A Two-phase Evolutionary Algorithm for Solving the Accuracy-diversity Dilemma in Recommendation

Aiming at the shortcomings of traditional recommendation algorithms in dealing with large-scale music data, such as low accuracy and poor real-time performance, a personalized recommendation algorithm based on the Spark platform is proposed. The algorithm is based on the Spark platform. The K-means clustering model between users and music is constructed using an AFSA (artificial fish swarm algorithm) to optimize the initial centroids of K-means to improve the clustering effect. Based on the scoring relationship between users and users and users and music attributes, the collaborative filtering algorithm is applied to calculate the correlation between users to achieve accurate recommendations. Finally, the performance of the designed recommendation model is validated by deploying the recommendation model on the Spark platform using the Yahoo Music dataset and online music platform dataset. The experimental results show that the use of improved AFSA can complete the optimization of K-means clustering centroids with good clustering results; combined with the distributed fast computing capability of Spark platform with multiple nodes, the recommendation accuracy has better performance than traditional recommendation algorithms; especially when dealing with large-scale music data, the recommendation accuracy and real-time performance are higher, which meet the current demand of personalized music recommendation.

The neighbor selection that determines which users are exploited to estimate a target user's ratings has an important influence on the accuracy of recommendations of collaborative filtering based recommender system. Two kinds of ways for neighbor selection: KNN and cluster-based, are lack of specificity which refers to selecting different appropriate neighbors for different given target users, and thus limit the accuracy of recommendation. Therefore, in this paper, firstly, we propose a method that employs the evolutionary algorithm to optimize neighbors for all target users. Secondly, overcoming the high time complexity of the first one, we present another approach in which community detection algorithm is utilized as a preprocessing, and then the evolution algorithm is employed to optimize the neighborhood size for every community. We present experiments on a standard benchmark data-set, and the results show that the two methods both realize the specificity in neighbor selection, and accordingly lead to a higher accuracy of recommendations. Besides, the second one makes a good compromise between the specificity and time complexity.

university is a complicated work so in the implementation it have violation of the constraints and it also takes a lot of time since it is created manually. In this paper evolutionary algorithm (EA) is used to create an effective and feasible schedules based on the real data input that is obtained from each department. The objective functions in EA contribute in gaining the fitness function to solve the constraints problem in the schedule by applying weighting for each hard constraints. The objective function is gained from the total of infringement in each soft constraints addition by score weighting. The genetic operator used in EA is stochastic variation Operator. As far as the reproduction operator is concerned, the tournament selection was used with size 3. Crossover operator is conducted after selection process with crossover probability equal to 0.05 and mutation rate is 0.1. The size of population was set to 9 and stopping criteria algorithm was left run for fitness value = 1. The simulation result shows that EA can create lecture schedules efficiently and feasibly. Moreover, it is also faster with the execution time of the proposed EA is less than 30 and easier than creating manually.

Broad learning approach to Surrogate-Assisted Multi-Objective evolutionary fuzzy clustering algorithm based on reference points for color image segmentation

This article investigates the impact of rating data characteristics on the performance of several popular recommendation algorithms, including user-based and item-based collaborative filtering, as well as matrix factorization. We focus on three groups of data characteristics: rating space, rating frequency distribution, and rating value distribution. A sampling procedure was employed to obtain different rating data subsamples with varying characteristics; recommendation algorithms were used to estimate the predictive accuracy for each sample; and linear regression-based models were used to uncover the relationships between data characteristics and recommendation accuracy. Experimental results on multiple rating datasets show the consistent and significant effects of several data characteristics on recommendation accuracy.

AI-driven recommendation algorithms are a signature feature of video streaming services and ideally contribute subscribers’ utility. However, that is not always the case. Given the default nature of recommendation algorithms, this study investigated the antecedents of reliance on recommendation algorithms by integrating the heuristic-systematic model with motivational orientations. A survey of 497 U.S. Netflix users revealed that the perceived accuracy and diversity of algorithmic recommendations, trust in AI, and attachment to the streaming platform increase algorithm reliance. Further, the perceived accuracy of recommendation algorithms has a stronger effect on algorithm reliance for those who have a greater attachment to the platform.

Evolutionary algorithms are randomized search heuristics that are often described as robust general purpose problem solvers. It is known, however, that the performance of an evolutionary algorithm may be very sensitive to the setting of some of its parameters. A different perspective is to investigate changes in the expected optimization time due to small changes in the fitness landscape. A class of fitness functions where the expected optimization time of the (1+1) evolutionary algorithm is of the same magnitude for almost all of its members is the set of linear fitness functions. Using linear functions as a starting point, a model of a fitness landscape is devised that incorporates important properties of linear functions. Unexpectedly, the expected optimization time of the (1+1) evolutionary algorithm is clearly larger for this fitness model than on linear functions.

Objectives: In order to further improve the mental health of college counselors, a recommendation algorithm is proposed based on social psychology to study the mental health status and influencing factors of college counselors. Methods: This algorithm analyzes the influencing factors of the user’s mental health by calculating the feature vector on the basis of user’s preference and similarity for the social environment resource. The matrix vector of resource similarity is constructed by the calculated cosine similarity, and the context modeling hierarchical model is constructed. Results: The user’s preference matrix for resources is calculated, so that a personalized recommendation algorithm based on label and collaborative filtering is proposed. Finally, the algorithm and model proposed are validated. Experiments show that when the value of N is 10 or 15, the recommendation algorithm by label can improve the recommendation accuracy and recall rate, which indicates that the proposed algorithm can significantly improve the quality of recommendation results. Conclusion: Therefore, the algorithm proposed can effectively improve the recommendation quality of college counselors’ choice of colleges to alleviate psychological stress.

As a representative of the new generation of information technology, reinforcement learning technology has received more and more attention, but no scholars have yet applied reinforcement learning recommendation technology to ideological and political education. In this paper, a diversity optimization recommendation algorithm for ideological and political education is constructed, and the vector representation model of the state is designed and optimized based on the Markov decision process. Through the actor-critic algorithm, to calculate the probability of recommendation sampling to generate a recommendation list and then establish a decision model in order to improve the ideological and political education recommendation algorithm on the user’s feedback of accurate judgement and recommendation decision-making effect. The experimental results show that the Diversity Opt performs well in terms of prediction error. The algorithm is ahead of the comparison algorithm by an average of 9.35% and 8.3%, respectively, and all other performances are also significantly better than the comparison algorithm. The average score of students’ performance in the experimental class applying the algorithm is 14.4 points higher than that of the control class. It shows that the recommendation algorithm helps to accurately recommend ideological and political education and exhibits beneficial practical effects.

Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function evaluations. Use of a population based iterative technique like evolutionary algorithms in such problem domains is thus practically prohibitive. An attractive alternative is to build surrogates or use an approximation of the actual fitness functions to be evaluated. Many regression and interpolation tools are available to build such meta models. This paper briefly discusses the architectures and use of such meta-modeling tools in an evolutionary optimization context. We further present an evolutionary algorithm framework which involves use of surrogate models for fitness function evaluation. The original framework namely, the dynamic approximate fitness based hybrid EA (DAFHEA) model [14] reduces computation time by controlled use of meta-models (in this case approximate model generated by support vector machine regression) to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the meta-model are generated from a single uniform model. This does not take into account uncertain scenarios involving noisy fitness functions. The enhanced model, DAFHEA-II, incorporates a multiple-model based learning approach for the support vector machine approximator to counter effects of noise [15]. Empirical results obtained by evaluating the frameworks using several benchmark functions (both non- noisy and noisy versions) are presented.

