Community detection in networks: A multidisciplinary review

Recommendation system (RS) is a technology that provides accurate recommendations to users However, it is not comprehensive to only consider the accuracy of the recommendation because users have different requirements To improve the comprehensive performance, this paper presents a hybrid recommendation model based on many-objective optimization, which can simultaneously optimize the accuracy, diversity, novelty and coverage of recommendation This model enhances the robustness of recommendations by mixing three different basic recommendation technologies Additionally, we solve it with many-objective evolutionary algorithm (MaOEA) and test it extensively Experimental results demonstrate the effectiveness of the presented model, which can provide the recommendations with more and novel items on the basis of accurate and diverse

A hybrid recommendation system with many-objective evolutionary algorithm

Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm.

A many-objective optimization recommendation algorithm based on knowledge mining

Recommender systems have been widely used in various domains including movies, news, music with an aim to provide the most relevant proposals to users from a variety of available options. Recommender systems are designed using techniques from many fields, some of which are: machine learning, information retrieval, data mining, linear algebra and artificial intelligence. Though in-memory nearest-neighbor computation is a typical approach for collaborative filtering due to its high recommendation accuracy; its performance on scalability is still poor given a huge user and item base and availability of only few ratings (i.e., data sparsity) in archetypal merchandising applications. In order to alleviate scalability and sparsity issues in recommender systems, several model-based approaches were proposed in the past. However, if research in recommender system is to achieve its potential, there is a need to understand the prominent techniques used directly to build recommender systems or for preprocessing recommender datasets, along with its strengths and weaknesses.
In this work, we present an overview of some of the prominent traditional as well as advanced techniques that can effectively handle data dimensionality and data sparsity. The focus of this survey is to present an overview of the applicability of some advanced techniques, particularly clustering, biclustering, matrix factorization, graph-theoretic, and fuzzy techniques in recommender systems. In addition, it highlights the applicability and recent research works done using each technique.

Scalability and sparsity issues in recommender datasets: a survey

Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

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Chonghuan Xu

Papers

A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm