A multi-valued and sequential-labeled decision tree method for recommending sequential patterns in cold-start situations

Hsu, Chang-Ling

doi:10.1007/s10489-020-01806-0

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…In addition to the data sparsity problem, the cold start problem is also a major challenge for recommendation models [ 42 ]. The cold start problem refers to giving personalized recommendations without historical user-app data.…”

Section: Methodsmentioning

confidence: 99%

IMGC-GNN: A multi-granularity coupled graph neural network recommendation method based on implicit relationships

et al. 2022

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In the application recommendation field, collaborative filtering (CF) method is often considered to be one of the most effective methods. As the basis of CF-based recommendation methods, representation learning needs to learn two types of factors: attribute factors revealed by independent individuals (e.g., user attributes, application types) and interaction factors contained in collaborative signals (e.g., interactions influenced by others). However, existing CF-based methods fail to learn these two factors separately; therefore, it is difficult to understand the deeper motivation behind user behaviors, resulting in suboptimal performance. From this point of view, we propose a multi-granularity coupled graph neural network recommendation method based on implicit relationships (IMGC-GNN). Specifically, we introduce contextual information (time and space) into user-application interactions and construct a three-layer coupled graph. Then, the graph neural network approach is used to learn the attribute and interaction factors separately. For attribute representation learning, we decompose the coupled graph into three homogeneous graphs with users, applications, and contexts as nodes. Next, we use multilayer aggregation operations to learn features between users, between contexts, and between applications. For interaction representation learning, we construct a homogeneous graph with user-context-application interactions as nodes. Next, we use node similarity and structural similarity to learn the deep interaction features. Finally, according to the learned representations, IMGC-GNN makes accurate application recommendations to users in different contexts. To verify the validity of the proposed method, we conduct experiments on real-world interaction data from three cities and compare our model with seven baseline methods. The experimental results show that our method has the best performance in the top- k recommendation.

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Section: Methodsmentioning

confidence: 99%

IMGC-GNN: A multi-granularity coupled graph neural network recommendation method based on implicit relationships

et al. 2022

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“…According to the test attribute value using a recursive algorithm, get a preliminary decision tree. C4.5 algorithmrelated computation formula as follows [30]. First, the expected value required for sample classification is given as follows:…”

Section: Establishment Of the Modelmentioning

confidence: 99%

Application of C4.5 Decision Tree Algorithm for Evaluating the College Music Education

Wang

2022

Mobile Information Systems

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Music courses in colleges and universities have undergone significant changes as the new curriculum reform has proceeded. As a result, student evaluations in the classroom are changing, and a diversified evaluation paradigm is gradually developing. Numerous new and more effective teaching concepts and teaching methods have been developed for revitalizing the state with science and education. This interrupts the standard instructional activities’ backward teaching pattern. Online teaching has become more significant in the area of education as technology, science, and Internet technologies have advanced. Music instructors at universities and colleges are continually updating their teaching methods and utilize several techniques to provide in-depth instruction in the classrooms. To expand students’ enthusiasm and involvement while also developing their musical creative talents, a web-based information educational administrations management system has been widely used in many universities and colleges. This study utilizes the C4.5 algorithm to create a decision tree model for establishing an evaluation system of classroom teaching to enhance the quality. The proposed algorithm evaluates the model’s accuracy and practicability using performance information from 125 teachers’ music classroom teaching. Finally, it identifies the decision-making attributes that affect the teachers’ evaluation. The quality of classroom teaching is evaluated, and some useful suggestions are provided based on the experimental results, which can support college and university decision-making by motivating teachers to improve their classroom teaching quality.

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“…Apriori algorithm is one of the most classic association rule mining algorithms, which was proposed by American scholar R. Agrawal. e Apriori algorithm consists of two main steps, the first step is to obtain the set of frequent items from the transaction records; the second step is to obtain the association rules based on the set of frequent items [5]. e Apriori algorithm is not without drawbacks, it has multiple iterations of recording things, resulting in I/O that cannot be done quickly, and can create multiple candidates sets as well as the problem of too frequent itemsets, which can make this algorithm less useful.…”

Section: Related Workmentioning

confidence: 99%

Personalized Hybrid Recommendation for Tourist Users Based on Matrix Cluster Apriori Mining Algorithm

Zhang

2022

Mathematical Problems in Engineering

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With the rapid development of Internet technology and the arrival of the era of big data, the rapid expansion of network information resources has formed massive information. Massive information resources have brought great convenience to people’s lives. However, it becomes more and more difficult to find the content that interests you, which is the phenomenon of “information overload.” In order to solve this problem, a solution based on personalized recommendation technology is proposed. In personalized recommendation technology, collaborative filtering algorithm is the most widely used technology. Clustering technology can effectively divide objects into groups, so that the similarity of attributes between objects in the same group is high, and the similarity of objects in different groups is low. The core step of the filtering recommendation algorithm is to find the similar neighbors of the target user by calculating the similarity. Applying the clustering technology to the recommendation can effectively improve the performance of the recommendation system. Aiming at the real-time problem of collaborative filtering recommendation, this paper introduces a method of firstly clustering users on the user item rating matrix, and finding the nearest neighbors in the clusters with high similarity with the target user, which effectively reduces the query space and improves the recommendation. This paper proposes a method to measure the user’s preference for item attributes, which is used in the above clustering process to improve the recommendation accuracy while retaining the advantage of reducing the query space. Aiming at the problem of poor recommendation accuracy, this paper proposes a fuzzy-improved K-means algorithm to cluster items in the product attribute matrix, and then fuses the similarity of the belongingness of items to clusters in the fuzzy clustering. The similarity calculated on the score matrix shows that this method is better than the traditional hybrid recommendation in accuracy.

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A multi-valued and sequential-labeled decision tree method for recommending sequential patterns in cold-start situations

Cited by 12 publications

References 26 publications

IMGC-GNN: A multi-granularity coupled graph neural network recommendation method based on implicit relationships

IMGC-GNN: A multi-granularity coupled graph neural network recommendation method based on implicit relationships

Application of C4.5 Decision Tree Algorithm for Evaluating the College Music Education

Personalized Hybrid Recommendation for Tourist Users Based on Matrix Cluster Apriori Mining Algorithm

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