Proceedings
JRP Publications
www.i-jrp.com
journalrp.editor@gmail.com
9353189468
JRP
Under the delegate of “Journal of Research Proceedings,” we anchor a
bimonthly electronic journal enclosing the diverse realms of the
educational research field. JRP is providing a platform for the
researchers, academicians, professionals, practitioners, and students to
impart and share knowledge in the form of high quality empirical and
theoretical research papers, case studies, literature reviews, and book
reviews.
User Reposed Based Movie Recommendation
System
Prashant Verma1, Saajan Shrestha2, Prakash3,Gaurab Sah4, Dr. Shrishail Math5 Students, CSE Department, Sri Krishna Institute of Technology, Bangalore,
India1-4
Professor, CSE Department, Sri Krishna Institute of Technology, Bangalore, India 5
ABSTRACT:
Filtering devices are often used to extract unwanted data from vast amounts of data. Recommender systems look for and predict useful and insightful things that a user might enter into the data. Filtering technologies are being used to exclude irrelevant data in vast amounts of information. The approach focuses on repurposing the knowledge and preferences of users in order to calculate future suggestions. This article presents a recommender system that generates suggestions depending on user data. It is achieved by examining the person's psychological analysis, watching history, and film reviews from many other websites. It's also predicated on requirements of aggregate resemblance. Both material and data aggregation are used in this approach. Both of these things may be described as follows: Collaboration filtration is the process of constructing structures going by past user activities. The system is then utilized to anticipate results that perhaps the customer would be interesting in (i.e. items that have already been selected or scored).People rely on their own events to make judgments that are in their economic interest, thus the suggestion system is commonplace. Recommendation algorithms are a sort of data filtration which anticipates customer interests for items they utilize or are considering purchasing. The real effectiveness of such systems is still being researched.
Keywords - Model-based, Memory-based, Content-based, Hybrid, Recommendation,Collaborative filtering
I.
INTRODUCTION
People rely on information to decide their interests, thus the classification method is ingrained in their daily lives. The information retrieval approach uses information from a user's prior actions (for example, previously purchased products or numeric ratings assigned to them) as well as comparable judgments made by many other users. Following that, several algorithms are employed to predict goods [1-3]. Despite the fact that there have been several techniques established in the past. Search, on the other hand, continues to exist since it is widely utilized in many systems that customize recommendations and cope with inaccuracy [4]. These requirements provide considerable difficulties. Several academics have utilized methods such as Alternate Least - square, Singular Value Decomposition, K-Nearest Neighbor algorithm, and Normal predictor method to address this problem. Recollection and prototype pattern matching approaches are the two types [5-8]. Consciousness techniques work solely on a user-item rating matrix and may simply be modified to
utilise all of the scores until the filter is applied. [9]. A design approach, such as a neural network, from the other side, produces a system which adapts from user reviews and suggests new items. The follows is a thorough overview of all of the aforementioned techniques. [10].
I. MODULES A. SEARCH
B. DATA PREPROCESSING:
We were provided a series of vectors (u; m; r; t) inside the training examples, wherein u is just the user ID, m is indeed the movie ID, r is the ratings u provided to m, and t has been the time. We generate predictions for just a list of user-movie pairings after training. The root mean squared error is used to calculate error. We produce the movie ids with both the relevant users and their evaluations with independent files following which was before [11-14].
C. DATA CLUSTERING:
We group people into groups based mostly on films they saw, and then groups of movies into groups depending on the group who watched them. The participants may then be re-clustered depending on how many movies they viewed in each film cluster. Films can also be re-clustered depending on the amount of individuals that viewed it for each individual cluster. Individuals are selected depending on media and films are grouped dependent on user in the first phase [15-18]. Persons are grouped based on film groups on the second and third passes, while movies are grouped based on person groups. A cluster is a collection of services that are comparable. Like such a club, there are certain users who have similar interests.
D. RECOMMENDATION:
The Euclidean distance d among 2 similar users locations is computed using this similarity measure. Because bigger numbers indicate more remote, and hence less similar, people, this number alone isn't a reliable similarity measure. If individuals are much more alike, the value must be lower. As a result, 1 / (1+d) is returned by the implement. The advantage of this strategy is that even at ru, recommendations are made quickly. One may argue that since the suggestions are calculated for a community rather than a person, they are much less personal [16-19]. This method will be much more successful in generating ideas for future users with little charges.
E. MAHOUT
Its goal is to create free, transmitted, and extensible architectures of advanced machine learning techniques shown in the fields of grouping, categorization, cooperative filtration, and repeated sequence matching. It also provides a full-stack option for integrating machine learning on big data controlled by the Hadoop framework. It was a search engine that makes recommendations [20].
