1,721,058 research outputs found
An improved switching hybrid recommender system using naive Bayes classifier and collaborative filtering
No full textRecommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. To date a number of recommendation algorithms have been proposed, where collaborative filtering and content-based filtering are the two most famous and adopted recommendation techniques. Collaborative filtering recommender systems recommend items by identifying other users with similar taste and use their opinions for recommendation; whereas content-based recommender systems recommend items based on the content information of the items. These systems suffer from scalability, data sparsity, over specialization, and cold-start problems resulting in poor quality recommendations and reduced coverage. Hybrid recommender systems combine individual systems to avoid certain aforementioned limitations of these systems. In this paper, we proposed a unique switching hybrid recommendation approach by combining a Naive Bayes classification approach with the collaborative filtering. Experimental results on two different data sets, show that the proposed algorithm is scalable and provide better performance – in terms of accuracy and coverage – than other algorithms while at the same time eliminates some recorded problems with the recommender systems
Novel Significance Weighting Schemes for Collaborative Filtering: Generating Improved Recommendations in Sparse Environments
No full textRecommender systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. To date, a number of recommender system algorithms have been proposed, where collaborative filtering is the most famous and adopted recommendation algorithm. Collaborative filtering recommender systems recommend items by identifying other similar users, in case of user-based collaborative filtering, or similar items, in case of item-based collaborative filtering. Significance weighting schemes assign different weights to neighbouring users/items found against an active user/item. Several significance weighting schemes have been proposed [1], [2], [3], [4]. In this paper, we claim that these proposed schemes are flawed by the fact that they can not be applied to general recommender system datasets. We provide the correct generalized significance weighting schemes using different novel heuristics, and by extensive experimental results on three different data sets, show how significance weighting schemes affect the performance of a recommender system. Furthermore, we claim that the conventional weighted sum prediction formula used in item-based [5] collaborative filtering is not correct for very sparse datasets. We provide the correct prediction formula and empirically evaluate it
Novel Heuristics for Coalition Structure Generation in Multi-Agent Systems
No full textA coalition is a set of self-interested agents that agree to cooperate for achieving a set of goals. Coalition formation is an active area of research in multi-agent systems nowadays. Central to this endeavour is the problem of determining which of the many possible coalitions to form in order to achieve some goal, which is called coalition structure generation. Coalition structure generation problem is extremely challenging due to the number of possible solutions that need to be examined, which grows exponentially with the number of agents involved. Generally, agents would enumerate all possible coalitions, store them in memory, and then try to construct the coalition structure that maximizes the sum of the values of the coalitions. However, this is not feasible when we have a large number of agents, and other constraints on execution time, and memory. Hence, there is a need to develop an algorithm that can generate solutions rapidly for large number of agents while providing bounds on the value of solution as well. With this in mind, we propose two new heuristics, namely LocalSearch and GreedySearch, for generating the coalition structure, which satisfy these properties. We empirically show that these heuristics are able to return ‘good-enough’ solutions in very short time. Furthermore, they enhance the performance of state of the art algorithm, IP (proposed by [12]) in terms of increased lower bound, anytime property, and solution quality. Furthermore, we implemented different heuristics for selecting a sub-space in the IP algorithm and show how the time required to find a good-enough solution depends on the selection of a sub-space in the IP algorithm
Privacy-Preserving Profiling
No full textWith the rise of social networking, and other sites which collect vast amounts of user data, the issue of user privacy has never been more important. When creating user profiles care must be taken to avoid collecting sensitive information, while ensuring that these profiles are fit for purpose. In this paper we present a specific instance of the privacy-preserving profiling problem in an expert-finding application. We present a dataset of profiles, as well as several datasets for contaminating these profiles, and provide experiments to test data quality and privacy-preserving performance. We present a simple solution based on training an LSA model on a clean profile corpus, which maintains performance and provides a moderate level of privacy
Building Switching Hybrid Recommender System Using Machine Learning Classifiers and Collaborative Filtering
No full textRecommender systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. To date a number of recommendation algorithms have been proposed, where collaborative filtering and content-based filtering are the two most famous and adopted recommendation techniques. Collaborative filtering recommender systems recommend items by identifying other users with similar taste and use their opinions for recommendation; whereas content-based recommender systems recommend items based on the content information of the items. Moreover, machine learning classifiers can be used for recommendation by training them on content information. These systems suffer from scalability, data sparsity, over specialization, and cold-start problems resulting in poor quality recommendations and reduced coverage. Hybrid recommender systems combine individual systems to avoid certain aforementioned limitations of these systems. In this paper, we proposed unique generalized switching hybrid recommendation algorithms that combine machine learning classifiers with the collaborative filtering recommender systems. Experimental results on two different data sets, show that the proposed algorithms are scalable and provide better performance—in terms of accuracy and coverage—than other algorithms while at the same time eliminate some recorded problems with the recommender systems
Fulfilling the needs of gray-sheep users in recommender systems, a clustering solution
No full textRecommender systems apply data mining techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on graysheep users problem responsible for the increased error rate in collaborative filtering based recommender systems algorithms. The main contribution of this paper lies in showing that (1) the presence of gray-sheep users can affect the performance— accuracy and coverage—of collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) graysheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of clusters can be found empirically; (3) contentbased profile of gray-sheep users can be used for making accurate recommendations. The effectiveness of the proposed algorithm is tested on the MovieLens dataset and community of movie fans in the FilmTrust Website, using mean absolute error, receiver operating characteristic sensitivity, and coverage
Learning the large-scale structure of the max-sat landscape using populations
Full text linkA new algorithm for solving MAX-SAT problems is introduced which clusters good solutions, and restarts the search from the closest feasible solution to the centroid of each cluster. This is shown to be highly efficient for finding good solutions of large MAX-SAT problems. We argue that this success is due to the population learning the large-scale structure of the fitness landscape. Systematic studies of the landscape are presented to support this hypothesis. In addition, a number of other strategies are tested to rule out other possible explanations of the success. Preliminary results are shown indicating that extensions of the proposed algorithm can give similar improvements on other hard optimisation problems
Rethinking Deep Thinking: stable learning of algorithms using Lipschitz Constraints
No full textIterative algorithms solve problems by taking steps until a solution is reached. Models in the form of Deep Thinking (DT) networks have been demonstrated to learn iterative algorithms in a way that can scale to different sized problems at inference time using recurrent computation and convolutions. However, they are often unstable during training, and have no guarantees of convergence/termination at the solution. This paper addresses the problem of instability by analyzing the growth in intermediate representations, allowing us to build models (referred to as Deep Thinking with Lipschitz Constraints (DT-L)) with many fewer parameters and providing more reliable solutions. Additionally our DT-L formulation provides guarantees of convergence of the learned iterative procedure to a unique solution at inference time. We demonstrate DT-L is capable of robustly learning algorithms which extrapolate to harder problems than in the training set. We benchmark on the traveling salesperson problem to evaluate the capabilities of the modified system in an NP-hard problem where DT fails to learn
A Novel Magnetic Update Operator for Quantum Evolutionary Algorithms
No full textQuantum Evolutionary Algorithms (QEA) are novel algorithms proposed for class of combinatorial optimization problems. The probabilistic representation of possible solutions in QEA helps the q-individuals to represent all the search space simultaneously. In QEA, Q-Gate plays the role of update operator and moves q-individuals toward better parts of search space to represent better possible solutions with higher probability. This paper proposes an alternative magnetic update operator for QEA. In the proposed update operator the q-individuals are some magnetic particles attracting each other. The force two particles apply to each other depends on their fitness and their distance. The population has a cellular structure and each q-individual has four neighbors. Each q-individual is attracted by its four binary solution neighbors. The proposed algorithm is tested on Knapsack Problems, Trap problem and fourteen numerical function optimization problems. Experimental results show better performance for the proposed update operator than Q-Gate
Renormalisation of 2D cellular automata with an absorbing state
Full text linkWe describe a real-space renormalisation scheme for non-equilibrium probabilistic cellular automata (PCA) models, and apply it to a two-dimensional binary PCA. An exact renormalisation scheme is rare, and therefore we provide a method for computing the stationary probability distribution of states for such models with which to weight the renormalisation, effectively minimising the error in the scale transformation. While a mean-field approximation is trivial, we use the principle of maximum entropy to incorporate nearest-neighbour spin-correlations in the steady-state probability distribution. In doing so we find the fixed point of the renormalisation is modified by the steady-state approximation order
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