SYSYEM Enhancing Movie Recommenders by means of KNN-based Algorithms Kamil Rojek 0 Rafał Ochorok 0 Maciej Wiencis 0 Faculty of Applied Mathematics, Silesian University of Technology , Kaszubska 23, 44-100 Gliwice , Poland 2022 8 49 54

The project concerns a system recommending films using the KNN algorithm. The program in order to find movies, is based on the history of viewed items. Movie Recommender initially chooses the best ones from the history of films, in order to finally give the proposals best suited to the user's preferences. The model works with data from IMDB [? ] data set downloaded from datasets.imdbws.com.

 eol>Movies Prediction model Classification algorithms Personalized movie prediction Python 1. Introduction 2. Assumptions for algorithms

Currently, the most dynamically developing IT tools are Each of the algorithms should be prepared to meet the methods of artificial intelligence [ 1, 2 ]. Algorithms sup- following criteria: porting decision-making or supporting inference based 1. Prepared according to the mathematical descripon fuzzy sets [ 3, 4 ] find a number of applications, among tion of the algorithm; others, in the detection of anomalies on roads [ 5 ] or in the control of intelligent home management systems [ 6, 7 ]. 2. Optimized for the performance on our data set; At this point, one cannot fail to mention a wide class of 3. Returns an array containing information about heuristic algorithms based on the observation of animal top k (number of predicted movies) movies, basbehavior [ 8, 9, 10 ], which are widely used. The energy ing on top 3 movies from our watch-list. reduction applications [ 11, 12, 13, 14, 15 ] are very important. The most common applications relate to the 3. Program description use of [16] neural networks in a wide variety of applications that afect almost every area of life [ 17, 18, 19, 20 ]. The task of our project is to create a system recommendVery interesting applications concern the care of the el- ing films using the KNN algorithm. The program in order derly [ 21, 22, 23, 24 ]. Often, neural networks are used to find movies, is based on the history of viewed items. in various types of detection tasks for certain features Movie Recommender initially chooses the best ones from [ 25, 26, 27, 28 ]. The use of neural networks also plays a the history of films. Then each video goes through the very important role in machine learning [29, 30]. algorithm so that the program finally gives the proposals

Due to digitization of our modern world prediction best suited to the user’s preferences. (Including watch models are extremely crucial in these days. That’s be- history). In this particular cases, KNN uses three metrics: cause they can optimize some of the user’s processes, that Taxi cab, Cosine distances and Euclidean. would facilitate comfort of using a given app. Movies are extremely complex thanks to a lot of variables into which they can be divided. People struggle with choosing 4. KNN history a movie to watch, because they not only might not be aware of their preferences, but also they may not have enough time to check and compare all data[31]. Whole problem could be solved by a program doing all the necessary calculations for you, basing on user’s watch history and reviews. 49–54 (1) (2) (3)

 5. Euclidean metrics history

Euclidean distance is the distance in Euclidean space; both concepts are named after ancient Greek mathematician Euclid, whose Elements became a standard textbook in geometry for many centuries.Concepts of length and distance are widespread across cultures, can be dated to the earliest surviving "protoliterate" bureaucratic documents from Sumer in the fourth millennium BC (far before Euclid),and have been hypothesized to develop in children earlier than the related concepts of speed and time.But the notion of a distance, as a number defined from two points, does not actually appear in Euclid’s Elements. Instead, Euclid approaches this concept implicitly, through the congruence of line segments, through the comparison of lengths of line segments, and through the concept of proportionality.

 6. Taxi Cab metrics history

Taxicab Geometry is a non-Euclidean Geometry that measures distance on horizontal and vertical lines. According to Taxicab Geometry - History, the taxicab metric was ifrst introduced by Hermann Minkowski (1864-1909) over 100 years ago; however, it did not get its name until 1952. Taxicab is unique in that it is only one axiom away from being a Euclidean metric. In Euclidean Geometry the minimum distance between two points is the shortest line segment between those two points. However, in Taxicab Geometry there can be multiple minimal distances or ‘shortest paths’ made up of line segments perpendicular or parallel to the x-axis. Taxicab Geometry - History suggests that modern research on taxicab did not occur until as recent as the 1980s. The measurement of distance using vertical and horizontal lines rather than diagonal lines has sparked questions about its applications and encouraged more research and exploration of this simple yet unique metric

 7. Description of the program’s operation

Initially, we started the project by preparing the data in such a way that we could then carry out calculations on them. For this purpose, we downloaded the IMDB database, which consisted of four files containing data on: • Data on the movie itself. • Ratings for individual videos. • The cast of the movie.

• Personal data of people participating in the film.

In order to optimize the algorithm, we do not use the full In this section we will present pseudocodes of the most names of the cast at this stage of operation. important algorithms used by us.

The next step is to create a person’s profile to keep a history of the videos watched along with the user’s rating. Before starting the algorithm, the program selects three top movies according to the user’s rating. Then, based on this data, it performs calculations to find the best matching items in our database.

The metric used in the KNN algorithm is the sum of the cosine, taxicab and euclidean distances,between the values of the film elements we compare, i.e. genres, writers, directors. We use previously created numerical values.

Formulas used to determine distances between successive parameters looks like this: • Cosine distance • Taxi cab distance

| − | • Euclidean distance 1 −

· ‖‖2‖‖2 where u i v are the arrays to be compared The next step is to use the KNN algorithm, which will use the previously described metric, in order to find the k-nearest neighbors of a given movie. In the algorithm itself, we predict finding k neighbors. As the algorithm can receive a maximum of 3 user top videos, it will therefore return a top 3k of the proposed positions. For example, if we add movies to the history: • Cofee & Kareem, rating: 8 • Das Cabinet des Dr. Caligari, rating: 9 • The Kid, rating: 8.5

Program would output:

Recommended Movies basing on: Cofee & Kareem • The F word • It’s All Gone Pete Tong • Psycho • 6 donne per 1’assassino Recommended Movies basing on: Das Cabinet des Dr. Caligari Recommended Movies basing on: The Kid • The Circus • Modern Times

 8. Algorithms

Algorithm 1: Cosine distance metric pseudocode Algorithm 3: Euclidean metric pseudocode Data: Input: Id of the first movie 1, Id of the second movie 2 Result: The lack of data genresA = genre of 1 genresB = genre of 2 genreDistance = the cosine distance between two values. directorA = genre of 1 directorB = genre of 2 directorDistance = the cosine distance between two values. writerA = genre of 1 writerB = genre of 2 writerDistance = the cosine distance between two values. return genreDistance + directorDistance + writerDistance Data: Input: Id of the first movie 1, Id of the second movie 2 Result: The lack of data genresA = genre of 1 genresB = genre of 2 genreDistance = the taxi cab distance between two. directorA = genre of 1 directorB = genre of 2 directorDistance = the taxi cab distance between two values. writerA = genre of 1 writerB = genre of 2 writerDistance = the taxi cab distance between two values. return genreDistance + directorDistance + writerDistance Algorithm 2: Taxicab metric pseudocode Data: Input: Id of the first movie 1, Id of the second movie 2 Result: The lack of data genresA = genre of 1 genresB = genre of 2 genreDistance = the euclidean distance between two values. directorA = genre of 1 directorB = genre of 2 directorDistance = the euclidean distance between two values. writerA = genre of 1 writerB = genre of 2 writerDistance = the euclidean distance between two values. return genreDistance + directorDistance + writerDistance

Data: Input: The name of the movie ,

Amount , User Name Result: Featured Videos newMovie = movie name distances=[] neighbors = []

end for in do if movie not in history then

Add distances to the distances array using the ’Similarities’ metric between the given movie and the rest of the movies in the database. end distances.sort() for in do

Add to ℎ calculated distances. end for ℎ in ℎ do

View featured video data ℎ end Algorithm 4: An algorithm that returns Recommended Videos based on user preferences.

Data: Input: movie’s name,

k - number of films searched Result: Prediction: k - movies’ name movie = movie information database row neighbors = KNN algorithm using the taxi metric, given k - amount of movies to be found end for ℎ in ℎ do neighbors = KNN algorithm using the Cosine distance metric end for ℎ in ℎ do neighbors = KNN algorithm using the

Euclidean metric = [] for ℎ in ℎ do = average rating of the movie (additional information from knn) += [ℎ, ] end return Algorithm 5: An algorithm containing various metrics to find the best matches for the user.

 9. Data base 9.1. Used Database

The following database was used for demonstration purposes in a non-commercial, scientific manner - IMDB [32] data set downloaded from datasets.imdbws.com. Tables used: • name.basics.tsv • title.basics.tsv • title.crew.tsv • title.ratings.tsv The database, after our simplifications and prior preparation, contains a collection of 9,827 films with information:

 9.2. Description of the columns

The set consists of 6000 rows and 7 columns. A detailed description is provided below: 1. OriginalTitle - Original title of a movie. 2. Genres - List of movie genres. 3. AverageRating - Average rating of a movie. 4. Writers - A list of writers of a given movie. 5. Directors - A list of directors of a given movie. 6. Genres_bin - Converted column ’Genres’ to a numerical form. 7. Writers_bin - Converted column ’Writers’ to a numerical form. 8. Directors_bin - Converted column ’Directors’ to a numerical form.

Based on the data base above, we have created several rankings that show the popularity ratio of the data that was used in the KNN algorithm: 10. Conclusion and future work In order to improve the operation of the algorithm and to make the use of it more enjoyable, you can use a more friendly GUI in the future. To make the algorithm work better, it is also possible to use more data (more extensive user history) to further refine the metic used.

 [7] F. Bonanno , G. Capizzi , A. Gagliano , C. Napoli , Optimal management of various renewable energy sources by a new forecasting method , in: SPEEDAM 2012 - 21st International Symposium on Power Electronics, Electrical Drives, Automation and Motion , 2012 , pp. 934 - 940 . doi: 10 .1109/SPEEDAM. 2012 . 6264603 . [8] Y. Zhang , S. Cheng, Y. Shi , D.-w. Gong, X. Zhao , Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm , Expert Systems with Applications 137 ( 2019 ) 46 - 58 . [9] M. Ren , Y. Song , W. Chu , An improved locally weighted pls based on particle swarm optimization for industrial soft sensor modeling , Sensors 19 ( 2019 ) 4099 . [10] D. Połap , M. Woźniak , C. Napoli , E. Tramontana, R. Damaševičius , Is the colony of ants able to recognize graphic objects? , Communications in Computer and Information Science 538 ( 2015 ) 376 - 387 . doi: 10 .1007/978-3- 319 -24770-0_ 33 . [11] M. Woźniak , A. Sikora , A. Zielonka , K. Kaur , M. S. Hossain , M. Shorfuzzaman , Heuristic optimization of multipulse rectifier for reduced energy consumption , IEEE Transactions on Industrial Informatics 18 ( 2021 ) 5515 - 5526 . [12] F. Bonanno , G. Capizzi, C. Napoli , Some remarks on the application of rnn and prnn for the chargedischarge simulation of advanced lithium-ions bat- [1] M. A. Sanchez , O. Castillo , J. R. Castro , Generalized tery energy storage , in: SPEEDAM 2012 - 21st Intertype-2 fuzzy systems for controlling a mobile robot national Symposium on Power Electronics, Electriand a performance comparison with interval type- cal Drives, Automation and Motion, 2012 , pp. 941 - 2 and type-1 fuzzy systems , Expert Systems with 945. doi:10 .1109/SPEEDAM. 2012 . 6264500 . Applications 42 ( 2015 ) 5904 - 5914 . [13] G. M. Khanal , G. Cardarilli , A. Chakraborty , S. Ac- [2] Q.- b . Zhang, P. Wang , Z.-h. Chen , An improved ciarito , M. Y. Mulla , L. Di Nunzio , R. Fazzolari , M. Re, particle filter for mobile robot localization based on A zno-rgo composite thin film discrete memristor, particle swarm optimization , Expert Systems with in: 2016 IEEE International Conference on SemiconApplications 135 ( 2019 ) 181 - 193 . ductor Electronics (ICSE), IEEE, 2016 , pp. 129 - 132 . [3] Y. Li , W. Dong , Q. Yang , S. Jiang , X. Ni , J. Liu, Auto- [14] F. Bonanno , G. Capizzi, S. Coco , C. Napoli , A. Laumatic impedance matching method with adaptive dani, G. Sciuto, Optimal thicknesses determination network based fuzzy inference system for wpt, IEEE in a multilayer structure to improve the spp efiTransactions on Industrial Informatics 16 ( 2019 ) ciency for photovoltaic devices by an hybrid fem - 1076-1085. cascade neural network based approach , in: 2014 In- [4] F. Qu , J. Liu, H. Zhu , D. Zang , Wind turbine condi- ternational Symposium on Power Electronics, Election monitoring based on assembled multidimen- trical Drives, Automation and Motion, SPEEDAM sional membership functions using fuzzy inference 2014 , IEEE Computer Society, 2014 , pp. 355 - 362 . system, IEEE Transactions on Industrial Informat- doi:10 .1109/SPEEDAM. 2014 . 