II. PROPOSEDSYSTEM
A comparison research of ALS on various methods was suggested. But, as contrasted to the 60-40 (Training - Testing) sample, employing a much more comprehensive training dataset of 80-20 (Training - Testing) gives a model with a lower RMSE [21-23]. The greater the regularisation value, the greater the RMSE, and conversely. The findings demonstrate that the ALS algorithm outperforms the SVD, KNN, and Normal Predictor algorithms in the prediction model. The technique they suggested has been utilised to provide effective movie suggestions [24-27]. The standard Movie Lens
100K set of data was used for the empirical study, and the results showed that the state-of-the-art recommendation algorithms were outperformed. [28-30]. In addition, the suggested technology's effectiveness should be evaluated in a variety of other recommendation systems scenarios [31-36].
.
Fig.1. Proposed System
IV. METHODOLOGY
Fig.2. Methodology
Fig. 2 depicts the suggestion system's research methods, which current operations such as filling up the set of data, pre-processing the information, trying to split the data, trying to fill the damage utilising gradient descent, going to add bias conditions for customers and films, training the neural network model, testing the prototype, and eventually observing the results [37-41].
V. IMPLEMENTATION
Collaborative Filtering: This method is based on the premise that individuals who loved something
in the past would like it again in the upcoming. This method creates a model based on player behaviour in the past. Recently viewed videos, bought goods, and item ratings are all examples of user activity. The model discovers a link between both the individuals and the objects in this way. The algorithm can then be used to forecast whichever product the user would be fascinated in or to provide a rating to that item. In recommender systems, wavelet packet deconstruction is employed as a cooperative filtering technique.
Fig.3. Collaborative Filtering
Singular Value Decomposition (SVD): SVD is indeed a linear algebraic approach that is gaining
popularity in the fields of machine learning and data science. Its prominence derives from its usage in the development of recommendation systems. There are several online user-centric apps, like video players, music players, e-commerce programs, and so on, that provide users additional additional stuff to interact in.
Identifying and proposing a large number of relevant goods that consumers would enjoy and select is always a difficulty. This work is accomplished using a variety of approaches, one of which is SVD. This article gives a quick overview of recommender systems, as well as a primer on wavelet decomposition and how it's used in movie suggestion.
Fig.4. Single Value Decomposition
VI. RESULTS
The final result of the recommendation system is generated on the basis of implementation are as follows:
I. Recommendation of movies for the user based on Collaborative Filtering
II. Recommendation of movies for the user based on Single Value Decomposition
VII. CONCLUSION
This article offers a synopsis of a collaborative filtering-based movie recommendation system. In research, several methodologies such as user-based filtration, piece of information sorting, alternate least square methods, KNN method, and Root Mean Square Method (RMSE), Mean Square method (MSE) were employed to assess the effectiveness of these systems. Each study has its own set of advantages and disadvantages. To provide movie recommendations, our algorithm takes into account user ratings. More features, such as the film's genre, producers, performers, and so on, may be explored in the next to give ideas.
REFERENCES
[1] Ngo, T.D., Bui, T.T., Pham, T.M., Thai, H.T., Nguyen, G.L. and Nguyen, T.N., 2021. Image deconvolution for optical small satellite with deep learning and real-time GPU acceleration. Journal of Real-Time Image Processing, pp.1-14.
[2] Nguyen, Ngoc‐Tu, Ming C. Leu, and Xiaoqing Frank Liu. "RTEthernet: Real‐time communication for manufacturing cyberphysical systems." Transactions on Emerging Telecommunications Technologies 29, no. 7 (2018): e3433.
[3]. Kharita, M. K., Kumar, A., & Singh, P. (2018). Item-Based Collaborative Filtering in Movie Recommendation in Real-time. 2018 First International Conference on Secure Cyber Computingand Communication (ICSCCC). I:10.1109/icsccc.2018.8703362
[4] Rajendrakumar, Shiny, and V. K. Parvati. "Automation of irrigation system through embedded computing technology." In Proceedings of the 3rd International Conference on Cryptography, Security and Privacy, pp. 289-293. 2019.
[5] Babu, R.G., Maheswari, K.U., Zarro, C., Parameshachari, B.D. and Ullo, S.L., 2020. Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images. Remote Sensing, 12(24), p.4135.
[6] Nguyen, Tu N., Bing-Hong Liu, and Shih-Yuan Wang. "On new approaches of maximum weighted target coverage and sensor connectivity: Hardness and approximation." IEEE Transactions on Network Science and Engineering 7, no. 3 (2019): 1736-1751.
