Springer Proceedings in Business and Economics Nenad Mladenović Angelo Sifaleras Marija Kuzmanović Editors Advances in Operational Research in the Balkans XIII Balkan Conference on Operational Research Springer Proceedings in Business and Economics More information about this series at http://www.springer.com/series/11960 Nenad Mladenović Angelo Sifaleras Marija Kuzmanović • • Editors Advances in Operational Research in the Balkans XIII Balkan Conference on Operational Research 123 Editors Nenad Mladenović Department of Industrial Engineering Khalifa University Abu Dhabi, UAE Angelo Sifaleras Department of Applied Informatics University of Macedonia Thessaloniki, Greece Marija Kuzmanović Faculty of Organizational Sciences University of Belgrade Belgrade, Serbia ISSN 2198-7246 ISSN 2198-7254 (electronic) Springer Proceedings in Business and Economics ISBN 978-3-030-21989-5 ISBN 978-3-030-21990-1 (eBook) https://doi.org/10.1007/978-3-030-21990-1 © Springer Nature Switzerland AG 2020 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Organization Editors Nenad Mladenović, Khalifa University, UAE Angelo Sifaleras, University of Macedonia, Greece Marija Kuzmanović, University of Belgrade, Serbia Scientific Committee Zoran Babic, University of Split, Croatia Taner Bilgiỗ, Bogaziỗi University, Turkey Sally Brailsford, University of Southampton, United Kingdom Mirjana Cangalovic, University of Belgrade, Serbia Tatjana Davidovic, Mathematical Institute, Serbia Marc Demange, ESSEC Business School in Paris, France Abraham Duarte, Universidad Rey Juan Carlos, Spain Ioan Dzitac, Aurel Vlaicu University of Arad, Romania Jose Rui Figueira, Technical University of Lisbon, Portugal Haris Gavranovic, International University of Sarajevo, Bosnia and Hercegovina Andreas Georgiou, University of Macedonia, Greece Alan Hertz, GERAD—Ecole des HEC, Canada Marius Iosifescu, Romanian Academy, Romania Cem Iyigun, Middle East Technical University, Turkey Milojica Jacimovic, Faculty of Sciences and Mathematics, Montenegro Maksat Kalimoldaev, Institute of Problems of Information and Control, Kazakhstan Vera Kovacevic-Vujcic, University of Belgrade, Serbia Jozef Kratica, Mathematical Institute, Serbia Martine Labbé, Université Libre de Bruxelles, Belgium Zohar Laslo, SCE—Shamoon College of Engineering, Israel v vi Organization Goran Lesaja, Georgia Southern University, USA Dragana Makajic-Nikolic, University of Belgrade, Serbia Milan Martic, University of Belgrade, Serbia Nikolaos Matsatsinis, Technical University of Crete, Greece Ion Mierlus Mazilu, Technical University of Civil Engineering, Romania Athanasios Migdalas, Aristotle University of Thessaloniki, Greece Miodrag Mihaljevic, Serbian Academy of Sciences and Arts, Serbia José Andrés Moreno Pérez, Universidad de La Laguna, Spain Dritan Nace, Universite de Technologie de Compiegne, France Zoran Ognjanovic, Mathematical Institute, Serbia Ceyda Ouz, Koỗ University, Turkey Panos Pardalos, University of Florida, USA Vangelis Paschos, Universite Paris Dauphine, France Vasile Preda, University of Bucharest, Romania Bozidar Radenkovic, University of Belgrade, Serbia Dragan Radojevic, Mihajlo Pupin Institute, Serbia Nikolaos Samaras, University of Macedonia, Greece Gordana Savic, University of Belgrade, Serbia Silviu Sburlan, Naval Academy Mircea cel Batran Constanta, Romania Yannis Siskos, University of Pireaus, Greece Roman Slowinski, Poznan University of Technology, Poland Grazia Speranza, University of Brescia, Italy Ioan Stancu-Minasian, Romanian Academy, Romania Stanko Stanic, University of Banja Luka, Bosnia and Herzegovina Milan Stanojevic, University of Belgrade, Serbia Milorad Stanojevic, University of Belgrade, Serbia Bogdana Stanojević, Mathematical Institute, Serbia Dusan Teodorovic, University of Belgrade, Serbia Blagorodna Todosioska, University “Ss Cyril and Methodius”- Skopje, Severna Macedonia Romica Trandafir, Technical University of Civil Engineering, Romania Alexis Tsoukias, Universite Paris Dauphine, France Dragan Urosevic, Mathematical Institute, Serbia Milorad Vidovic, University of Belgrade, Serbia Mirko Vujosevic, University of Belgrade, Serbia Dionysius Yannacopoulos, Technological Educational Institution of Piraeus, Greece Amirgaliyev Yedilkhan, Institute of Information and Computational Technologies, Kazakhstan Lidija Zadnik Stirn, University of Ljubljana, Slovenia Constantin Zopounidis, Technical University of Crete, Greece Gỹvenỗ ahin, Sabanci University, Turkey Organization Honorary Scientific Committee Slobodan Guberinic, University of