Attention-based dynamic user preference modeling and nonlinear feature interaction learning for collaborative filtering recommendation

2021 ◽  
pp. 107652
Author(s):  
Ruiqin Wang ◽  
Yunliang Jiang ◽  
Jungang Lou
Keyword(s):  
Collaborative Filtering ◽  
User Preference ◽  
Feature Interaction ◽  
Preference Modeling ◽  
Nonlinear Feature

scholarly journals A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021 ◽  
Vol 11 (12) ◽  
pp. 5416
Author(s):  
Yanheng Liu ◽  
Minghao Yin ◽  
Xu Zhou
Keyword(s):  
Collaborative Filtering ◽  
User Preference ◽  
Computation Method ◽  
Group Recommendation ◽  
Preference Rating ◽  
Filtering Method ◽  
Personal Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Group Members ◽  
Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

scholarly journals Personalized Mobile Video Recommendation Based on User Preference Modeling by Deep Features and Social Tags

2019 ◽  
Vol 9 (18) ◽  
pp. 3858
Author(s):  
Jiafeng Li ◽  
Chenhao Li ◽  
Jihong Liu ◽  
Jing Zhang ◽  
Li Zhuo ◽  
...  
Keyword(s):  
Recommendation System ◽  
Moving Average ◽  
Likelihood Estimation ◽  
User Preference ◽  
Personalized Recommendation ◽  
Mobile Video ◽  
Preference Modeling ◽  
Social Tags ◽  
Personalized Video ◽  
Video Recommendation

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.

scholarly journals A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation

2020 ◽  
Vol 1453 ◽  
pp. 012140 ◽  
Author(s):  
Yuxi Chen ◽  
Xiaotong Zhang ◽  
Qing Zhao ◽  
Faheem Akhtar
Keyword(s):  
Collaborative Filtering ◽  
Time Effect ◽  
User Preference ◽  
Filtering Approach

scholarly journals Multi-Component Graph Convolutional Collaborative Filtering

2020 ◽  
Vol 34 (04) ◽  
pp. 6267-6274
Author(s):  
Xiao Wang ◽  
Ruijia Wang ◽  
Chuan Shi ◽  
Guojie Song ◽  
Qingyong Li
Keyword(s):  
Collaborative Filtering ◽  
Research Effort ◽  
User Preference ◽  
Cost Performance ◽  
Fine Grained ◽  
Multiple Components ◽  
Significant Performance ◽  
Performance Gains ◽  
Latent Components ◽  
Component Graph

The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph. In recent years, we have witnessed an emerging research effort in exploring user-item graph for collaborative filtering methods. Nevertheless, the formation of user-item interactions typically arises from highly complex latent purchasing motivations, such as high cost performance or eye-catching appearance, which are indistinguishably represented by the edges. The existing approaches still remain the differences between various purchasing motivations unexplored, rendering the inability to capture fine-grained user preference. Therefore, in this paper we propose a novel Multi-Component graph convolutional Collaborative Filtering (MCCF) approach to distinguish the latent purchasing motivations underneath the observed explicit user-item interactions. Specifically, there are two elaborately designed modules, decomposer and combiner, inside MCCF. The former first decomposes the edges in user-item graph to identify the latent components that may cause the purchasing relationship; the latter then recombines these latent components automatically to obtain unified embeddings for prediction. Furthermore, the sparse regularizer and weighted random sample strategy are utilized to alleviate the overfitting problem and accelerate the optimization. Empirical results on three real datasets and a synthetic dataset not only show the significant performance gains of MCCF, but also well demonstrate the necessity of considering multiple components.

Study on Collaborative Filtering Recommendation Model Fusing User Reviews

2019 ◽  
Vol 23 (5) ◽  
pp. 864-873
Author(s):  
Heyong Wang ◽  
◽  
Ming Hong ◽  
Jinjiong Lan
Keyword(s):  
Collaborative Filtering ◽  
Topic Model ◽  
User Preference ◽  
High Dimensional ◽  
Fusion Method ◽  
User Reviews ◽  
Preference Information ◽  
Collaborative Recommendation ◽  
Public Datasets ◽  
Rating Information

The traditional collaborative filtering model suffers from high-dimensional sparse user rating information and ignores user preference information contained in user reviews. To address the problem, this paper proposes a new collaborative filtering model UL_SAM (UBCF_LDA_SIMILAR_ADD_MEAN) which integrates topic model with user-based collaborative filtering model. UL_SAM extracts user preference information from user reviews through topic model and then fuses user preference information with user rating information by similarity fusion method to create fusion information. UL_SAM creates collaborative filtering recommendations according to fusion information. It is the advantage of UL_SAM on improving recommendation effectiveness that UL_SAM enriches information for collaborative recommendation by integrating user preference with user rating information. Experimental results of two public datasets demonstrate significant improvement on recommendation effectiveness in our model.

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