Click-aware purchase prediction with push at the top

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user-item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see https://doi.org/10.1016/j.ins.2020.02.06

Designing Re-materialized Interactive Object Using Makey Makey

As ICT (Information & Communication Technology) and microcontroller technology have been developed, physical characteristics in products have disappeared gradually because controlling and displaying data becomes main features of products. However, after the concept of Graspable User Interface was introduced, there have been many design experiments for materializing user interfaces inside 2D screens and products. These are very meaningful in terms of designing intuitive and natural interaction methods because a human being lives in the physical world. In order to design re-materialized interactive object, tangible interaction researches from journals, thesis, and articles about the experimental prototypes were collected and categorized with the human-system interaction model. The human-system interaction model is suggested in this paper to describe the relationship amongst a human, interfaces (i.e. mouse, keyboard, screen, tangible user interface), and control and data inside object. The collected cases are organized in three different phases with the human-system interaction model. This paper proposes new model to generate concepts for next generation tangible interactive object design. The Makey Makey was selected as the toolkit for the new experiment and two interactive product design courses were instructed at Yonsei University and Seoul National University of Science & Technology from 2014 to 2015 in Korea. Many interesting outcomes by mapping control and data with humans everyday behaviors were designed. This paper can contribute to the study on the future of products how objects can be designed in tangible and physical ways.OAIID:RECH_ACHV_DSTSH_NO:A201625592RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A080155CITE_RATE:FILENAME:abstract-483.pdfDEPT_NM:디자인학부EMAIL:[email protected]_YN:FILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/f20faf7c-3d58-4837-b6d4-220eafeaa0c7/linkCONFIRM:

Effects of nucleon resonances on η\eta photoproduction off the neutron reexamined

We investigate $\eta$ photoproduction off the neutron target, i.e., $\gamma n \to \eta n$, employing an effective Lagrangian method combining with a Regge approach. As a background, we consider nucleon exchange in the $s$-channel diagram and $\rho$- and $\omega$-meson Regge trajectories in the $t$ channel. The role of nucleon resonances given in the Review of Particle Data Group in the range of $W \approx 1500 - 2100$ MeV and the narrow nucleon resonance $N(1685,1/2^+)$ is extensively studied. The numerical results of the total and differential cross sections, double polarization observable $E$, and helicity-dependent cross sections $\sigma_{1/2}$, $\sigma_{3/2}$ are found to be in qualitative agreement with the recent A2 experimental data. The predictions of the beam asymmetry are also given.Comment: 12 pages, 6 figure

Evaluation of a specific diagnostic marker for rheumatoid arthritis based on cyclic citrullinated peptide

AbstractA specific peptide marker for diagnosing rheumatoid arthritis (RA) was found based on cyclic citrullinated peptide (CCP) using the following three steps: (1) analysis of the binding epitope of autoimmune antibodies using ϵ-aminocaproic acid-modified peptides; (2) RA diagnosis using sequence-modified peptides; and (3) evaluation of the peptides’ diagnostic performance for RA diagnosis. Ninety-five serum samples were analyzed by ELISA and compared using MedCalc (version 15.2.1). Microplate binding ϵ-aminocaproic acid was added to the N- or C-terminal of the CCP sequence. The N-terminal anchoring peptide assay showed 15% higher specificity compared with the C-terminal anchoring peptide assay. Based on this result, the hydrophilic C-terminal sequence of CCP was substituted with a hydrophobic amino acid. Among the sequence-modified peptides, CCP11A (in which alanine was substituted for the 11th amino acid of CCP) assay showed the highest sensitivity (87%) and specificity (100%) for RA diagnosis. Thus, CCP11A was selected as a possible specific marker peptide for RA diagnosis and further analyzed. The results of this analysis indicated that CCP11A showed better specificity than the CCP assay in both healthy individuals (11% better) and OA cohort (20% better). From these results, CCP11A was evaluated as a specific marker for diagnosing RA with higher diagnostic performance

Identification of the optimal hiit protocol for fatigue resistance in adolescent athletes: a randomized controlled trial

The combat sports athletes developed great gains in both muscular function and fatigue resistance by utilizing high-intensity interval training (HIIT). However, it has not been investigated fully whether different work-to-rest ratios of HIIT show the effectiveness on muscle function in adolescent athletes. The purpose of the study was to compare different work-to-rest ratios by applying different rest times in response to the identical work time during HIIT on muscle function in adolescent taekwondo athletes. Forty-seven adolescent male taekwondo athletes (mean age: 16.7±0.8 years) were randomly assigned to the control group (n=11) vs. three HIIT groups by work-to-rest ratios; (1) 1:2 [30s:60s] (n=12), (2) 1:4 [30s:120s] (n=12), and (3) 1:8 [30s:240s] (n=12). All groups completed 10 experimental sessions over four weeks, while the control group maintained their regular taekwondo training. Muscular functions were measured by assessing isokinetic muscle strength and endurance of the knee extensor and flexor. The participants performed three sets of twenty maximal extension and flexion contractions at 120°s-1 with a 1-min interval between the sets for fatigue resistance. Blood samples were collected to measure free-testosterone, cortisol, creatine kinase, and urea as stress-to-recovery indicators. A positive effect on improving muscle fatigue resistance was observed at the first set of assessments in the HIIT with 1:4 (Δ10.2%, p<.05) and 1:8 groups (Δ8.6%, p<.05). Additionally, the 1:4 group exhibited fatigue resistance improvement in the second set (Δ7.7%, p<.01) without any changes of stress-to-recovery indicators, while the other groups did not show any improvement. The 30s all-out work with 120s rest time, lasting over a brief 4-week period, improved participants’ fatigue resistance. A certain amount of rest time between high-intense movements is required to optimize muscle development in adolescent athletes compared with insufficient rest time