Belgrade, Serbia Basil Manos, Aristotle University of Thessaloniki, Greece Slobodan Krcevinac, University of Belgrade, Serbia Byron Papathanasiou, Aristotle University of Thessaloniki, Greece Sotirios Papachristos, University of Ioannina, Greece Radivoj Petrovic, University of Belgrade, Serbia Dragos Cvetkovic, Serbian Academy of Sciences and Arts, Serbia Gradimir Milovanovic, Serbian Academy of Sciences and Arts, Serbia Constantin Tsouros, Aristotle University of Thessaloniki, Greece Organizing Committee Gordana Savic, University of Belgrade, Serbia Marija Kuzmanović, University of Belgrade, Serbia Dragana Makajic-Nikolic, University of Belgrade, Serbia Bogdana Stanojeviỗ, Mathematical Institute, Serbia Nebojsa Nikolic, University of Belgrade, Serbia Biljana Panic, University of Belgrade, Serbia Milos Nikolic, University of Belgrade, Serbia Gordana Nastic, Mathematical Institute, Serbia Milena Popovic, University of Belgrade, Serbia Minja Marinovic, University of Belgrade, Serbia Bisera Andric-Gusavac, University of Belgrade, Serbia Dusan Dzamic, University of Belgrade, Serbia vii Preface This book is a monograph from submissions from the XIII Balkan Conference on Operational Research “OR in Balkans—Recent Advances” (BALCOR 2018) held in Belgrade, Serbia during May 25–28, 2018 This year, we have received more than 166 contributions of 249 authors from 37 countries After rigorous review process, 17 high-quality papers are included in this monograph Selected papers refer to theory and application of operational research Theoretical papers address the problems of multi-objective optimization, pattern recognition, and reliability improvement Application papers are related to variety of fields: health care, tourism, forest policy, inventory, project management, ecology, and ICT management The book is divided into four main parts: Part I: Combinatorial Optimization & Heuristics Part II: Multicriteria Decision Analysis & Multi-objective Optimization Part III: Performance Measurement & Data Envelopment Analysis Part IV: Business Applications The organizers of the conference are: The Mathematical Institute of the Serbian Academy of Sciences and Arts (SANU), University of Belgrade: Faculty of Organizational Sciences, Faculty of Transport and Traffic Engineering, Yugoslav Society for Applied and Industrial Mathematics (JUPIM), and Society of Operations Researchers of Serbia (DOPIS), Belgrade, Serbia We appreciate the support of the Ministry of Education, Science and Technological Development of the Republic of Serbia and EURO—The Association of European Operational Research Societies We wish to express our heartfelt appreciation to the Editorial Committee, reviewers, and our students We appreciate all the authors and participants for their contributions that, made this conference and monograph possible Finally, we thank the publisher, Springer Abu Dhabi, UAE Thessaloniki, Greece Belgrade, Serbia February 2019 Nenad Mladenović Angelo Sifaleras Marija Kuzmanović ix Contents Part I Combinatorial Optimization & Heuristics Dichotomy Algorithms in the Multi-class Problem of Pattern Recognition Damir N Gainanov, Nenad Mladenović and Berenov Dmitriy Determining the Set of the Most Critical System Components—Optimization Approach Petar Pavlović, Dragana Makajić-Nikolić and Mirko Vujošević 15 Metaheuristics and Error Detection Approaches for Multiproduct EOQ-Based Inventory Control Problem Slobodan Antic and Lena Djordjevic Milutinovic 31 Part II Multicriteria Decision Analysis & Multi-objective Optimization On Fuzzy Solutions to a Class of Fuzzy Multi-objective Linear Optimization Problems Bogdana Stanojević and Milan Stanojević 63 Multiattribute Methods as a Means for Solving Ecological Problems in Water Resources—Lake Pollution Milena J Popović, Bisera Š Andrić Gušavac and Ana S Katić 77 Forest Policy Evaluation in European Countries Using the PROMETHEE Method Stefanos Tsiaras and Zacharoula Andreopoulou 95 The Contribution of ICT in EU Development Policy: A Multicriteria Approach 111 Christiana Koliouska, Zacharoula Andreopoulou and Mariana Golumbeanu xi 278 N Vrani´c et al ∀c ∈ C, sc = arg maxu(c, s) In RS the utility of an item is usually represented by a rating which indicates how much a particular user liked a particular item In CF, items are recommended to the target user through an analysis of neighbor users ratings on those items (Koohi and Kiani 2017) The common methods used in CF to find neighbor users are measures of similarity (Bobadilla et al 2013) The most popular similarity measures in CF are the Pearson