S.T., Pennock, D.M., Madani, O., Good, N., DeCoste, D.: Naive filterbots for robust cold-start recommendations. In: Proceedings of the 12th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 699–705 (2006)
Yildirim, H., Krishnamoorthy, M.S.: A random walk method for alleviating the sparsity problem in collaborative filtering. In: Proceedings of the 3rd ACM Conference on Recommender Systems, pp. 131–138 (2008)
Liu, N.N., Yang, Q.: A ranking-oriented approach to collaborative filtering. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 83–90 (2008)
Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems. In: Proceedings of the 33rd ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 210–217 (2010)
Zhao, X., Zhang, W., Wang, J.: Interactive collaborative filtering. In: Proceedings of the 22nd ACM Conference on Information and Knowledge Management, pp. 1411–1420 (2013)
Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Automatic multimedia cross-model correlation discovery. In: Proceedings of the 10th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 653–658 (2001)
Cane, W.L., Stephen, C.C., Fu-lai, C.: Integrating collaborative filtering and sentiment analysis. In: Proceedings of the ECAI 2006 Workshop on Recommender Systems, pp. 62–66 (2006)
Niklas, J., Stefan, H.W., Mark, C.M., Iryna, G.: Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations. In: Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion, pp. 57–64 (2009)
Raghavan, S., Gunasekar, S., Ghosh, J.: Review quality aware collaborative filtering. In: Proceedings of the 6th ACM Conference on Recommender Systems, pp. 123–130 (2012)
Li, M., Dias, B., Jarman, I.: Grocery shopping recommendations based on basket-sensitive random walk. In: Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1215–1223 (2009)
Faridani, S.: Using canonical correlation analysis for generalized sentiment analysis product recommendation and search. In: Proceedings of 5th ACM Conference on Recommender Systems, pp. 23–27 (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. Mach. Learn. 3, 993–1022 (2003)
Yin, Z., Gupta, M., Weninger, T., Han, J.: A unified framework for link recommendation using random walks. In: Proceedings of the Advances in Social Networks Analysis and Mining, pp. 152–159 (2010)
Li, L., Zheng, L., Fan, Y., Li, T.: Modeling and broadening temporal user interest in personalized news recommendation. Expert Syst. Appl. 41(7), 3163–3177 (2014)
Wijaya, D.T., Bressan, S.: A random walk on the red carpet: rating movies with user reviews and pagerank. In: Proceedings of the ACM International Conference on Information and Knowledge Management CIKM 2008, pp. 951–960 (2008)
Kudo, T., Matsumoto, Y.: Fast method for Kernel-based text analysis. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pp. 24–31 (2003)
Kobayashi, N., Inui, K., Matsumoto, Y., Tateishi, K., Fukushima, S.: Collecting evaluative expressions for opinion extraction. J. Nat. Lang. Process. 12(3), 203–222 (2005)
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57 (1999)
Li, Y., Yang, M., Zhang, Z.: Scientific articles recommendation. In: Proceedings of the ACM International Conference on Information and Knowledge Management CIKM 2013, pp. 1147–1156 (2013)
Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to un-supervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 417–424 (2002)
Joachims, T.: SVM Light Support Vector Machine. Department of Computer Science. Cornell University (1998)
Xue, G.R., Yang, Q., Zeng, H.J., Yu, Y., Chen, Z.: Exploiting the hierarchical structure for link analysis. In: Proceedings of the 28th ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 186–193 (2005)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. 30(1–7), 107–117 (1998)
Yates, B., Neto, R.: Modern Information Retrieval. Addison Wesley, New York (1999)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, pp. 187–295 (2007)
Dai, W., Yang, Q., Xue, G., Yu, Y.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 193–200 (2007)
Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 448–456 (2011)
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Fukumoto, F., Sugiyama, H., Suzuki, Y., Matsuyoshi, S. (2016). Exploiting Guest Preferences with Aspect-Based Sentiment Analysis for Hotel Recommendation. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2015. Communications in Computer and Information Science, vol 631. Springer, Cham. https://doi.org/10.1007/978-3-319-52758-1_3
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