Abstract
Websites that compare product reviews have emerged as an essential informational resource for customers trying to make informed judgments about their purchases. Through these platforms, customers may browse user reviews and ratings, search and compare products, and base their decisions on the expertise and experiences of other shoppers. Due to the significance of user-generated material in the purchasing process, there is an increasing need for sophisticated and user-friendly product review comparison websites. With technologies like artificial intelligence, social integration, and better verification procedures, a proposed system aims to improve the user experience in order to meet this demand. As a result, users will experience a platform that is more personalized, entertaining, and outfitted with cutting-edge technologies to deliver reliable information to customers. Product review comparison websites have the ability to play an even more significant part in the decision-making process by staying on the cutting edge of technical developments and consistently enhancing the user experience. This study presents an overview of websites that compare product reviews, the suggested system, and their influence on the choice to buy.
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
Wanaskar UH, Vij SR, Mukhopadhyay D (2013) A hybrid web-recommendation system based on the improved association rule mining algorithm. J Softw Eng Appl 6:396–404
Campos PG, BellogÃn A, DÃez F, Chavarriaga JE (2010) Simple time-biased KNN-based recommendations. ACM. 978-1-4503-0258-6
Tewari AS, Kumar A, Barman AG (2014) Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining. IEEE, pp 500–503. 9781-4799-2572-8
George T, Merugu S (2005) A scalable collaborative filtering framework based on co-clustering. In: Proc. 5th IEEE Int. Conf. Data Mining, pp 625–628
Sarwar BM, Karypis G, Konstan JA, Reidl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proc. 10th Int. World Wide Web Conf., pp 285–295
Gensler L (2018) The world’s largest retailers 2017: Amazon and Alibaba are closing in on Walmart. https://www.forbes.com/sites/laurengensler/2017/05/24/the-worldslargest-retailers-2017-walmart-cvs-amazon/#5f3bc35020b5. Accessed 30 April 2018
Passi R (2017) Recommending items using collectively trained models. In: International conference on information technology (ICIT)
Shoja BM, Tabrizi N (2019) Customer reviews analysis with deep neural networks for e-commerce recommender systems. IEEE Access 7
Laishram A, Sahu SP, Padmanabhan V, Udgata SK (2016) Collaborative filtering, matrix factorization and population based search. The nexus unveiled. ICONIP, Part III, LNCS 9949, pp 352–361
Kaur J, Bedi RK, Gupta SK. Product recommendation systems a comprehensive review
Gavhane S, Patil J, Kadwe H, Thakhre P. Product recommendation using machine learning algorithm—a better approach
Dwivedi R, Anand A, Johri P, Banerji A, Gaur NK. Product based recommendation system on Amazon data
Dabhade MG, Chopde NR. A result review analysis of product recommendation system in domain sensitive manner
He W, Zhang J, Akula V. Comparing consumer produced product reviews across multiple website with sentiment classification
Purkaystha B, Datta T, Islam S, Jannat M-E (2017) Product recommendation: a deep learning factorization method using separate learners. In: 20th international conference of computer and information technology (ICCIT)
Esslimani I, Brun A, Boyer A (2009) A collaborative filtering approach combining clustering and navigational based correlations. In: Proc. 5th Int. Conf. Web Inf. Syst. Technol., pp 364–369
Janjarassuk U, Puengrusme S (2019) Product recommendation based on genetic algorithm. In: 5th international conference on engineering, applied sciences and technology (ICEAST)
Hofmann T, Puzicha J (1999) Latent class models for collaborative filtering. In: Proc. 6th Int. Joint Conf. Artif. Intell., pp 688–693
Wan Y, Menon S, Ramaprasad A (2003) A classification of product comparison agents, pp 498–504
The rise of price comparison sites in South East Asia (2013)
Shrivastava R, Sisodia DS (2019) Product recommendations using textual similarity based learning models. In: International conference on computer communication and informatics (ICCCI)
Ahamed MT, Afroge S (2019) A recommender system based on deep neural network and matrix factorization for collaborative filtering. In: International conference on electrical, computer and communication engineering (ECCE)
Shopping price comparison scripts
Serenko A, Hayes J (2009) Investigating the functionality and performance of online shopping bots for electronic commerce: a follow-up study. Int J Electron Bus
Almaghrabi M, Chetty G (2018) A deep learning based collaborative neural network framework for recommender system. In: International conference on machine learning and data engineering (iCMLDE)
Zhang S, Wang W, Ford J, Makedon F (2006) Learning from incomplete ratings using non-negative matrix factorization. In: Proc. 6th SIAM Int. Conf. Data Mining, pp 549–553
Du J, Li L, Gu P, Xie Q (2019) A group recommendation approach based on neural network collaborative filtering. In: IEEE 35th international conference on data engineering workshops (ICDEW)
Herlocker J, Konstan J, Terveen L, Riedl J (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–10
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors have no conflicts of interest to declare that are relevant to the content of this article. No funding was received for conducting this study.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bhave, D., Rohit, P., Mrugesh, R., Sumit, S. (2023). Product Review and Recommendation. In: Balas, V.E., Semwal, V.B., Khandare, A. (eds) Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_24
Download citation
DOI: https://doi.org/10.1007/978-981-99-3177-4_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3176-7
Online ISBN: 978-981-99-3177-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)