correlation coefficient and cosine-based coefficient (Candilier et al 2007) 2.2 Interpolative Boolean Algebra The interpolative Boolean algebra (IBA) is the consistent [0, 1]-valued generalization of Boolean algebra (BA) introduced by Radojevic (2000) IBA is utilized as a basis for Boolean consistent fuzzy logic, since all laws on which BA relies on are satisfied, including axioms of excluded middle and contradiction (Radojevic 2008a) In IBA framework, all BA elements are realized as corresponding IBA-based functions and IBA-based logical operations extend classical operations Unlike most of many-valued logics (including conventional fuzzy logic), IBA is based on the principle of structural functionality (SF) The main reason for introducing this principle as opposite to traditional truth functionality (TF) is that TF is binary in its essence In other words, TF is valid only in the classical two-valued case from the perspective of Boolean laws, and not in the general case (Radojevi´c 2008b) By introducing SF principle, Radojevic (Radojevic 2008a) emphasizes the structure of the logical expression Hence, IBA consists of two levels: symbolic and valued On the symbolic level, a logical expression is uniquely mapped to a generalized Boolean polynomial (GBP) Subsequently, the values are introduced and the expression is evaluated on the valued level The structural functionality also implies that logical functions are vectors in nature, and the Boolean consistent calculations of values are accomplished by the immanent structure vectors On the symbolic level, IBA is technically based on IBA transformation rules and GBPs IBA resolve the procedure of transforming a Boolean function into GBP directly: any logical function can be mapped into the corresponding GBP according to the set of predefined rules (Radojevic 2008b) For a set of primary attributes (elements of B A(Ω)), the transformation procedure of Boolean functions into GBP is defined in (Radojevic 2008c): • For combined elements F(a1 , , an ), G(a1 , , an ) ∈ B A(Ω): (F ∧ G)⊗ = F ⊗ ⊗ G ⊗ , (F ∨ G)⊗ = F ⊗ + G ⊗ − (F ∧ G)⊗ , A Recommender System With IBA Similarity Measure 279 (¬F)⊗ = − F ⊗ • For primary variables {a1 , , an }: (ai ∧ a j )⊗ = ⊗ a j , i = j , , i = (ai ∨ a j )⊗ = + a j − (ai ∧ a j )⊗ , (¬ai )⊗ = − GBPs have the ability to process values of primary attributes from the real unit interval [0, 1] in a Boolean consistent manner, i.e., to preserve all algebraic characteristics GBP is a polynomial whose variables are primary attributes (free elements of Boolean algebra) while operators are standard + and -, and generalized product ⊗ Generalized product is any function which maps ⊗ : [0, 1]×[0, 1] → [0, 1] and satisfies all four axioms of t-norms (commutativity, associativity, monotonicity, boundary condition) and the additional axiom of non-negativity condition (Radojevic 2008a) In the case of two attributes = {a, b}, generalized product is from the following interval: max(a + b − 1, 0) ≤ a ⊗ b ≤ min(a, b) Depending on the nature of the primary attributes, we can discuss three marginal cases for operator selection The first case refers to elements of the same or the similar nature and implies the usage of minimum as generalized product: a ⊗ b = min(a, b) The second involves elements of the same or the similar nature but negatively correlated, where Lukasiewicz t-norm is proposed: a ⊗ b = max(a + b − 1, 0) In the case of statistically independent elements that are different by nature standard product is used: a⊗b =a·b 2.2.1 IBA Similarity Measure Measuring similarity with logical relations of implication, bi-implication, and equivalence, is considered as a valuable and prominent approach to similarity modeling 280 N Vrani´c et al (Luukka 2011; Le Capitaine 2012; Beliakov et al 2014) As opposite to distancebased and probabilistic similarity measures, logic-based relations are particularly suitable for comparing objects/attributes described by the intensity of the properties In fact, it offers a different perspective in perceiving similarity In IBA framework, the equivalence relation is used for defining a similarity measure (Poledica et al 2013, 2015) The relation of equivalence is defined as the following logical expression: (a ⇔ b) = (a ⇒ b) ∧ (b ⇒ a) As it is stated, the first step in dealing with logical function in IBA framework is to assess its structure In other words, the IBA transformation rules should be applied The transformation of the equivalence relation to GBP is conducted as follows: (a ⇔ b)⊗ = ((a ⇒ b) ∧ (b ⇒ a))⊗ = (a ⇒ b)⊗ ⊗ (b ⇒ a)⊗ = (1 − a + a ⊗ b) ⊗ (1 − b + a ⊗ b) =1−b+a⊗b−a+a⊗b−a⊗a⊗b+a⊗b−a⊗b⊗b +a⊗b⊗a⊗b =1−b+a⊗b−a+a⊗b−a⊗b+a⊗b −a⊗b+a⊗b =1−b−a+2·a⊗b The relation of equivalence in the sense of IBA, S I B A : [0, 1]2 → [0, 1], can be used as a similarity measure because it satisfied all predefined conditions, i.e., GBP that uniquely corresponds to the relation of equivalence satisfies the properties of reflexivity, transitivity, and symmetry when minimum is used as generalized product (min:= ⊗) The detailed proofs may be found in (Poledica et al 2013) The fact that the consistent comparison of different objects is only possible by the same criteria (Radojevic 2010) supports the usage of minimum as generalized product in IBA similarity measure GBP of IBA similarity measure and its realization on the valued level is as follows: S I B A (a, b) = (a ⇔ b)⊗ = − b − a + · a ⊗ b = − b − a + · min(a, b) IBA similarity measure is particularly valuable since its clear interpretation given in Fig The similarity of two attributes a and b in IBA framework is equal to the sum of two parts: the intensity of both having the same property and the intensity of both not having that property (Poledica et al 2013) In the case of multi-attribute object comparison, IBA similarity should be used along with chosen aggregation operator In order to stay within IBA framework, it is advised to use logical aggregation (LA) (Milosevic et al 2018) LA is a transparent, Boolean consistent manner of attribute aggregation using logical or pseudological functions (Radojevic 2008b; Milosevic et al 2018) There are two distinctive approaches in IBA-based framework for modeling similarity: a simple attributeby-attribute (the similarity between objects is LA of individual IBA similarities of A Recommender System With IBA Similarity Measure 281 Fig IBA similarity function attributes) and a comparison on the level of the object (LA function is used to uniquely represent the object and IBA similarity is used afterward) More details about these approaches may be found in (Milosevic et al 2018) A Recommender System with IBA Similarity Measure In this section, the research methodology for proposed recommendation system is introduced and the illustrative example is shown The collaborative filtering provides recommendations based on the similarities between users or items Predictions are made for the target user through reliance on the entire database, which include user ratings of items CF method can be divided into user-based CF and item-based CF The center of focus in the current study is the user-based approach In the user-based approach, it is assumed that if some users had similar interests in the past, they will have similar interests in the future Based on this assumption, items are recommended to the target user The user-based CF operates on an n × m matrix, with n users and m number of items The matrix records the preferences of n users on m movies, in other words, it shows ratings of users on specific items When we want to recommend an item to a specific user, users which are the most similar to the target user, i.e., neighbor users, are determined by the system Prediction for the target user is made according to earlier ratings of users on items which the target user did not rate Actually, the recommended items will be those items which neighbor users rated with high rating Therefore, we can say that the ratings provided by users for items are the key input to CF recommender systems Figure presents how the collaborative recommender method functions By looking for the similarities between users, we are searching for a set of N neighbor users from the database who have similar preferences as specific user x In other words, we are looking for users who rate movies in the same or at least at the similar manner as the target user One of the common methods used in CF to find neighbor users is through resorting to similarity measures In this paper, we are utilizing recently proposed IBA-based similarity framework to find a set of N neighbor users Particularly, the simple attribute-by-attribute com- 282 N Vrani´c et al Fig Recommender system parison is applied, i.e., IBA similarity measure is used to assess similarities between individual rankings and simple average is used as LA operator Hereafter, only the users with similarity level higher that predefined threshold are considered as neighbors There can also be a predefined number of the most similar neighbors, similarly as in k-NN algorithm After the set of neighbor users has been created, the next step