Representation learning with collaborative autoencoder for personalized recommendation

Recommendation algorithm is not only widely used in entertainment media, but also plays an important role in national strategy, such as the recommendation algorithm of byte beating company. This paper studies the personalized recommendation algorithm based on representation learning. The data in social network is complex, and the data mainly exists in various platforms. This paper introduces AI (Artificial Intelligence) algorithm to guide the algorithm of representation learning, and integrates the algorithm steps of representation learning, to realize the implementation of personalized recommendation algorithm in social network, and compares the representation learning algorithm. Finally, this paper designs a method based on heat conduction and text mining to provide users with webpage recommendations and help users better mine interesting popular webpages. Research shows that the performance of IMF is better than that of PMF because it overcomes the sparsity of data by pre-filling. The accuracy of IMF is 3.69% higher than that of PMF on the epinions data set, and 6.24% higher than that of PMF on the double data set. Rtcf, socialmf, tcars, CSIT, isrec, and hesmf have better performance than PMF and IMF. Among them, rtcf, socialmf, tcars, CSIT, isrec, and hesmf improve the MAE performance of PMF by 7.6%, 6.3%, 8.8%, 7.9%, 9.5% and 14.2%, respectively.

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Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

Algorithms ◽

10.3390/a11090137 ◽

2018 ◽

Vol 11 (9) ◽

pp. 137 ◽

Cited By ~ 32

Author(s):

Qingyao Ai ◽

Vahid Azizi ◽

Xu Chen ◽

Yongfeng Zhang

Keyword(s):

Knowledge Base ◽

Collaborative Filtering ◽

Large Scale ◽

Explicit Knowledge ◽

State Of The Art ◽

Representation Learning ◽

Knowledge Bases ◽

Personalized Recommendation ◽

Structured Knowledge ◽

Explainable Recommendation

Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms—especially the collaborative filtering (CF)- based approaches with shallow or deep models—usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the availability of vast amounts of data and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users’ historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines.

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Personalized recommendation algorithm research based on social network analysis

Future Computer and Information Technology ◽

10.2495/icfcit130591 ◽

2013 ◽

Author(s):

Jian Zhang ◽

Xiaoli Xu

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Personalized Recommendation ◽

Recommendation Algorithm

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Speech Representation Learning for Emotion Recognition Using End-to-End ASR with Factorized Adaptation

10.21437/interspeech.2020-2524 ◽

2020 ◽

Author(s):

Sung-Lin Yeh ◽

Yun-Shao Lin ◽

Chi-Chun Lee

Keyword(s):

Emotion Recognition ◽

Representation Learning ◽

Speech Representation ◽

End To End

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Transfer-Representation Learning for Detecting Spoofing Attacks with Converted and Synthesized Speech in Automatic Speaker Verification System

10.21437/interspeech.2019-2014 ◽

2019 ◽

Cited By ~ 2

Author(s):

Su-Yu Chang ◽

Kai-Cheng Wu ◽

Chia-Ping Chen

Keyword(s):

Speaker Verification ◽

Representation Learning ◽

Verification System ◽

Synthesized Speech

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A Drug Target Interaction Prediction Based on LINE-RF Learning

Current Bioinformatics ◽

10.2174/1574893615666191227092453 ◽

2020 ◽

Vol 15 (7) ◽

pp. 750-757

Author(s):

Jihong Wang ◽

Yue Shi ◽

Xiaodan Wang ◽

Huiyou Chang

Keyword(s):

Network Topology ◽

Drug Target ◽

Large Scale ◽

Representation Learning ◽

New Drugs ◽

Combination Method ◽

Learning Methods ◽

Network Representation ◽

On Line ◽

Clinical Experiments

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments. Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development. Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs. Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016. Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

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Digging for the truth: the case for active annotation in evaluating the credibility of online medical information (Preprint)

10.2196/preprints.25920 ◽

2020 ◽

Author(s):

Mikołaj Morzy ◽

Bartłomiej Balcerzak ◽

Adam Wierzbicki ◽

Adam Wierzbicki

Keyword(s):

Machine Learning ◽

Medical Information ◽

Representation Learning ◽

Training Dataset ◽

Highly Qualified ◽

Human In The Loop ◽

Annotation Process ◽

Comprehensive Framework ◽

Online Sources ◽

The Web

BACKGROUND With the rapidly accelerating spread of dissemination of false medical information on the Web, the task of establishing the credibility of online sources of medical information becomes a pressing necessity. The sheer number of websites offering questionable medical information presented as reliable and actionable suggestions with possibly harmful effects poses an additional requirement for potential solutions, as they have to scale to the size of the problem. Machine learning is one such solution which, when properly deployed, can be an effective tool in fighting medical disinformation on the Web. OBJECTIVE We present a comprehensive framework for designing and curating of machine learning training datasets for online medical information credibility assessment. We show how the annotation process should be constructed and what pitfalls should be avoided. Our main objective is to provide researchers from medical and computer science communities with guidelines on how to construct datasets for machine learning models for various areas of medical information wars. METHODS The key component of our approach is the active annotation process. We begin by outlining the annotation protocol for the curation of high-quality training dataset, which then can be augmented and rapidly extended by employing the human-in-the-loop paradigm to machine learning training. To circumvent the cold start problem of insufficient gold standard annotations, we propose a pre-processing pipeline consisting of representation learning, clustering, and re-ranking of sentences for the acceleration of the training process and the optimization of human resources involved in the annotation. RESULTS We collect over 10 000 annotations of sentences related to selected subjects (psychiatry, cholesterol, autism, antibiotics, vaccines, steroids, birth methods, food allergy testing) for less than $7 000 employing 9 highly qualified annotators (certified medical professionals) and we release this dataset to the general public. We develop an active annotation framework for more efficient annotation of non-credible medical statements. The results of the qualitative analysis support our claims of the efficacy of the presented method. CONCLUSIONS A set of very diverse incentives is driving the widespread dissemination of medical disinformation on the Web. An effective strategy of countering this spread is to use machine learning for automatically establishing the credibility of online medical information. This, however, requires a thoughtful design of the training pipeline. In this paper we present a comprehensive framework of active annotation. In addition, we publish a large curated dataset of medical statements labelled as credible, non-credible, or neutral.

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