Cold Start Recommendation Algorithm Based on Latent Factor Prediction

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.

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Implementation of English “Online and Offline” Hybrid Teaching Recommendation Platform Based on Reinforcement Learning

Security and Communication Networks ◽

10.1155/2021/4875330 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Danling Dong ◽

Libo Wu

Keyword(s):

Reinforcement Learning ◽

Online Teaching ◽

Value Function ◽

Cold Start ◽

The State ◽

User Evaluation ◽

Function Estimation ◽

Recommendation Algorithm ◽

Target User ◽

Hybrid Teaching

At present, there is a serious disconnect between online teaching and offline teaching in English MOOC large-scale hybrid teaching recommendation platform, which is mainly due to the problems of cold start and matrix sparsity in the recommendation algorithm, and it is difficult to fully tap the user's interest characteristics because it only considers the user's rating but neglects the user's personalized evaluation. In order to solve the above problems, this paper proposes to use reinforcement learning thought and user evaluation factors to realize the online and offline hybrid English teaching recommendation platform. First, the idea of value function estimation in reinforcement learning is introduced, and the difference between user state value functions is used to replace the previous similarity calculation method, thus alleviating the matrix sparsity problem. The learning rate is used to control the convergence speed of the weight vector in the user state value function to alleviate the cold start problem. Second, by adding the learning of the user evaluation vector to the value function estimation of the state value function, the state value function of the user can be estimated approximately and the discrimination degree of the target user can be reflected. Experimental results show that the proposed recommendation algorithm can effectively alleviate the cold start and matrix sparsity problems existing in the current collaborative filtering recommendation algorithm and can dig deep into the characteristics of users' interests and further improve the accuracy of scoring prediction.

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Hybrid attentional memory network for computational drug repositioning

BMC Bioinformatics ◽

10.1186/s12859-020-03898-4 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Jieyue He ◽

Xinxing Yang ◽

Zhuo Gong ◽

lbrahim Zamit

Keyword(s):

Drug Repositioning ◽

Cold Start ◽

New Drugs ◽

Data Sets ◽

Latent Factor ◽

Disease Associations ◽

Memory Network ◽

Predicted Values ◽

New Perspective ◽

Cold Start Problem

Abstract Background Drug repositioning has been an important and efficient method for discovering new uses of known drugs. Researchers have been limited to one certain type of collaborative filtering (CF) models for drug repositioning, like the neighborhood based approaches which are good at mining the local information contained in few strong drug–disease associations, or the latent factor based models which are effectively capture the global information shared by a majority of drug–disease associations. Few researchers have combined these two types of CF models to derive a hybrid model which can offer the advantages of both. Besides, the cold start problem has always been a major challenge in the field of computational drug repositioning, which restricts the inference ability of relevant models. Results Inspired by the memory network, we propose the hybrid attentional memory network (HAMN) model, a deep architecture combining two classes of CF models in a nonlinear manner. First, the memory unit and the attention mechanism are combined to generate a neighborhood contribution representation to capture the local structure of few strong drug–disease associations. Then a variant version of the autoencoder is used to extract the latent factor of drugs and diseases to capture the overall information shared by a majority of drug–disease associations. During this process, ancillary information of drugs and diseases can help alleviate the cold start problem. Finally, in the prediction stage, the neighborhood contribution representation is coupled with the drug latent factor and disease latent factor to produce predicted values. Comprehensive experimental results on two data sets demonstrate that our proposed HAMN model outperforms other comparison models based on the AUC, AUPR and HR indicators. Conclusions Through the performance on two drug repositioning data sets, we believe that the HAMN model proposes a new solution to improve the prediction accuracy of drug–disease associations and give pharmaceutical personnel a new perspective to develop new drugs.

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A music recommendation algorithm based on clustering and latent factor model

MATEC Web of Conferences ◽

10.1051/matecconf/202030903009 ◽

2020 ◽

Vol 309 ◽

pp. 03009

Author(s):

Yingjie Jin ◽

Chunyan Han

Keyword(s):

Collaborative Filtering ◽

Large Scale ◽

User Preference ◽

Implicit Feedback ◽

Music Recommendation ◽

Recommendation Algorithm ◽

Latent Factor ◽

Latent Factor Models ◽

Feedback Data ◽

Preference Matrix

The collaborative filtering recommendation algorithm is a technique for predicting items that a user may be interested in based on user history preferences. In the recommendation process of music data, it is often difficult to score music and the display score data for music is less, resulting in data sparseness. Meanwhile, implicit feedback data is more widely distributed than display score data, and relatively easy to collect, but implicit feedback data training efficiency is relatively low, usually lacking negative feedback. In order to effectively solve the above problems, we propose a music recommendation algorithm combining clustering and latent factor models. First, the user-music play record data is processed to generate a user-music matrix. The data is then analyzed using a latent factor probability model on the resulting matrix to obtain a user preference matrix U and a musical feature matrix V. On this basis, we use two K- means algorithms to perform user clustering and music clustering on two matrices. Finally, for the user preference matrix and the commodity feature matrix that complete the clustering, a user-based collaborative filtering algorithm is used for prediction. The experimental results show that the algorithm can reduce the running cost of large-scale data and improve the recommendation effect.

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The Comparison of Efficiency between the Recommendation Algorithm Based on Multi-Attribute Rating Matrix and the Algorithm Based on UIARM

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.457-458.1544 ◽

2012 ◽

Vol 457-458 ◽

pp. 1544-1549

Author(s):

Hang Yin ◽

Gui Ran Chang ◽

Xing Wei Wang

Keyword(s):

Computation Time ◽

Cold Start ◽

Poor Quality ◽

Recommendation Algorithm ◽

Relevant Item ◽

Item Attribute ◽

Low Efficiency ◽

Item Attributes ◽

New Item ◽

Cold Start Problem

this recommendation algorithm based on User-Item Attribute Rating Matrix (UIARM) can solve the cold-start problem, but the recommended low efficiency, poor quality. The use of Multi-Attribute Rating Matrix (MARM) can solve this problem; it can reduce the computation time and improve the recommendation quality effectively. The user information is analyzed to create their attribute-tables. The user's ratings are mapped to the relevant item attributes and the user's attributes respectively to generate a User Attribute-Item Attribute Rating Matrix. After UAIARM is simplified, MARM will be created. When a new item/user enters into this system, the attributes of new item/user and MARM are matched to find the N users/item with the highest match degrees as the target of the new items or the recommended items. Experiment results validate the cold-start recommendation algorithm based on MARM is efficient.

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