is to predict ratings of movies which the target user did not rate The prediction of the rating of user x on the specific item (p) that also factors the relative proximity of the nearest neighbor N is done using a simple average as shown in Eq (7) (Koohi and Kiani 2017): pr ed(x, p) = n n ri p i=1 where n represents the number of neighbor users To recommend items to the target user, first of all, a numerical value for unrated items will be calculated, and then a list of top N high-valued items to be recommended to the target user is prepared In most cases, the range of users ratings is on a scale of 1–5, whereby indicates most interesting and signifies a poor opinion On the basis of this assumption, two types of recommendations are 5/4321 and 54/321 methods, proposed by Tsai and Hung (2012) In the 5/4321 method, only items with a rating value of will be recommended to the target user Also in the 54/321 method, only items with rating values of and belong to the recommendation class, while ratings from one to three belong to non-recommendation class (Ramezani et al 2014; Koohi and Kiani 2017) In this research, we used 54/321 method, where items that get ratings and by the neighbor users are recommended to the target user and item ratings from to are rejected In general, this model consists of two steps in the shown method; first one is to calculate similarity between users using IBA similarity framework, and next one to predict ratings using an average of ratings of n neighbor users on the item p The model may be considered as quite flexible since both LA function in the attribute- A Recommender System With IBA Similarity Measure 283 by-attribute comparison and simple average in the ranking prediction step may be replaced with more powerful aggregation operator 3.1 Illustrative Example The CF method with IBA similarity measure described above is illustrated on the simple example For instance, the normalized ratings of movies given by users are shown in Table In this example, we choose User as the target user First, we need to calculate similarity between User and all other users, and for that purpose we use IBA similarity measure and the simple average operator For example, when we calculate similarity between User and User we find movies which both users rated, and calculate similarity for each movie using IBA similarity measure Similarity between User and User for Movie is 0.95 and for Movie is 0.9 After we apply simple average operator on obtained similarities we get overall similarity (0.925) between User and User Similarities between User and the two other users are respectively 0.825 and 0.35 In case we use 0.8 as a similarity threshold Users and will be a part of neighbor set As we can see from the Table our target user didn’t rate movies and 5, so we need to predict ratings for those movies using ratings that User and gave to this movie We calculated the rating for User and rescaled it in the starting range of to As a result, User would probably rate movie with grade 4.25 and movie with 0.625 Based on 54/321 method, where only items with rating values of and will be recommended to the target user, we will reject movie and recommend movie to the User Experiment and Evaluation We implemented proposed method in Matlab, the experiments have been executed on Intel(R) Xeon(R) CPU E5-2680 v2@ 2.80 GHz, 64 GB memory The operating system is Windows Server 2008 R2 Enterprise Table Movie ratings—normalized values Movie Movie Movie Movie Movie User 0.10 0.20 0.80 0.00 0.15 User 0.15 0.10 0.00 0.70 0.00 User 0.30 0.00 0.90 0.90 0.10 User 0.00 0.90 0.20 0.20 0.75 284 N Vrani´c et al 4.1 Datasets Analysis To evaluate the prediction models and validate the proposed recommendation method we used three different datasets, which are popular in the domain of recommender systems: MovieLens 100K, MovieLens 1M and CiaoDVD The GroupLens research group (Harper and Konstan 2016) at the University of Minnesota collected the MovieLens datasets (http: //grouplens.org/datasets/movielens/) MovieLens consists of two datasets, first one is smaller named MovieLens 100k which includes 100000 ratings of 1682 movies by 943 users and a bigger one, named MovieLens 1M, which includes 1000209 ratings of 3952 movies by 6040 users In both datasets ratings are on a scale of (bad film) to (masterpiece) Every user has rated at least 20 movies and all movies have been rated at least once In the MovieLens 100K dataset just 6.3% of movie ratings are available, and in MovieLens 1M only 4.2% of ratings are available Therefore, sparsity