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.

scholarly journals Combining Classification and User-Based Collaborative Filtering for Matching Footwear Size

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 771
Author(s):  
Aleix Alcacer ◽  
Irene Epifanio ◽  
Jorge Valero ◽  
Alfredo Ballester
Keyword(s):  
Missing Data ◽  
Collaborative Filtering ◽  
Simulation Study ◽  
Information Sources ◽  
Low Cost ◽  
Anthropometric Measurements ◽  
User Preference ◽  
Size Mismatch ◽  
Preference Information ◽  
First Time

Size mismatch is a serious problem in online footwear purchase because size mismatch implies an almost sure return. Not only foot measurements are important in selecting a size, but also user preference. This is the reason we propose several methodologies that combine the information provided by a classifier with anthropometric measurements and user preference information through user-based collaborative filtering. As novelties: (1) the information sources are 3D foot measurements from a low-cost 3D foot digitizer, past purchases and self-reported size; (2) we propose to use an ordinal classifier after imputing missing data with different options based on the use of collaborative filtering; (3) we also propose an ensemble of ordinal classification and collaborative filtering results; and (4) several methodologies based on clustering and archetype analysis are introduced as user-based collaborative filtering for the first time. The hybrid methodologies were tested in a simulation study, and they were also applied to a dataset of Spanish footwear users. The results show that combining the information from both sources predicts the foot size better and the new proposals provide better accuracy than the classic alternatives considered.

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 Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lili Wang ◽  
Ting Shi ◽  
Shijin Li
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recall Rate ◽  
Cosine Similarity ◽  
Fusion Method ◽  
Complex Matrix ◽  
Recommendation Algorithm ◽  
Kl Divergence ◽  
Experimental Effect ◽  
User Recommendation

Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to correctly recommend relevant services for users by using the recommendation method based on location and collaborative filtering. The similarity measure between users is low. This paper proposes a fusion method based on KL divergence and cosine similarity. KL divergence and cosine similarity have advantages by comparing three similar metrics at different K values. Using the fusion method of the two, the user’s similarity with the preference is reused. By comparing the location-based collaborative filtering (LCF) algorithm, user-based collaborative filtering (UCF) algorithm, and user recommendation algorithm (F2F), the proposed method has the preparation rate, recall rate, and experimental effect advantage. In different median values, the proposed method also has an advantage in experimental results.

scholarly journals Author Topic Model based Collaborative Filtering for Personalized POI Recommendation

2015 ◽  
pp. 1-1 ◽  
Author(s):  
Shuhui Jiang ◽  
Xueming Qian ◽  
Jialie Shen ◽  
Yun Fu ◽  
Tao Mei
Keyword(s):  
Collaborative Filtering ◽  
Topic Model ◽  
Model Based ◽  
Poi Recommendation

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.

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