To enhance students’ English writing ability, English creative writing has become a common course offered by colleges and universities. English reference is an indispensable tool for creative English writing. How to choose appropriate English references is an important guarantee to complete good work. Therefore, this paper proposes a Deep Semantic Mining-based Recommendation (DSMR) algorithm for English writing reference selection and recommendation to assist the completion of high-quality English creative writing works. The model can extract user features and document attributes more accurately by deeply mining semantic information of literature content and user needs, so as to achieve more accurate recommendations. First, the Bidirectional Encoder Representation from Transformers (BERT) pretraining model is adopted to process literature content and user requirement documents. Through in-depth mining of user characteristics and literature attributes, the problems of data sparsity and cold start of items are effectively alleviated. Then, the forward long short-term memory (LSTM) network was used to focus on the changes in user preferences over time, resulting in more accurate recommendations. The experimental results show that the use of heterogeneous information can significantly improve the recommendation performance, and the additional use of user attribute information can also improve the recommendation performance. Compared with other benchmark models, the recommendation quality of this model is greatly improved.

A new approach is presented to handle constraints optimization using evolutionary algorithms in this paper. First, we present a specific varying fitness function technique, this technique incorporates the problem's constraints into the fitness function in a dynamic way. The resulting varying fitness function facilitates the EA search. On one hand, The new fitness function without any parameters can properly evaluate not only feasible solution, but also infeasible one, on other hand, the information of the best solution in the current population is also concerned in fitness function, which make search more efficient. Meanwhile, a new crossover operator based on simplex crossover operator and a new PSO mutation operator is also proposed, and both the operators utilize the information of good individuals in the current populations so they can produce high quality offspring. Based on these, a new evolutionary algorithm for constrained optimization problems is proposed. The simulations are made on five widely used benchmark problems, and the results indicate the proposed algorithm is effective.

Recommender system has received tremendous attention and has been studied by scholars in recent years due to its wide applications in different domains. With the in‐depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. The success of modern recommender system mainly depends on the understanding and application of the context of recommendation requests. However, when leveraging deep learning algorithms for recommendation, the impact of context information such as recommendation time and location is often neglected. In this paper, a time‐aware convolutional neural network‐ (CNN‐) based personalized recommender system TC-PR is proposed. TC-PR actively recommends items that meet users’ interests by analyzing users’ features, items’ features, and users’ ratings, as well as users’ time context. Moreover, we use Tensorflow distributed open source framework to implement the proposed time‐aware CNN‐based recommendation algorithm which can effectively solve the problems of large data volume, large model, and slow speed of recommender system. The experimental results on the MovieLens‐1m real dataset show that the proposed TC-PR can effectively solve the cold‐start problem and greatly improve the speed of data processing and the accuracy of recommendation.

For the evolutionary algorithm, the representation of the electronic circuit has two methods, one kind is code with the electronic circuit solution space, the other is code with the problem space. How one representation quality weighs may think the following questions? The first is the code method should as far as possible complete, it is say for the significance solution circuit or the optimize solution obtains in the problem space may represented by this code method. The second is the code method should speeds up the convergence speed of the algorithm search. The hardware representation methods mainly include binary bit string representation, tree representation, Cartesian Genetic Programming representation and other representations. In this paper, we introduce the representations of the binary bit string and Cartesian Genetic Programming in detail, and based on the two representations we design the evolutionary algorithm for electronic circuits optimization. Then we introduce the encoding of the circuit as a chromosome, the genetic operators and the fitness function of our algorithm. For the case studies showed this algorithm has proved to be efficient, and the experiment results showed that we have gained better results.

Traditionally, it is often assumed that data sparsity is a big problem of user-based collaborative filtering algorithm. However, the analysis is based only on data quantity without considering data quality, which is an important characteristic of data, sparse high quality data may be good for the algorithm, thus, the analysis is one-sided. In this paper, the effects of training ratings with different levels of sparsity on recommendation quality are first investigated on a real world dataset. Preliminary experimental results show that data sparsity can have positive effects on both recommendation accuracy and coverage. Next, the measurement of data noise is introduced. Then, taking data noise into consideration, the effects of data sparsity on the recommendation quality of the algorithm are re-evaluated. Experimental results show that if sparsity implies high data quality (low noise), then sparsity is good for both recommendation accuracy and coverage. This result has shown that the traditional analysis about the effect of data sparsity is one-sided, and has the implication that recommendation quality can be improved substantially by choosing high quality data.