levels of those two datasets are high We also tested our approach on CiaoDVD dataset collected by Guo et al (2014) in December 2013 by crawling 17 categories of film DVDs from the dvd.ciao.co.uk website The rating scale on this dataset is from to This dataset includes 35835 ratings given by 2248 users over 16861 movies The sparsity level of this dataset is higher than MoviLens datasets, just 1% of ratings are available Ciao allows the users to establish social relations (i.e., trust relationships) with others General statistics of these datasets are summarized in Table 4.2 Performance Measures Two types of measures can be used for performance evaluation of a recommender system The first one is coverage, where the amount of items that can be recommended to the users is counted This measure evaluates a recommender system ability to provide recommendations In our case, a certain movie with only a few ratings might not be recommended to a specific user, because no similar users have rated that particular movie The overall coverage for all users is calculated as the percentage of items of which a prediction is requested and for which the recommender system is able to make a prediction (Herlocker et al 1999) The second type of measure Table General statistics of the MovieLens 100 k, MovieLens 1M, and CiaoDVD datasets Dataset MovieLens 100K Number of users 943 Number of items Number of ratings Sparsity level (%) 1682 100000 93.7 MovieLens 1M 6040 3952 1000209 9.8 CiaoDVD 2248 16861 35835 99 A Recommender System With IBA Similarity Measure 285 is accuracy, which compares the recommendations with the actual known relevant items of ratings Mean absolute error (MAE) is a standard and popular measure used to evaluate the accuracy of preference modeling methods in recommender systems MAE gives the deviation of the estimated preference from the true preference value specified by the active user The lower the MAE is, the better the prediction ratings Besides MAE we used three statistical measures of accuracy: precision, recall and F1 score Precision represents the probability that an item recommended as relevant is truly relevant It is defined as the ratio of items correctly predicted as relevant among all the items selected Recall represents the probability that a relevant item will be recommended as relevant It is defined as the ratio of items correctly predicted as relevant among all the items known to be relevant Precision and recall are inversely related and are dependent on the length of the recommendation list With the increasing amount of retrieved items, the recall increases, but precision usually drops with larger item sizes This is the reason why both measures are used The F1 score combines the precision and recall to evaluate the algorithm performance A method with high coverage, precision, recall, and F1 scores and a low MAE value is considered as a good recommendation method Results The aim of the experiment was to compare RS performance with IBA similarity measure and the two commonly used similarity measures: Pearson correlation and cosine-based approach We considered different neighbor sizes and different similarity thresholds since these parameters are critical for the performance of RS First, we tested different neighbor sizes from to 30 (Nilashi et al 2014) Further, we considered neighbors with three different similarity thresholds 0.9, 0.8 and 0.7 For example, the neighbor set includes all users whose similarity to a specific user is higher than 0.9 We repeated the experiment 10 times to examine the consistency and significance of the results Table shows the performance measures and coverage for the proposed IBA RS approach for predefined number of neighbors on each dataset As expected the coverage is higher when the number of neighbors is higher Further, we can see that MAE slowly decays as coverage is growing Based on the considered performance measures it is hard to conclude what is the best number of neighbors For instance, as it can be seen in Table for MovieLens 100K dataset the best recall was obtained with 10 neighbors, the precision is higher with 20 neighbors and MAE is the best with 30 neighbors The results for precision, recall, and F1 not vary much regardless of the number of neighbors We can conclude that it is not crucial how many neighbors we include in the coverage, but how relevant the neighbors are For that reason, we also considered a similarity threshold to separate the users whose similarity is above the predefined level 286 N Vrani´c et al Table Performances of RS with IBA similarity measure—different neighbor sizes Dataset MovieLens 100K MovieLens 1M CiaoDVD Number of neighbors Coverage (%) MAE Precision (%) Recall (%) F1 (%) 17.50 0.18 62 89 73 10 24.40 0.18 64 94 76 15 29.50 0.22 58 78 66 20 36.78 0.15 70 77 75 30 44.35 0.13 66 87 75 8.20 0.13 78 83 80 10 13.00 0.12 71 83 76 15 16.00 0.17 67 67 67 20 21.62 0.13 76 82 79 30 29.12 0.13 69 78 73 3.80 0.25 30 89 45 10 4.20 0.24 32 88 47 15 4.90 0.25 28 87 42 20 6.70 0.27 27 85 40 30 9.40 0.28 25 85 39 In the experiment, we achieved much higher coverage rates when we used a similarity threshold instead of a predefined number of neighbors Further, we only tasted CF with Pearson correlation and cosine-based similarity measures with different similarity thresholds Table presents performance of proposed IBA RS approach and IBA with Pearson and cosine-based similarity measures, with different similarity thresholds for each dataset As expected the coverage is higher when a similarity threshold is lower As it can be seen in Tables and 4, for IBA RS value of recall is always higher than precision regardless of the number of neighbor users Recall calculates how many of the movies that should be recommended will be recommended to the target user Thus, in case of IBA RS there are only a small number of movies that are not but should be recommended We applied 54/321 method for movie recommendation where ratings and are good suggestions and movie ratings from to are rejected The main issue in prediction with the proposed IBA RS was to make a difference between ratings and In that case, small deviations from actual rating caused that some movies were recommended and shouldn’t be Therefore, the value of precision is lower In future work, we could consider replacing simple average in the ranking prediction with more powerful aggregation operator to obtain a higher value of precision From the aspect of MAE and recall the recommender system with IBA similarity outperforms both Pearson correlation and cosine-based approach (Table 4) In terms of precision and F1, the RS with IBA similarity measure gives the same or better A Recommender System With IBA Similarity Measure 287 Table Performances of RS with IBA similarity measure—different similarity thresholds Dataset Similarity measure MovieLens IBA 100K Pearson Cosinebased MovieLens IBA 1M Pearson Cosinebased CiaoDVD IBA Pearson Cosinebased Similarity threshold Coverage (%) MAE Precision (%) Recall (%) F1 (%) 0.9 42.17 0.16 50 85 63 0.8 87.20 0.16 63 92 75 0.7 96.50 0.17 67 92 77 0.9 42.17 0.27 70 82 75 0.8 87.20 0.34 67 89 75 0.7 96.50 0.39 80 84 82 0.9 42.17 0.30 55 71 63 0.8 87.20 0.34 65 75 70 0.7 96.50 0.35 60 68 64 0.9 19.63 0.08 75 92 83 0.8 75.07 0.15 62 71 66 0.7 82.59 0.16 62 74 67 0.9 19.63 0.15 78 86 82 0.8 75.07 0.22 72 76 74 0.7 82.59 0.25 68 72 70 0.9 19.63 0.21 79 85 82 0.8 75.07 0.29 65 67 66 0.7 82.59 0.23 64 72 68 0.9 11.50 0.191 43 75 57 0.8 42.58 0.181 57 63 60 0.7 65.87 0.193 53 61 57 0.9 11.50 0.43 52 62 57 0.8 42.58 0.28 63 79 71 0.7 65.87 0.47 59 65 62 0.9 11.50 0.31 48 58 53 0.8 42.58 0.33 53 61 57 0.7 65.87 0.42 49 57 53 results compared to the cosine-based coefficient, but slightly lower percentages than Pearson correlation Conclusion In this research we proposed a collaborative filtering method that employs IBA similarity measure for calculation of similarity between users In order to analyze 288 N Vrani´c et al and evaluate the proposed logic-based similarity measure in recommender systems we tested three popular datasets: MovieLens 100K, MovieLens 1M, and CiaoDVD For the purpose of comparison, we also applied two most commonly used similarity measures: Pearson correlation and cosine-based approach In the experiment, the parameters critical for performance were tested, e.g., similarity threshold from 0.7 to 0.9 or neighbor sizes from to 30 The performances of a recommender system were measured using several statistical indicators: mean absolute error, precision, recall, and F1 score A recommender system with IBA similarity measures outperformed the others with respect to MAE and recall The results also showed that IBA similarity measure obtained slightly lower precision and F1 than Pearson correlation, but slightly higher compared to cosine-based similarity measure In general, the results have indicated that IBA similarity measure is suitable to be used in recommender systems, especially in the cases when it is needed to recommend as many items as possible (high recall) Even though statistical measures are traditionally used in recommender systems, proposed