[7] Manjanaik, N., B. D. Parameshachari, S. N. Hanumanthappa, and Reshma Banu. "Intra Frame Coding In Advanced Video Coding Standard (H. 264) to Obtain Consistent PSNR and Reduce Bit Rate for Diagonal Down Left Mode Using Gaussian Pulse." In IOP Conference Series: Materials Science and Engineering, vol. 225, no. 1, p. 012209. IOP Publishing, 2017.
[8] Hemalatha, K. L., SUNILKUMAR MANVI, and HN SURESH. "ADAPTIVE WEIGHTED- COVARIANCE REGULARIZED KERNEL FUZZY C MEANS ALGORITHM FOR MEDICAL
IMAGE SEGMENTATION." Journal of Theoretical & Applied Information Technology 95, no. 14 (2017).
[9] Bhuvaneswary, N., S. Prabu, K. Tamilselvan, and K. G. Parthiban. "Efficient Implementation of Multiply Accumulate Operation Unit Using an Interlaced Partition Multiplier." Journal of Computational and Theoretical Nanoscience 18, no. 4 (2021): 1321-1326.
[10] Hemalatha, K. L., S. M. Ashitha, and S. R. Meghana. "Design and implementation of modified FCM in the detection of brain tumor." Int. J. Adv. Sci. Res. Eng 3, no. 4 (2017): 2850-2858.
[11]. Cui, Bei-Bei. (2017). Design and Implementation of Movie Recommendation System Based on Knn Collaborative Filtering Algorithm. ITM Web of Conferences.
[12] Dwivedi, R., Dey, S., Chakraborty, C. and Tiwari, S., 2021. Grape disease detection network based on multi-task learning and attention features. IEEE Sensors Journal.
[13] Babu, N.V., 2014. Design and performance analysis of clustering mechanisms for wireless sensor networks.
[14]. FadhelAljunid, Mohammed & D H, Manjaiah. (2018). Movie Recommender System Based on Collaborative FilteringUsing Apache Spark. https://doi.org/10.1007/978-981-13-1274-8_22
[15] Vu, D.L., Nguyen, T.K., Nguyen, T.V., Nguyen, T.N., Massacci, F. and Phung, P.H., 2020. HIT4Mal: Hybrid image transformation for malware classification. Transactions on Emerging Telecommunications Technologies, 31(11), p.e3789.
[16] Lingappa, H., Suresh, H. and Manvi, S., 2018. Medical image segmentation based on extreme learning machine algorithm in kernel fuzzy c-means using artificial bee colony method. Int. J. Intell. Eng. Syst, 11, pp.128-136
[17]. Miryala, Goutham & Gomes, Rahul & Dayananda, Karanam. (2017). COMPARATIVE ANALYSIS OF MOVIE RECOMMENDATION SYSTEM USING COLLABORATIVE FILTERING IN SPARK ENGINE.
Journal of Global Research in Computer Science. 8. 10- 14.
[18]. Banerjee, Anurag &Basu, Tanmay. (2018). Yet Another Weighting Scheme for Collaborative Filtering Towards Effective Movie Recommendation.
[19] Prabu, S., Balamurugan, V. and Vengatesan, K., 2019. Design of cognitive image filters for suppression of noise level in medical images. Measurement, 141, pp.296-301.
[20] Bhuvaneswary, N., S. Prabu, S. Karthikeyan, R. Kathirvel, and T. Saraswathi. "Low Power Reversible Parallel and Serial Binary Adder/Subtractor." Further Advances in Internet of Things in Biomedical and Cyber Physical Systems (2021): 151.
[21] Shivappriya, S. N., S. Karthikeyan, S. Prabu, R. Pérez de Prado, and B. D. Parameshachari. "A modified ABC-SQP-based combined approach for the optimization of a parallel hybrid electric vehicle." Energies 13, no. 17 (2020): 4529.
[22] Muhammad, L.J., Algehyne, E.A., Usman, S.S., Ahmad, A., Chakraborty, C. and Mohammed, I.A., 2021. Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset. SN computer science, 2(1), pp.1-13.
[23] Nayak, Jithendra PR, K. Anitha, B. D. Parameshachari, Reshma Banu, and P. Rashmi. "PCB Fault detection using Image processing." In IOP Conference Series: Materials Science and Engineering, vol. 225, no. 1, p. 012244. IOP Publishing, 2017.
[24] Le, Ngoc Tuyen, Jing-Wein Wang, Duc Huy Le, Chih-Chiang Wang, and Tu N. Nguyen.
"Fingerprint enhancement based on tensor of wavelet subbands for classification." IEEE Access 8 (2020): 6602-6615.
