Abstract
With the development of e-commerce, online shopping becomes increasingly popular. Very often, online shopping customers read reviews written by other customers to compare similar items. However, the number of customer reviews is typically too large to look through in a reasonable amount of time. To extract information that can be used for online shopping decision support, this paper investigates a novel data mining problem of mining distinguishing customer focus sets from customer reviews. We demonstrate that this problem has many applications, and at the same time, is challenging. We present dFocus-Miner, a mining method with various techniques that makes the mined results interpretable and user-friendly. Moreover, we propose a visualization design to display the results of dFocus-Miner. Our experimental results on real world data sets verify the effectiveness and efficiency of our method.
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Bay SD, Pazzani MJ (2001) Detecting group differences: mining contrast sets. Data Min Knowl Discov 5(3):213–246
Dong G, Bailey J (eds) (2012) Contrast data mining: concepts, algorithms, and applications. CRC Press, Boca Raton
Dong G, Li J (1999) Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the 5th ACM international conference on knowledge discovery and data mining, KDD, pp 43–52
El-Kishky A, Song Y, Wang C, Voss CR, Han J (2014) Scalable topical phrase mining from text corpora. PVLDB 8(3):305–316
Ghose A, Ipeirotis PG (2007) Designing novel review ranking systems: predicting the usefulness and impact of reviews. In: Proceedings of the 9th international conference on electronic commerce: the wireless world of electronic commerce, pp 303–310
Hu M, Liu B (2004) Mining opinion features in customer reviews. In: Proceedings of the 19th AAAI conference on artificial intelligence, 16th conference on innovative applications of artificial intelligence, AAAI, pp 755–760
Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE international conference on data mining, ICDM, pp 263–272
Ji X, Bailey J, Dong G (2007) Mining minimal distinguishing subsequence patterns with gap constraints. Knowl Inf Syst 11(3):259–286
Karamshuk D, Noulas A, Scellato S, Nicosia V, Mascolo C (2013) Geo-spotting: mining online location-based services for optimal retail store placement. In: Proceedings of the 19th ACM international conference on knowledge discovery and data mining, KDD, pp 793–801
Koren Y, Bell RM, Volinsky C (2009) Matrix factorization techniques for recommender systems. IEEE Comput 42(8):30–37
Li J, Liu G, Wong L (2007) Mining statistically important equivalence classes and delta-discriminative emerging patterns. In: Proceedings of the 13th ACM international conference on knowledge discovery and data mining, KDD, pp 430–439
Li X, Xu G, Chen E, Li L (2015) Learning user preferences across multiple aspects for merchant recommendation. In: Proceedings of the 15th IEEE international conference on data mining, ICDM, pp 865–870
Li X, Xu G, Chen E, Li L (2015) MARS: a multi-aspect recommender system for point-of-interest. In: Proceedings of the 31st IEEE international conference on data engineering, ICDE, pp 1436–1439
Liu J, Shang J, Wang C, Ren X, Han J (2015) Mining quality phrases from massive text corpora. In: Proceedings of the 36th ACM international conference on management of data, SIGMOD, pp 1729–1744
McAuley JJ, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on recommender systems, pp 165–172
Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11):39–41
Mukherjee S, Basu G, Joshi S (2013) Incorporating author preference in sentiment rating prediction of reviews. In: Proceedings of the 22nd international world wide web conference, WWW, pp 47–48
Wang D, Zhu S, Li T (2013) SumView: a web-based engine for summarizing product reviews and customer opinions. Expert Syst Appl 40(1):27–33
Wang H, Lu Y, Zhai C (2010) Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM international conference on knowledge discovery and data mining, KDD, pp 783–792
Wang L, Zhao H, Dong G, Li J (2005) On the complexity of finding emerging patterns. Theor Comput Sci 335(1):15–27
Yang H, Duan L, Dong G, Nummenmaa J, Tang C, Li X (2015) Mining itemset-based distinguishing sequential patterns with gap constraint. In: Proceedings of the 20th international conference on database systems for advanced applications, DASFAA, pp 39–54
Zhang F, Zheng K, Yuan NJ, Xie X, Chen E, Zhou X (2015) A novelty-seeking based dining recommender system. In: Proceedings of the 24th international conference on world wide web, WWW, pp 1362–1372
Zhang W, Wang J, Feng W (2013) Combining latent factor model with location features for event-based group recommendation. In: Proceedings of the 19th ACM international conference on knowledge discovery and data mining, KDD, pp 910–918
Zhao Q, Wang H, Lv P, Zhang C (2014) A bootstrapping based refinement framework for mining opinion words and targets. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management, CIKM, pp 1995–1998
Zheng VW, Cao B, Zheng Y, Xie X, Yang Q (2010) Collaborative filtering meets mobile recommendation: a user-centered approach. In: Proceedings of the 24th AAAI conference on artificial intelligence, AAAI
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This work was supported in part by the National Natural Science Foundation of China (61572332), the Fundamental Research Funds for the Central Universities (2016SCU04A22), the China Postdoctoral Science Foundation (2014M552371, 2016T90850), and the Academy of Finland (295694).
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Duan, L., Liu, L., Dong, G. et al. Mining distinguishing customer focus sets from online customer reviews. Computing 100, 335–351 (2018). https://doi.org/10.1007/s00607-018-0601-1
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DOI: https://doi.org/10.1007/s00607-018-0601-1