Abstract
Using social media and e-commerce sites, users convey their preferences and interests via reviews, feedback, and comments. These comments and reviews consist of details about a given product or an item and also users’ thoughts. Different features of user-generated content include various features such as emotions, sentiments, review usefulness, and so forth that exhibit a promising exploration in the domain of recommendation frameworks. This paper harness reviews as the content generated from user to exploit topics based on topic modeling using latent Dirichlet allocation for generating topic distribution profile of users. Examination of topic distribution profile of users gives us a new prospect for recommendation of products which is based on hidden thematic framework of user preferences. Assessment on books and movie dataset confirms the adequacy of the suggested topic distribution profile for recommendation system framework.
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
References
Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 6:734–749
Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: The adaptive web. Springer, Heidelberg, pp 325–341
Schafer J, Ben et al (2007) Collaborative filtering recommender systems. In: The adaptive web. Springer, Heidelberg, pp 291–324
Chen L, Chen G, Wang F (2015) Recommender systems based on user reviews: the state of the art. User Model User-Adap Inter 25(2):99–154
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022
Boyd-Graber J, Blei D, Zhu X (2007) A topic model for word sense disambiguation. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL)
Haghighi A, Vanderwende L (2009) Exploring content models for multi-document summarization. In: Proceedings of human language technologies: the 2009 annual conference of the north american chapter of the association for computational linguistics. Association for Computational Linguistics
McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on recommender systems
Seroussi Y, Bohnert F, Zukerman I (2012) Authorship attribution with author-aware topic models. In: Proceedings of the 50th annual meeting of the association for computational linguistics: short papers-volume 2. Association for Computational Linguistics
Harper FM, Konstan JA (2015) The MovieLens datasets: history and context. ACM Trans Interact Intell Syst (TiiS) 5(4) Article 19, 19p. http://doi.org/10.1145/2827872
Ziegler C-N, McNee SM, Konstan JA, Lausen G (2005) Proceedings of the 14th international world wide web conference (WWW ’05), May 10–14, Chiba, Japan
Chakraverty S, Saraswat M (2017) Review based emotion profiles for cross domain recommendation. Multimed Tools Appl 76(24):25827–25850
McCallum AK (2002)MALLET: a machine learning for language toolkit. http://mallet.cs.umass.edu
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Saraswat, M., Chakraverty, S., Sharma, A. (2020). Review-Based Topic Distribution Profile for Recommender Systems. In: Jain, V., Chaudhary, G., Taplamacioglu, M., Agarwal, M. (eds) Advances in Data Sciences, Security and Applications. Lecture Notes in Electrical Engineering, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-15-0372-6_35
Download citation
DOI: https://doi.org/10.1007/978-981-15-0372-6_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0371-9
Online ISBN: 978-981-15-0372-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)