Abstract
Many industries use recommendation systems (RS) to identify product recommendations when users actively participate on e-commerce sites. Recently, massive growth in both goods and consumers has faced serious challenges. Numerous websites present the consumer with numerous options at once, creating a lot of confusion. In addition, finding the right product or active user is an essential part of RS. Products are already recommended based on consumer preferences and sociodemographic trends. A hybrid action-related recommendation based on K-Nearest Neighbor Similarity (HAR-KNN) combines the ease of hybrid filtering with the development of feature vectors to improve the user behavior matrix. To categorize attributes, it uses both quality and quantity classifiers. Additionally, the proposed methodology overcomes shortcomings in earlier approaches to evaluating user preference for goods and feature analysis. The SOM AND KNN classification technique has been approved for the purpose of locating information about user behavior online and in real time for a specific user group containing a large amount of data in relation to the commonalities among many users and target users. A test result is evaluated by using highly predictive metrics such as precision (P), recall (R), F, as well as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE).
Supported by organization x.
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
Nithya BN, Geetha DE, Kumar M (2023) Optimization-assisted personalized event recommendation for event-based social networks. Adv Eng Softw 176:103368
Shrivastava R, Sisodia DS, Nagwani NK (2023) Deep neural network-based multi-stakeholder recommendation system exploiting multi-criteria ratings for preference learning. Expert Syst Appl 213:119071
Sheng X et al (2023) Adaptive curriculum sequencing and education management system via group-theoretic particle swarm optimization. Systems 11(1):34
Vassakis K et al (2023) Big data analytics on location-based social networks for knowledge generation in tourism-the case of Crete Island. Int J Technol Mark 17(1):99–123
Vedavathi N, Kumar KMA (2023) E-learning course recommendation based on sentiment analysis using hybrid Elman similarity. Knowl-Based Syst 259:110086
Anwar T, Uma V, Srivastava G (2023) CDRec-CAS: cross-domain recommendation using context-aware sequences. IEEE Trans Comput Soc Syst
Fkih F (2023) Enhancing item-based collaborative filtering by users’ similarities injection and low-quality data handling. Data Knowl Eng 144:102126
Chen X et al (2023) Exploring science-technology linkages: a deep learning-empowered solution. Inf Process Manage 60(2):103255
Vedavathi N, Kumar KMA (2023) PLRec: an efficient approach towards e-learning recommendation using LSTM-CNN technique. In: Proceedings of the international conference on cognitive and intelligent computing: ICCIC 2021, vol 2. Singapore: Springer Nature Singapore
Hyun S et al (2023) Timed pattern-based analysis of collaboration failures in system-of-systems. J Syst Softw, 111613
Nilashi M et al (2023) The impact of multi-criteria ratings in social networking sites on the performance of online recommendation agents. Telematics Inf 76:101919
Zhang Q (2023) Exploring the effects of overload and stress factors on WeChat moments users’ discontinuous usage intention: an SSO perspective. In: SHS Web of conferences vol 155. EDP Sciences
Moon MJ, Lee S (2023) The future of informational tools with big data informatics: opportunities and challenges for evidence-based policymaking. Routledge Handb Policy Tools, 559–569
Huang N et al (2023) Cross-platform sequential recommendation with sharing item-level relevance data. Inf Sci 621:265–286
Pilato G, Vella F (2023) A survey on quantum computing for recommendation systems. Information 14(1):20
Duan JJ et al (2023) A modeling design method for complex products based on LSTM neural network and Kansei engineering. Appl Sci 13(2):710
Kyaw KS et al (2023) Business intelligent framework using sentiment analysis for smart digital marketing in the E-commerce era. Asia Soc Issues 16(3):e252965–e252965
Jiang T et al (2023) Guest editorial: Special issue on integrated local energy systems. Int J Electr Power Energy Syst 148:108929
Jayasudha J, Thilagu M (2023) A survey on sentimental analysis of student reviews using natural language processing (NLP) and text mining. In: Innovations in intelligent computing and communication: first international conference, ICIICC 2022, Bhubaneswar, Odisha, India, December 16-17, 2022, Proceedings. Cham: Springer International Publishing
Folino G, Godano CO, Pisani FS (2023) An ensemble-based framework for user behaviour anomaly detection and classification for cybersecurity. J Supercomput, 1–24
Bizimis M (2023) Hybrid recommendation systems using neural networks
Wang Y-J, Lee AJT (2023) Movie account recommendation on Instagram. ACM Trans Internet Technol
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, S., Shakya, H.K. (2023). Hybrid Real-Time Implicit Feedback SOM-Based Movie Recommendation Systems. In: Tanwar, S., Wierzchon, S.T., Singh, P.K., Ganzha, M., Epiphaniou, G. (eds) Proceedings of Fourth International Conference on Computing, Communications, and Cyber-Security. CCCS 2022. Lecture Notes in Networks and Systems, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-99-1479-1_28
Download citation
DOI: https://doi.org/10.1007/978-981-99-1479-1_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1478-4
Online ISBN: 978-981-99-1479-1
eBook Packages: EngineeringEngineering (R0)