6872103 . ics 16 ( 2019 ) 4028 - 4037 . [15] S. Acciarito , A. Cristini , L. Di Nunzio , G. M. Khanal , [5] M. Woźniak , A. Zielonka , A. Sikora , Driving sup- G. Susi, An a vlsi driving circuit for memristorport by type-2 fuzzy logic control model, Expert based stdp , in: 2016 12th Conference on Ph. D. ReSystems with Applications 207 ( 2022 ) 117798. search in Microelectronics and Electronics (PRIME), [6] M. Woźniak , A. Zielonka , A. Sikora , M. J. Piran , IEEE, 2016 , pp. 1 - 4 . A. Alamri, 6g-enabled iot home environment con- [16] V. S. Dhaka , S. V. Meena , G. Rani , D. Sinwar , M. F. trol using fuzzy rules, IEEE Internet of Things Ijaz, M. Woźniak, A survey of deep convolutional Journal 8 ( 2020 ) 5442 - 5452 . neural networks applied for prediction of plant leaf diseases , Sensors 21 ( 2021 ) 4749 . [17] G. Capizzi , G. Lo Sciuto , C. Napoli , M. Woźniak, neural network-based finger-vein recognition using G. Susi, A spiking neural network-based long-term nir image sensors , Sensors 17 ( 2017 ) 1297. prediction system for biogas production , Neural [28] G. Capizzi , G. Lo Sciuto , M. Woźniak , R. DamašeNetworks 129 ( 2020 ) 271 - 279 . vičius, A clustering based system for automated oil [18] N. Brandizzi , S. Russo , R. Brociek , A. Wajda , First spill detection by satellite remote sensing, Lecture studies to apply the theory of mind theory to green Notes in Computer Science (including subseries and smart mobility by using gaussian area cluster- Lecture Notes in Artificial Intelligence and Lecture ing , volume 3118 , CEUR-WS , 2021 , pp. 71 - 76 . Notes in Bioinformatics) 9693 ( 2016 ) 613 - 623 . [19] F. Bonanno , G. Capizzi, G. Lo Sciuto , C. Napoli, [29] A. T. Özdemir , B. Barshan , Detecting falls with Wavelet recurrent neural network with semi- wearable sensors using machine learning techparametric input data preprocessing for micro-wind niques , Sensors 14 ( 2014 ) 10691 - 10708 . power forecasting in integrated generation systems , [30] K. G. Liakos , P. Busato , D. Moshou , S. Pearson, in: 5th International Conference on Clean Electri- D. Bochtis, Machine learning in agriculture: A cal Power: Renewable Energy Resources Impact, review , Sensors 18 ( 2018 ) 2674 . ICCEP 2015 , 2015 , p. 602 - 609 . [31] G. Iannizzotto , L. Lo Bello , A. Nucita , G. M. Grasso , [20] G. Borowik , M. Woźniak , A. Fornaia , R. Giunta , A vision and speech enabled, customizable , virtual C. Napoli, G. Pappalardo , E. Tramontana , A soft- assistant for smart environments , in: 2018 11th ware architecture assisting workflow executions International Conference on Human System Interon cloud resources , International Journal of Elec- action (HSI) , IEEE, 2018 , pp. 50 - 56 . tronics and Telecommunications 61 ( 2015 ) 17 - 23 . [32] I. IMDb. com, Imdb datasets, 1990 - 2022 . URL: https: doi:10.1515/eletel-2015-0002. //www.imdb.com/interfaces/. [21] M. Wieczorek , J. Siłka , M. Woźniak , S. Garg , M. M. Hassan , Lightweight convolutional neural network model for human face detection in risk situations , IEEE Transactions on Industrial Informatics 18 ( 2021 ) 4820 - 4829 . [22] S. Illari , S. Russo , R. Avanzato , C. Napoli , A cloudoriented architecture for the remote assessment and follow-up of hospitalized patients , in: CEUR Workshop Proceedings , volume 2694 , CEUR-WS , 2020 , pp. 29 - 35 . [23] N. Dat , V. Ponzi , S. Russo , F. Vincelli , Supporting impaired people with a following robotic assistant by means of end-to-end visual target navigation and reinforcement learning approaches , in: CEUR Workshop Proceedings , volume 3118 , CEUR-WS , 2021 , pp. 51 - 63 . [24] R. Brociek , G. Magistris , F. Cardia , F. Coppa , S. Russo , Contagion prevention of covid-19 by means of touch detection for retail stores , in: CEUR Workshop Proceedings , volume 3092 , CEUR-WS , 2021 , pp. 89 - 94 . [25] O. Dehzangi , M. Taherisadr , R. ChangalVala, Imubased gait recognition using convolutional neural networks and multi-sensor fusion , Sensors 17 ( 2017 ) 2735 . [26] M. Wozniak , C. Napoli , E. Tramontana, G. Capizzi, G. Lo Sciuto , R. Nowicki , J. Starczewski , A multiscale image compressor with rbfnn and discrete wavelet decomposition , in: Proceedings of the International Joint Conference on Neural Networks , volume 2015 - September, Institute of Electrical and Electronics Engineers Inc ., 2015 . doi: 10 .1109/ IJCNN. 2015 . 7280461 . [27] H. G. Hong , M. B. Lee , K. R. Park , Convolutional