Personalized Recommendation System Reflecting User Preference with Context-Awareness for Mobile TV

With the explosive growth of mobile videos, helping users quickly and effectively find mobile videos of interest and further provide personalized recommendation services are the developing trends of mobile video applications. Mobile videos are characterized by their wide variety, single content, and short duration, and thus traditional personalized video recommendation methods cannot produce effective recommendation performance. Therefore, a personalized mobile video recommendation method is proposed based on user preference modeling by deep features and social tags. The main contribution of our work is three-fold: (1) deep features of mobile videos are extracted by an improved exponential linear units-3D convolutional neural network (ELU-3DCNN) for representing video content; (2) user preference is modeled by combining user preference for deep features with user preference for social tags that are respectively modeled by maximum likelihood estimation and exponential moving average method; (3) a personalized mobile video recommendation system based on user preference modeling is built after detecting key frames with a differential evolution optimization algorithm. Experiments on YouTube-8M dataset have shown that our method outperforms state-of-the-art methods in terms of both precision and recall of personalized mobile video recommendation.

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Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback

Computational Intelligence and Neuroscience ◽

10.1155/2022/9593957 ◽

2022 ◽

Vol 2022 ◽

pp. 1-12

Author(s):

Huazhen Liu ◽

Wei Wang ◽

Yihan Zhang ◽

Renqian Gu ◽

Yaqi Hao

Keyword(s):

Neural Network ◽

Matrix Factorization ◽

Recommendation System ◽

Heterogeneous Data ◽

User Preference ◽

Personalized Recommendation ◽

Implicit Feedback ◽

Network Training ◽

Feedback Data ◽

Explicit Feedback

Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear feature of explicit-implicit feedback of user-item interaction. Second, combined with the traditional matrix factorization, explicit feedback is used to accurately reflect the explicit preference and the potential preferences of users to build a recommendation model; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according to prediction results, personalized recommendation list is pushed to the user. The feasibility, validity, and robustness are fully demonstrated in comparison with multiple baseline models on two real datasets.

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Personalized Recommendation Based on Trust and Preference

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.713-715.2288 ◽

2015 ◽

Vol 713-715 ◽

pp. 2288-2291 ◽

Cited By ~ 1

Author(s):

Li Hua Wu ◽

Wen Feng Chen

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

User Preference ◽

Personalized Recommendation ◽

Actual Application ◽

Relationship Of ◽

The Relationship

Collaborative filtering recommendation is a mainstream personalized recommendation method, which has some flaws in actual application. And there are some inaccurate recommendation results in some cases. Considering the relationship of trust and similarity of user preference, this paper introduces trust to recommendation model and considers multi-dimensional factors of user preference, proposes a personalized recommendation method based on trust and preference. The recommendation method can improve the accuracy of recommendation system. Finally, this paper proves effectiveness of this recommendation method through the experiments.

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Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model

Mathematical Problems in Engineering ◽

10.1155/2020/8871126 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Dan Xiang ◽

Zhijie Zhang

Keyword(s):

Association Rule ◽

Recommendation System ◽

Real Life ◽

User Preference ◽

Personalized Recommendation ◽

Recommendation Algorithm ◽

Fuzzy Association Rules ◽

Cross Border ◽

Fuzzy Association Rule ◽

The Impact

Since cross-border e-commerce involves the export and import of commodities, it is affected by many policies and regulations, resulting in some special requirements for the recommendation system, which makes the traditional collaborative filtering recommendation algorithm less effective for the cross-border e-commerce recommendation system. To address this issue, a simple yet effective cross-border e-commerce personalized recommendation is proposed in this paper, which integrates fuzzy association rule and complex preference into a recommendation model. Under the constraint of fuzzy association rules, a hybrid recommendation model based on user complex preference features is constructed to mine user preference features, and personalized commodities recommendation is realized according to user behavior preference. Compared with the traditional recommendation algorithm, the improved algorithm reduces the impact of data sparsity. The experiment also verifies that the improved fuzzy association rule algorithm has a better recommendation effect than the existing state-of-the-art recommendation models. The recommendation system proposed in this paper has better generalization and has the performance to be applied to real-life scenarios.

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