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.

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 A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

2021 ◽  
Vol 11 (8) ◽  
pp. 3719
Author(s):  
Sun-Young Ihm ◽  
Shin-Eun Lee ◽  
Young-Ho Park ◽  
Aziz Nasridinov ◽  
Miyeon Kim ◽  
...  
Keyword(s):  
Missing Data ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
High Reliability ◽  
User Preferences ◽  
User Preference ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Common Interests ◽  
Recommendation Accuracy

Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is sparse. Data imputation can alleviate the data sparsity problem by substituting a virtual part of the missing user preferences. In this paper, we propose a k-recursive reliability-based imputation (k-RRI) that first selects data with high reliability and then recursively imputes data with additional selection while gradually lowering the reliability criterion. We also propose a new similarity measure that weights common interests and indifferences between users and items. The proposed method can overcome disregarding the importance of missing data and resolve the problem of poor data imputation of existing methods. The experimental results demonstrate that the proposed approach significantly improves recommendation accuracy compared to those resulting from the state-of-the-art methods while demanding less computational complexity.

scholarly journals UV Inscription and Pressure Induced Long-Period Gratings through 3D Printed Amplitude Masks

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1977
Author(s):  
Ricardo Oliveira ◽  
Liliana M. Sousa ◽  
Ana M. Rocha ◽  
Rogério Nogueira ◽  
Lúcia Bilro
Keyword(s):  
State Of The Art ◽  
Low Cost ◽  
Resonance Wavelength ◽  
Complex Structures ◽  
Pressing Method ◽  
Long Period ◽  
Long Period Gratings ◽  
3D Printed ◽  
Mechanical Pressing ◽  
First Time

In this work, we demonstrate for the first time the capability to inscribe long-period gratings (LPGs) with UV radiation using simple and low cost amplitude masks fabricated with a consumer grade 3D printer. The spectrum obtained for a grating with 690 µm period and 38 mm length presented good quality, showing sharp resonances (i.e., 3 dB bandwidth < 3 nm), low out-of-band loss (~0.2 dB), and dip losses up to 18 dB. Furthermore, the capability to select the resonance wavelength has been demonstrated using different amplitude mask periods. The customization of the masks makes it possible to fabricate gratings with complex structures. Additionally, the simplicity in 3D printing an amplitude mask solves the problem of the lack of amplitude masks on the market and avoids the use of high resolution motorized stages, as is the case of the point-by-point technique. Finally, the 3D printed masks were also used to induce LPGs using the mechanical pressing method. Due to the better resolution of these masks compared to ones described on the state of the art, we were able to induce gratings with higher quality, such as low out-of-band loss (0.6 dB), reduced spectral ripples, and narrow bandwidths (~3 nm).

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.

A dissipative particle dynamics simulation study on phase diagrams for the self-assembly of amphiphilic hyperbranched multiarm copolymers in various solvents

Soft Matter ◽  
2017 ◽  
Vol 13 (36) ◽  
pp. 6178-6188 ◽  
Author(s):  
Haina Tan ◽  
Chunyang Yu ◽  
Zhongyuan Lu ◽  
Yongfeng Zhou ◽  
Deyue Yan
Keyword(s):  
Phase Diagrams ◽  
Simulation Study ◽  
Self Assembly ◽  
Dissipative Particle Dynamics ◽  
Particle Dynamics ◽  
The Self ◽  
Dynamics Simulation ◽  
Dynamics Simulations ◽  
First Time ◽  
Dissipative Particle Dynamics Simulation

This work discloses for the first time the self-assembly phase diagrams of amphiphilic hyperbranched multiarm copolymers in various solvents by dissipative particle dynamics simulations.

Printing ultrathin graphene oxide nanofiltration membranes for water purification

2017 ◽  
Vol 5 (39) ◽  
pp. 20860-20866 ◽  
Author(s):  
Mahdi Fathizadeh ◽  
Huynh Ngoc Tien ◽  
Konstantin Khivantsev ◽  
Jung-Tsai Chen ◽  
Miao Yu
Keyword(s):  
Graphene Oxide ◽  
Water Purification ◽  
Inkjet Printing ◽  
Low Cost ◽  
Nanofiltration Membranes ◽  
Highly Effective ◽  
First Time ◽  
Polymeric Supports ◽  
Effective Water

We demonstrated for the first time that inkjet printing can be a low-cost, easy, fast, and scalable method for depositing ultrathin (7.5–60 nm) uniform graphene oxide (GO) nanofiltration membranes on polymeric supports for highly effective water purification.

scholarly journals Improved selection of participants in genetic longevity studies: family scores revisited

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mar Rodríguez-Girondo ◽  
Niels van den Berg ◽  
Michel H. Hof ◽  
Marian Beekman ◽  
Eline Slagboom
Keyword(s):  
Missing Data ◽  
The Netherlands ◽  
Family Size ◽  
Simulation Study ◽  
Missing Values ◽  
Selection Criterion ◽  
Limited Success ◽  
Main Challenge ◽  
Historical Sample ◽  
Selection Of

Abstract Background Although human longevity tends to cluster within families, genetic studies on longevity have had limited success in identifying longevity loci. One of the main causes of this limited success is the selection of participants. Studies generally include sporadically long-lived individuals, i.e. individuals with the longevity phenotype but without a genetic predisposition for longevity. The inclusion of these individuals causes phenotype heterogeneity which results in power reduction and bias. A way to avoid sporadically long-lived individuals and reduce sample heterogeneity is to include family history of longevity as selection criterion using a longevity family score. A main challenge when developing family scores are the large differences in family size, because of real differences in sibship sizes or because of missing data. Methods We discussed the statistical properties of two existing longevity family scores: the Family Longevity Selection Score (FLoSS) and the Longevity Relatives Count (LRC) score and we evaluated their performance dealing with differential family size. We proposed a new longevity family score, the mLRC score, an extension of the LRC based on random effects modeling, which is robust for family size and missing values. The performance of the new mLRC as selection tool was evaluated in an intensive simulation study and illustrated in a large real dataset, the Historical Sample of the Netherlands (HSN). Results Empirical scores such as the FLOSS and LRC cannot properly deal with differential family size and missing data. Our simulation study showed that mLRC is not affected by family size and provides more accurate selections of long-lived families. The analysis of 1105 sibships of the Historical Sample of the Netherlands showed that the selection of long-lived individuals based on the mLRC score predicts excess survival in the validation set better than the selection based on the LRC score . Conclusions Model-based score systems such as the mLRC score help to reduce heterogeneity in the selection of long-lived families. The power of future studies into the genetics of longevity can likely be improved and their bias reduced, by selecting long-lived cases using the mLRC.

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