logic-based approach utilizing IBA similarity measure showed promising results on the tested datasets For future work, the following issues are to be considered: • Improvement of the proposed model by replacing simple average with more powerful aggregation operator in the ranking prediction • Single evaluation performance measure for measuring the performance of a recommender system that combines all the proposed measures • Implementation of IBA framework in a content-based recommender system and compare its 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Filtering (CF), 275–277, 281, 287 Conjoint analysis, 139, 140, 142, 143, 145, 146, 148, 150 D Data Envelopment Analysis (DEA), 97, 106, 139–142, 144, 145, 147, 149, 150, 152, 162, 163, 167, 168, 173–178, 181, 183, 187–189, 196–198, 200–202 Data mining, 168, 266, 268 Decision-making unit, 173, 183 Development policy, 111, 113, 116, 118, 119 Disjoint routes, 187, 188, 195, 201 Dynamic discrete inventory control model, 31–34, 40, 50, 57 E Efficiency analysis, 141, 187, 188, 190, 196, 201 Efficiency assessment, 141, 144, 174 Error detection approaches, 57 European Union (EU), 95–98, 102, 106, 111–114, 167, 168, 177, 181, 210, 238, 244 F Fingerprint, 237–248 Forest Policy, 95–98, 101–103, 105, 106 Fraud detection, 253 Fuzzy coefficients, 63–65, 68, 72, 75 Fuzzy linear programming, 64, 125 H Hamming distance, 12 Heuristic algorithm, 15, 28 Heuristics, 23, 31, 33, 34, 38–40, 42–44, 46–49, 57 I IBA similarity measure, 275, 280, 282, 283, 285–288 Importance measures, 15, 16, 18, 21, 25–29 Index, 23, 67, 81, 83, 85, 141, 142, 170, 175, 176, 224, 226, 227, 229, 230, 232 Information and Communication Technology (ICT), 111 Interpolative Boolean Algebra (IBA), 275–278 Inventory and promotion planning, 126, 134 IT project, 221, 222, 226, 234 © Springer Nature Switzerland AG 2020 N Mladenović et al (eds.), Advances in Operational Research in the Balkans, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-21990-1 291 292 L Lake pollution, 79 M Maturity, 221–227, 229, 230, 234, 235 Medical devices, 207, 209, 211, 214–216 Metaheuristics, 33, 44, 46, 57 Minimal Cut Sets (MCSs), 15, 17 Mobile advertising, 251–254 Model, 17, 22, 25, 27, 28, 64, 125–130, 132, 133, 168, 173, 174, 177, 188–190, 192–194, 196, 197, 201, 212, 252, 254, 276, 282, 288 Multiattribute methods, 77, 79 Multi-card, 237–239, 243, 248 Multi-criteria method, 80, 82, 111, 113 Multi-objective optimization, 64, 66 Multiple Criteria Decision Analysis (MCDA), 79, 81, 92, 95–98, 106, 114 Multiple testing, 257–262 N Network performance, 187, 189, 190, 195, 196, 200 O Operational Research (OR), 83, 167, 168 Optimization, 15–17, 28, 31, 32, 34, 38, 49, 57, 63–65, 79, 80, 168, 211, 224 P Parametric analysis, 64, 65, 69, 75 Partition function, 7, 11 Pattern recognition, 3, 4, 13 Perception, 139, 141, 143, 147, 150, 152, 161–163, 215, 216, 226 PERT, 221, 225, 227, 234 PIN, 238–248 Preferences, 87, 88, 114, 116, 133, 139–143, 146, 147, 150, 154, 156–158, 160, 163, 267, 270, 276, 277, 281 Index Production, 32, 116, 126, 129–131, 134, 141, 144, 173, 211 PROMETHEE II, 77, 78, 82, 83, 85, 87, 88, 90, 92, 96, 111, 113–116, 119 Public procurement, 207–211, 216 Q Quality, 13, 32, 44, 48, 49, 78, 80–82, 87, 88, 90, 92, 143, 148, 161, 187, 188, 195, 200, 201, 207–209, 211–213, 215–217, 222, 246–248, 276 Quality losses, 207, 216 Quality of Service (QoS), 187–189 R Recommender System (RS), 268, 275–277, 281, 282, 284–286, 288 Reliability, 15–17, 19, 27, 216, 228 S Service class mapping, 189, 190, 192, 198, 201 Set covering problem, 15, 18, 22, 28 Similarity modeling, 279 Ski injury, 265, 268, 269 Ski lift congestion, 266 Ski lift transportation data, 265–272 Ski resorts decision-making, 265 Smart card, 237–239, 243–245, 247, 248 Spreadsheet, 31, 33, 34, 40, 42, 49–52, 55–57 T Travel and tourism, 167–169, 176–180, 183 U User-based collaborative filtering, 276 W Water resources, 78, 80–82, 116 ... (electronic) Springer Proceedings in Business and Economics ISBN 97 8-3 -0 3 0-2 198 9-5 ISBN 97 8-3 -0 3 0-2 199 0-1 (eBook) https://doi.org/10.1007/97 8-3 -0 3 0-2 199 0-1 © Springer Nature Switzerland AG 2020 This... N Mladenovi´c et al (eds.), Advances in Operational Research in the Balkans, Springer Proceedings in Business and Economics, https://doi.org/10.1007/97 8-3 -0 3 0-2 199 0-1 _2 15 16 P Pavlovi´c et al... Engineering, Khalifa University, Abu Dhabi, UAE e-mail: nenadmladenovic12@gmail.com © Springer Nature Switzerland AG 2020 N Mladenovi´c et al (eds.), Advances in Operational Research in the Balkans,