The MAE value of the conventional resource collaborative filtering recommendation algorithm is high, so a collaborative filtering recommendation algorithm for Human Resource Management MOC resources based on the Spark...
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The amount of information on the Internet is too large and the information is overloaded, which cannot meet the growing personalized needs of people. The recommendation system is deeply applied in real life, changing ...
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collaborative filtering recommendation algorithm is the most widely used recommendationalgorithm in recommendation systems. Based on the collaborative filtering recommendation algorithm, a time-based user clustering ...
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ISBN:
(纸本)9781728158556
collaborative filtering recommendation algorithm is the most widely used recommendationalgorithm in recommendation systems. Based on the collaborative filtering recommendation algorithm, a time-based user clustering collaborative filtering recommendation algorithm is proposed. In this novel algorithm, all users and their interest changes are considered. There are three mainly improved steps in this novel algorithm. Firstly, the user rating time is added to increase the similarity between users when users are clustered. Secondly, in order to reduce the search space of similar users, only the nearest neighbor with a high similarity to the target user can be found in the cluster by adding the rating time. Finally, the traditional collaborative filtering recommendation algorithm is used to recommend the target users in the clustered data set. By using the MovieLens100K dataset, some experiments show the validity of the algorithm.
With the development of the Internet and mobile Internet, live streaming e-commerce has become an emerging e-commerce force. However, traditional recommendationalgorithms have shortcomings in terms of accuracy and pe...
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With the development of the Internet and mobile Internet, live streaming e-commerce has become an emerging e-commerce force. However, traditional recommendationalgorithms have shortcomings in terms of accuracy and personalization of recommendation results, and more intelligent and personalized recommendationalgorithms need to be applied. This article aimed to achieve personalized product recommendations and enhance the shopping experience of users by analyzing their historical behavioral data, real-time interests and needs, combined with big data and artificial intelligence technology. The collaborative filtering recommendation algorithm based on live streaming had an average recommendation accuracy of over 80% for user groups 1, 2, and 3. The research results of this article had important practical significance for promoting the healthy development of live streaming e-commerce platforms, improving user experience, and enhancing platform competitiveness.
In recent years, the arrival of big data era has brought new opportunities and challenges to collaborativefilteringrecommendation system. Introducing trust into traditional collaborativefilteringalgorithm, a colla...
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In recent years, the arrival of big data era has brought new opportunities and challenges to collaborativefilteringrecommendation system. Introducing trust into traditional collaborativefilteringalgorithm, a collaborative filtering recommendation algorithm based on improved trust is proposed. However, there is still room for research and improvement on how to expand the limited social relations and how to reveal the impact of user interaction on user characteristics. Hadoop, as an open source metaphysical computing platform, realizes the function of cloud computing, which is widely used by researchers. Therefore, the author studies the collaborative filtering recommendation algorithm based on trust relationship in big data. In order to solve the data sparseness problem commonly associated with the collaborative filtering recommendation algorithm, the trust relationship is combined with the traditional collaborativefilteringalgorithm. Through the transferability of trust relationship, the relationship between trust degree and similarity is used to improve the data sparsity problem, and a collaborative filtering recommendation algorithm based on trust model is formed. However, the algorithm still needs to be improved in terms of time performance. The next step is to combine the clustering algorithm to make the recommendationalgorithm further improve the time performance.
collaborative filtering recommendation algorithm is the most widely used recommendationalgorithm in recommendation systems. Based on the collaborative filtering recommendation algorithm, a time-based user clustering ...
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collaborative filtering recommendation algorithm is the most widely used recommendationalgorithm in recommendation systems. Based on the collaborative filtering recommendation algorithm, a time-based user clustering collaborative filtering recommendation algorithm is proposed. In this novel algorithm, all users and their interest changes are considered. There are three mainly improved steps in this novel algorithm. Firstly, the user rating time is added to increase the similarity between users when users are clustered. Secondly, in order to reduce the search space of similar users, only the nearest neighbor with a high similarity to the target user can be found in the cluster by adding the rating time. Finally, the traditional collaborative filtering recommendation algorithm is used to recommend the target users in the clustered data set. By using the MovieLens100 K dataset, some experiments show the validity of the algorithm.
With the increase of volume, velocity, and variety of big data, the traditional collaborative filtering recommendation algorithm, which recommends the items based on the ratings from those like-minded users, becomes m...
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ISBN:
(纸本)9783319897431;9783319897424
With the increase of volume, velocity, and variety of big data, the traditional collaborative filtering recommendation algorithm, which recommends the items based on the ratings from those like-minded users, becomes more and more inefficient. In this paper, two varieties of algorithms for collaborativefilteringrecommendation system are proposed. The first one uses the improved k-means clustering technique while the second one uses the improved k-means clustering technique coupled with Principal Component Analysis as a dimensionality reduction method to enhance the recommendation accuracy for big data. The experimental results show that the proposed algorithms have better recommendation performance than the traditional collaborative filtering recommendation algorithm.
With the advent and explosive growth of the Web over the past decade, recommender systems have become at the heart of the business strategies of e-commerce and Internet-based companies such as Google, YouTube, Faceboo...
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ISBN:
(纸本)9781538643662
With the advent and explosive growth of the Web over the past decade, recommender systems have become at the heart of the business strategies of e-commerce and Internet-based companies such as Google, YouTube, Facebook, Netflix, LinkedIn, Amazon, etc. Hence, the collaborative filtering recommendation algorithms are highly valuable and play a vital role at the success of such businesses in reaching out to new users and promoting their services and products. With the aim of improving the recommendation performance of such an algorithm, this paper proposes a new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. The k-means algorithm and Singular Value Decomposition (SVD) are both used to cluster similar users and reduce the dimensionality. It proposes and evaluates an effective two stage recommender system that can generate accurate and highly efficient recommendations. The experimental results show that this new method significantly improves the performance of the recommendation systems .
The disadvantage of the traditional CFAbMD algorithm is no consideration of impact of local users' neighbor on item rating. Aiming at this problem, a new CFAbMD algorithm is proposed considering both ALS matrix fa...
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ISBN:
(纸本)9789811026638;9789811026621
The disadvantage of the traditional CFAbMD algorithm is no consideration of impact of local users' neighbor on item rating. Aiming at this problem, a new CFAbMD algorithm is proposed considering both ALS matrix factorization and user nearest neighbor (CFAbMD-UNN), which integrates the similarity information among users into the matrix factorization of model. Furthermore, the CFAbMD-UNN algorithm was implemented in parallel on Spark. Experiments on Movielens shows that the propsosed CFAbMD-UNN algorithm outperforms the traditional CFAbMD algorithm.
The rapid development of Internet information technology makes the problem of information overload become more and more serious, and recommendation system is one of the effective ways to solve this problem which is fa...
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ISBN:
(纸本)9781510845008
The rapid development of Internet information technology makes the problem of information overload become more and more serious, and recommendation system is one of the effective ways to solve this problem which is favored by people. However, for the massive data information, the recommended algorithm faces the bottleneck problem of processing speed and computing resources, so this paper proposed a parallel collaborative filtering recommendation algorithm based on Spark. The RLPSO algorithm is used to optimize the clustering factor of the K-means clustering algorithm by associating users with similar interests into a cluster and using the recommended algorithm for users to recommend is implemented on the Spark platform. The experimental results show that the improved algorithm has a significant improvement in the prediction accuracy, and has a higher speedup and stability compared with the traditional collaborative filtering recommendation algorithm.
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