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A Greedy-based Algorithm in Optimizing Student's Recommended Timetable Generator with Semester Planner

Khyrina Airin Fariza Abu Samah, Siti Qamalia Thusree, Ahmad Firdaus Ahmad Fadzil, Lala Septem Riza, Shafaf Ibrahim, Noraini Hasan
2022 International Journal of Advanced Computer Science and Applications  
We calculate the priority task sequence to achieve the best optimal solution. The greedy algorithm can solve the optimization problem with the best optimal solution for each situation.  ...  Hence, this research aims to optimize the recommended semester planner, Timetable Generator using a greedy algorithm to increase student productivity.  ...  CONCLUSION We presented a recommended semester planner using the optimization technique greedy optimization.  ... 
doi:10.14569/ijacsa.2022.0130146 fatcat:vwyso6qqefbhbpayivj5tia2fq

Revisiting revisits in trajectory recommendation [article]

Aditya Krishna Menon, Dawei Chen, Lexing Xie, Cheng Soon Ong
2017 arXiv   pre-print
Trajectory recommendation is the problem of recommending a sequence of places in a city for a tourist to visit.  ...  Overall, our results indicate that a greedy graph-based heuristic offer excellent performance and runtime, leading us to recommend its use for removing loops at prediction time.  ...  More broadly, investigation of efficient means of ensuring global cohesion -e.g. preventing homogeneous results -is an important direction for the advancement of citizen-centric recommendation. ) ILP  ... 
arXiv:1708.05165v1 fatcat:mt72pvivefhateqoeeaq35hcx4

A Greedy Approach for Budgeted Maximum Inner Product Search [article]

Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S. Dhillon
2016 arXiv   pre-print
Maximum Inner Product Search (MIPS) is an important task in many machine learning applications such as the prediction phase of a low-rank matrix factorization model for a recommender system.  ...  By carefully studying the problem structure of MIPS, we develop a novel Greedy-MIPS algorithm, which can handle budgeted MIPS by design.  ...  As a result, the development of efficient algorithms for MIPS is needed in large-scale recommender systems.  ... 
arXiv:1610.03317v1 fatcat:sagzruvbubhvnpguqk7kgsrhva

Automatic physical design tuning

Sanjay Agrawal, Eric Chu, Vivek Narasayya
2006 Proceedings of the 2006 ACM SIGMOD international conference on Management of data - SIGMOD '06  
We present scenarios where exploiting sequence information in the workload is crucial for performance tuning.  ...  We also propose techniques for addressing the technical challenges arising from treating the workload as a sequence.  ...  OPTIMAL ALGORITHM In this section, we describe an algorithm to generate an optimal solution to the physical design problem for workload sequences (defined in Section 2.2).  ... 
doi:10.1145/1142473.1142549 dblp:conf/sigmod/AgrawalCN06 fatcat:oj7c4qcv7bhota6ibdz7aq7gva

Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity [article]

Laming Chen, Guoxin Zhang, Hanning Zhou
2018 arXiv   pre-print
However, the maximum a posteriori (MAP) inference for DPP which plays an important role in many applications is NP-hard, and even the popular greedy algorithm can still be too computationally expensive  ...  To overcome the computational challenge, in this paper, we propose a novel algorithm to greatly accelerate the greedy MAP inference for DPP.  ...  For example, when recommending a long sequence of items to the user, each time only a small portion of the sequence catches the user's attention.  ... 
arXiv:1709.05135v2 fatcat:lca5thenjrgznh2zyp3ndguo5y

Optimizing exact genetic linkage computations

Maáyan Fishelson, Dan Geiger
2003 Proceedings of the seventh annual international conference on Computational molecular biology - RECOMB '03  
In this paper we present the use of stochastic greedy algorithms for optimizing this order.  ...  Consequently, computing the likelihood of data, which is needed for learning linkage parameters, using exact inference procedures calls for an extremely efficient implementation that carefully optimizes  ...  We also thank Natalia Graiz and Marina Shteinberg for implementing an earlier version of our stochastic algorithm and running initial experiments.  ... 
doi:10.1145/640075.640089 dblp:conf/recomb/FishelsonG03 fatcat:hyprlf3ky5g6bd5av66tqft7pe

Optimizing Exact Genetic Linkage Computations

Maayan Fishelson, Dan Geiger
2004 Journal of Computational Biology  
In this paper we present the use of stochastic greedy algorithms for optimizing this order.  ...  Consequently, computing the likelihood of data, which is needed for learning linkage parameters, using exact inference procedures calls for an extremely efficient implementation that carefully optimizes  ...  We also thank Natalia Graiz and Marina Shteinberg for implementing an earlier version of our stochastic algorithm and running initial experiments.  ... 
doi:10.1089/1066527041410409 pmid:15285892 fatcat:ifq2nnw3ubfndbt4tz6jjgts5y

Contextual Exploration Using a Linear Approximation Method Based on Satisficing [article]

Akane Minami, Yu Kono, Tatsuji Takahashi
2021 arXiv   pre-print
The generalization of RS provides an algorithm to reduce the volume of exploratory actions by adopting a different approach from existing optimization algorithms.  ...  Thus, we propose Linear RS (LinRS), which is a type of satisficing algorithm and a linear extension of risk-sensitive satisficing (RS), for application to a wider range of tasks.  ...  For example, when humans take an exam, they would be able to study more efficiently if they are informed of the cutoff point in advance.  ... 
arXiv:2112.06452v1 fatcat:nt6mnjckqzdh5gcnjjdsdyhlma

Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences [article]

Chao Qian, Dan-Xuan Liu, Chao Feng, Ke Tang
2021 arXiv   pre-print
Note that these best-known approximation guarantees can be obtained only by different greedy-style algorithms before.  ...  We prove that for each kind of previously studied monotone submodular objective functions over sequences, i.e., prefix monotone submodular functions, weakly monotone and strongly submodular functions,  ...  (denoted as G-Greedy) and the greedy algorithm for the application of recommender systems with n ∈ {100, 200, . . . , 1000} and k = 20.  ... 
arXiv:2104.09884v1 fatcat:irt5exdkgbdtnkzjkqfkj2l45q

The Order of Things: Position-Aware Network-friendly Recommendations in Long Viewing Sessions [article]

Theodoros Giannakas, Thrasyvoulos Spyropoulos, Pavlos Sermpezis
2019 arXiv   pre-print
Caching offers an opportunity for a win-win scenario: nearby content can improve the video streaming experience for the user, and free up valuable network resources for the operator.  ...  (ii) can the resulting optimization problems be solved efficiently, when considering both sequences of dependent accesses (e.g., YouTube) and position preference?  ...  Due to this relaxation, the greedy algorithm is an upper bound for [7] , looking at the recommendation problem only. V. VALIDATION RESULTS A.  ... 
arXiv:1905.04947v1 fatcat:hlpcfglmrfd5nfkgr2fvqgtgzi

Compact Representation of GPS Trajectories over Vectorial Road Networks [chapter]

Ranit Gotsman, Yaron Kanza
2013 Lecture Notes in Computer Science  
The first method represents the given route as a sequence of greedy paths. We provide two algorithms to generate a greedy-path code for a sequence of n vertices on the map.  ...  Decoding a greedy-path code can be done in O(n) time. The second method codes a route as a sequence of shortest paths.  ...  We present two approaches to represent a route compactly-as a sequence of greedy paths or as a sequence of shortest paths. We provide two algorithms for computing the sequence of greedy paths.  ... 
doi:10.1007/978-3-642-40235-7_14 fatcat:k6tpn5fp5zarpbvzioyd4utdg4

Beyond Query: Interactive User Intention Understanding

Yang Yang, Jie Tang
2015 2015 IEEE International Conference on Data Mining  
In order to generate "smart" questions in an optimal sequence, we propose the IHS algorithm based on heuristic search.  ...  We prove an error bound for the proposed algorithm on the ranking of target items given the questions and answers.  ...  We thank Chengtao Li and Xun Zhen for valuable discussions and suggestions. Chengtao also contributed to the theoretical analysis of the proposed algorithm.  ... 
doi:10.1109/icdm.2015.113 dblp:conf/icdm/YangT15 fatcat:gpaheqk2jjah5kbq6o3y7pxwhy

GALO:A New Intelligent Task Scheduling Algorithm in Cloud Computing Environment

Omer K. Jasim Mohammad
2018 International Journal of Engineering & Technology  
This paper proposed a cloud computing task scheduling algorithm based on greedy algorithm and Antlion Optimizer algorithm.  ...  This paper suggested the objective share-search function of the make-span and costs of the tasks in order to improve the initialization of the pheromone, the greedy algorithm and the pheromone update method  ...  Acknowledgement I acknowledge university of Fallujah for support me to complete this scientific work and thanks to my colleagues in the computer center.  ... 
doi:10.14419/ijet.v7i4.16486 fatcat:cozbpdo3grfxbnrhnhwkwow32y

Threshold-Bounded Influence Dominating Sets for Recommendations in Social Networks

Magdalini Eirinaki, Nuno Moniz, Katerina Potika
2016 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom)  
The difference of the proposed framework with most social recommender systems is that we need to generate recommendations including more than one item and in the absence of explicit ratings, solely relying  ...  under which conditions and with what cost we can form neighborhoods of influence within a social network, in order to assist individuals with little or no prior genuine information through a two-phase recommendation  ...  Consecutive work is focused on proposing optimizations of the greedy algorithm for better efficiency, see [18] , [1] .  ... 
doi:10.1109/bdcloud-socialcom-sustaincom.2016.67 dblp:conf/bdcloud/EirinakiMP16 fatcat:r6gxnqhhvjhhzmnxyrqvyddwo4

Feedback Adaptive Learning for Medical and Educational Application Recommendation

Cem Tekin, Sepehr Elahi, Mihaela Van Der Schaar
2020 IEEE Transactions on Services Computing  
Since computing the optimal recommendation sequence is intractable, as a benchmark, we define an oracle that sequentially recommends apps to maximize the expected immediate gain.  ...  with episodic versions of ✏n-greedy, Thompson sampling, and collaborative filtering methods.  ...  ACKNOWLEDGMENTS The authors would like to thank Ken Cheung and Xinyu Hu of Columbia University for providing us with IntelliCare data that was used in the simulations.  ... 
doi:10.1109/tsc.2020.3037224 fatcat:53memzm4uzhfreidmyc3whw4am
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