Abstract
To help in decision making, buyers in online shopping tend to go through each product features, functionalities, etc. provided by vendors and reviews made by other users, which is not an effective way when confronting loaded of information and especially if the buyers are beginner users who have limited experience and knowledge. To deal with these problems, we propose a framework of purpose-oriented recommendation which present a ranking of products suitable for a designated user purpose by identifying important product features to fulfill the purpose from user reviews. As technical foundation for realizing the framework, we propose several methods to mine relation between user purposes and product features from the online reviews. The experimental results employing reviews of digital cameras in Amazon.com show the effectiveness and stability of proposed methods with acceptable rate of precision and recall.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, K., Narayanan, R., Choudhary, A.: Voice of the customers: Mining online customer reviews for product feature-based ranking. In: Proceeding of 3rd Workshop on Online Social Networks (WOSN), pp. 1–9 (2010)
Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proceeding of 19th National Conference on Artificial Intelligence, pp. 755–760 (2004)
Kangale, A., Kumar, S.K., Naeem, M.A., Williams, M., Tiwari, M.K.: Mining consumer reviews to generate ratings of different product attributes while producing feature-based review-summary. Int. J. Syst. Sci. 47, 3272–3286 (2016)
Kamal, A.: Review mining for feature based opinion summarization and visualization. arXiv Preprint arXiv:1504.03068 (2015)
Uchida, S., Yamamoto, T., Kato, M.P., Ohshima, H., Tanaka, K.: Object search by experience attributes. DBSJ Japanese J. 14-J(7), 1–7 (2016)
Ramage, D., Hall, D., Nallapati, R., Manning, C.D.: Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. In: Proceeding of EMNLP 2009, pp. 248–256 (2009)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceeding of Workshop at International Conference on Learning Representations (ICLR) (2013)
Loper, E., Bird, S.: NLTK: the natural language toolkit. In: Proceeding of ETMTNLP 2002, vol. 1, pp. 63–70 (2002)
Agichtein, E., Gravano, L.: Snowball: extracting relations from large plain-text collections. In: Proceeding of 5th ACM Conference on Digital libraries, pp. 85–94 (2000)
Thelen, M., Riloff, E.: A bootstrapping method for learning semantic lexicons using extraction pattern contexts. In: Proceeding of EMNLP 2002, pp. 214–221 (2002)
Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceeding of 14th Conference on Computational Linguistics, pp. 539–545 (1992)
McAuley, J., Pandey, R., Leskovec, J.: Inferring networks of substitutable and complementary products. In: Proceeding 21th KDD, pp. 785–794
Pantel, P., Ravichandran, D., Hovy, E.: Towards terascale knowledge acquisition. In: Proceeding of 20th International Conference on Computational Linguistics, pp. 771–777 (2004)
Chen, X., Chen, C.-H., Fai Leong, K., Jiang, X.: An ontology learning system for customer needs representation in product development. Int. J. Adv. Manuf. Technol. 67, 441–453 (2013)
Online Survey about Digital Cameras, https://www.surveymonkey.com/r/RMW9C7T
Wong, T.-L., Lam, W.: Learning to extract and summarize hot item features from multiple auction web sites. Knowl. Inf. Syst. 14, 143–160 (2008)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceeding of 10th KDD. pp. 168–177 (2004)
Pantel, P., Pennacchiotti, M.: Espresso: leveraging generic patterns for automatically harvesting semantic relations patrick. In: Proceeding of Joint Conference of 21st COLING and 44th ACL, pp. 113–120 (2006)
Acknowledgment
This work was supported by JSPS KAKENHI Grant Number 15K00423 and the Kayamori Foundation of Informational Science Advancement.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yong, S., Asano, Y. (2017). Mining Relationship Between User Purposes and Product Features Towards Purpose-Oriented Recommendation. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-61845-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61844-9
Online ISBN: 978-3-319-61845-6
eBook Packages: Computer ScienceComputer Science (R0)