[25] Rajendran, Ganesh B., Uma M. Kumarasamy, Chiara Zarro, Parameshachari B. Divakarachari, and Silvia L. Ullo. "Land-use and land-cover classification using a human group-based particle swarm optimization algorithm with an LSTM Classifier on hybrid pre-processing remote-sensing images." Remote Sensing 12, no. 24 (2020): 4135.
[26] Nguyen, Tu N., Bing-Hong Liu, Nam P. Nguyen, and Jung-Te Chou. "Cyber security of smart grid: attacks and defenses." In ICC 2020-2020 IEEE International Conference on Communications (ICC), pp. 1-6. IEEE, 2020.
[27]. Zhao, Zhi-Dan & Shang, Ming Sheng. (2010). User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop. 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010.
[28] Parameshachari, B. D., H. T. Panduranga, and S. K. Naveenkumar. "Partial encryption of medical images by dual DNA addition using DNA encoding." In 2017 international conference on recent innovations in signal processing and embedded systems (RISE), pp. 310-314. IEEE, 2017.
[29] Nguyen, Ngoc-Tu, Bing-Hong Liu, Shao-I. Chu, and Hao-Zhe Weng. "Challenges, designs, and performances of a distributed algorithm for minimum-latency of data-aggregation in multi-channel WSNs." IEEE Transactions on Network and Service Management 16, no. 1 (2018): 192-205.
[30] Parameshachari, B. D., Rashmi P. Kiran, P. Rashmi, M. C. Supriya, Rajashekarappa, and H. T. Panduranga. "Controlled partial image encryption based on LSIC and chaotic map." In ICCSP, pp. 60-63. 2019.
[31] Nguyen, Ngoc-Tu, and Bing-Hong Liu. "The mobile sensor deployment problem and the target coverage problem in mobile wireless sensor networks are NP-hard." IEEE Systems Journal 13, no. 2 (2018): 1312-1315.
[32] Parameshachari, B.D., Panduranga, H.T. and liberata Ullo, S., 2020, September. Analysis and Computation of Encryption Technique to Enhance Security of Medical Images. In IOP Conference Series: Materials Science and Engineering (Vol. 925, No. 1, p. 012028). IOP Publishing.
[33] L. Tan, K. Yu, A. K. Bashir, X. Cheng, F. Ming, L. Zhao, X. Zhou, “Towards Real-time and Efficient Cardiovascular Monitoring for COVID-19 Patients by 5G-Enabled Wearable Medical Devices: A Deep Learning Approach”, Neural Computing and Applications, 2021
[34] L. Tan, K. Yu, F. Ming, X. Cheng, G. Srivastava, “Secure and Resilient Artificial Intelligence of Things: a HoneyNet Approach for Threat Detection and Situational Awareness”, IEEE Consumer Electronics Magazine, 2021, doi: 10.1109/MCE.2021.3081874.
[35] L. Tan, N. Shi, K. Yu, M. Aloqaily, Y. Jararweh, “A Blockchain-Empowered Access Control Framework for Smart Devices in Green Internet of Things”, ACM Transactions on Internet Technology, vol. 21, no. 3, pp. 1-20, 2021
[36] K. Yu, L. Tan, M. Aloqaily, H. Yang, and Y. Jararweh, “Blockchain-Enhanced Data Sharing with Traceable and Direct Revocation in IIoT”, IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2021.3049141.
[37] K. Yu, L. Lin, M. Alazab, L. Tan, B. Gu, “Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System”, IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2020.3042504.
[38] K. Yu, M. Arifuzzaman, Z. Wen, D. Zhang and T. Sato, “A Key Management Scheme for Secure Communications of Information Centric Advanced Metering Infrastructure in Smart Grid,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 8, pp. 2072-2085, August 2015.
[39] Y. Gong, L. Zhang, R. Liu, K. Yu and G. Srivastava, "Nonlinear MIMO for Industrial Internet of Things in Cyber–Physical Systems," IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5533-5541, Aug. 2021, doi: 10.1109/TII.2020.3024631.
[40] C. Feng, K. Yu, M. Aloqaily, M. Alazab, Z. Lv and S. Mumtaz, "Attribute-Based Encryption with Parallel Outsourced Decryption for Edge Intelligent IoV," IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 13784-13795, Nov. 2020, doi: 10.1109/TVT.2020.3027568.
[41] W. Zeng, Z. Guo, Y. Shen, A. K. Bashir, K. Yu, Y. D. Al-Otaibi, and X. Gao, “Data-Driven Management for Fuzzy Sewage Treatment Processes Using Hybrid Neural Computing”, Neural Computing and Applications, https://doi.org/10.1007/s00521-020-05655-3.
