Product Ranking on Online Platforms

On online platforms, consumers face an abundance of options that are displayed in the form of a position ranking. Only products placed in the first few positions are readily accessible to the consumer, and she needs to exert effort to access more options. For such platforms, we develop a two-stage sequential search model where, in the first stage, the consumer sequentially screens positions to observe the preference weight of the products placed in them and forms a consideration set. In the second stage, she observes the additional idiosyncratic utility that she can derive from each product and chooses the highest-utility product within her consideration set. For this model, we first characterize the optimal sequential search policy of a welfare-maximizing consumer. We then study how platforms with different objectives should rank products. We focus on two objectives: (i) maximizing the platform’s market share and (ii) maximizing the consumer’s welfare. Somewhat surprisingly, we show that ranking products in decreasing order of their preference weights does not necessarily maximize market share or consumer welfare. Such a ranking may shorten the consumer’s consideration set due to the externality effect of high-positioned products on low-positioned ones, leading to insufficient screening. We then show that both problems—maximizing market share and maximizing consumer welfare—are NP-complete. We develop novel near-optimal polynomial-time ranking algorithms for each objective. Further, we show that, even though ranking products in decreasing order of their preference weights is suboptimal, such a ranking enjoys strong performance guarantees for both objectives. We complement our theoretical developments with numerical studies using synthetic data, in which we show (1) that heuristic versions of our algorithms that do not rely on model primitives perform well and (2) that our model can be effectively estimated using a maximum likelihood estimator. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

Visual Sequential Search Test Analysis: An Algorithmic Approach

In this work we present an algorithmic approach to the analysis of the Visual Sequential Search Test (VSST) based on the episode matching method. The data set included two groups of patients, one with Parkinson’s disease, and another with chronic pain syndrome, along with a control group. The VSST is an eye-tracking modified version of the Trail Making Test (TMT) which evaluates high order cognitive functions. The episode matching method is traditionally used in bioinformatics applications. Here it is used in a different context which helps us to assign a score to a set of patients, under a specific VSST task to perform. Experimental results provide statistical evidence of the different behaviour among different classes of patients, according to different pathologies.

Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Reranking Strategy

Finding defects early in a software system is a crucial task, as it creates adequate time for fixing such defects using available resources. Strategies such as symmetric testing have proven useful; however, its inability in differentiating incorrect implementations from correct ones is a drawback. Software defect prediction (SDP) is another feasible method that can be used for detecting defects early. Additionally, high dimensionality, a data quality problem, has a detrimental effect on the predictive capability of SDP models. Feature selection (FS) has been used as a feasible solution for solving the high dimensionality issue in SDP. According to current literature, the two basic forms of FS approaches are filter-based feature selection (FFS) and wrapper-based feature selection (WFS). Between the two, WFS approaches have been deemed to be superior. However, WFS methods have a high computational cost due to the unknown number of executions available for feature subset search, evaluation, and selection. This characteristic of WFS often leads to overfitting of classifier models due to its easy trapping in local maxima. The trapping of the WFS subset evaluator in local maxima can be overcome by using an effective search method in the evaluator process. Hence, this study proposes an enhanced WFS method that dynamically and iteratively selects features. The proposed enhanced WFS (EWFS) method is based on incrementally selecting features while considering previously selected features in its search space. The novelty of EWFS is based on the enhancement of the subset evaluation process of WFS methods by deploying a dynamic re-ranking strategy that iteratively selects germane features with a low subset evaluation cycle while not compromising the prediction performance of the ensuing model. For evaluation, EWFS was deployed with Decision Tree (DT) and Naïve Bayes classifiers on software defect datasets with varying granularities. The experimental findings revealed that EWFS outperformed existing metaheuristics and sequential search-based WFS approaches established in this work. Additionally, EWFS selected fewer features with less computational time as compared with existing metaheuristics and sequential search-based WFS methods.

ANALSIS DAN PERANCANGAN KAMUS BAHASA ILMIAH TUMBUHAN BERBASIS ANDROID

The scientific name of living things, especially plants, is one of the things that is basically very interesting to learn and know, because the scientific name of living things provides an important role, among others with scientific names it will easily know the characteristics, relationships of relatives, and interactions of living things in the environment. Knowledge of plant classification has generally been taught to high school students and MIPA students or biology majors, but interest in learning about plant taxonomy is still low due to the source of information that is not yet widely available. Therefore, the development of learning media is needed one of them by building an android-based plant scientific language dictionary application that can be one of the alternative learning media for students and students. The algorithm applied is sequential search that performs search faster because the search process is already in order. That way the interest in learning students and students about the scientific language of plants can be encouraged by the spirit in learning by using this learning medium. Based on the research that has been done, the application of the scientific language dictionary of plants can be used by students, students and the public as a means of learning in understanding the scientific names on each plant that is equipped with the alphabetic menu a-z and plant categories and search features to make it easier for users to find plant names

More Negative FRN From Stopping Searches Too Late Than Too Early: An ERP Study

It is widely known that the feedback from a decision outcome may evoke emotions like regret, which results from a comparison between the gain the decision-maker has made and the gain he/she might make. Less is known about how search behavior is linked to feedback in a sequential search task such as searching for jobs, employees, prices, investments, disinvestments, or other items. What are the neural responses once subjects decide to stop searching and receive the feedback that they stopped too early or too late compared with the optimal stopping time? In an experimental setting of a search task, we found that the feedback-related negativity (FRN) induced by the feedback from stopping too late was more negative than stopping too early, suggesting that subjects might experience stronger regret when stopping too late. Subjects preferred to stop searching earlier if the last feedback was that they stopped too late, and vice versa, although they did not always benefit more from such adjustment. This might reflect general patterns of human learning behavior, which also manifests in many other decisions. Gender differences and risk attitudes were also considered in the study.

Perancangan Aplikasi Pengenalan Tokoh Penemu Benda-Benda Penting Di Dunia Berbasis Android Menggunakan Metode Sequential Search

The inventor figures succeeded in contributing to people's lives and making people's lives developed with the findings of the inventor figures, but there are still many people who do not know and know the inventor figures, people only know the objects found by the inventor figures but do not know who the characters find the objects. that thing. The introduction of the inventor character is one of the reference materials that can be used to introduce the inventor character. In this day and age, android-based inventor character recognition applications are very efficient to use. In making a character recognition application, strings can be implemented for the word search process. String can be interpreted as an approach how to find the pattern of arrangement of string characters in other strings or part of the body of text. String has several algorithms, one of which is the Sequential Search algorithm where this algorithm is a very simple search algorithm that is carried out by comparing data one by one from a predetermined data set (Array) until the data is completed (found) or there is no match (not found). ). This inventor character recognition application is designed by utilizing the setudio android software. So that the application is easier to use and can be used independently anywhere and anytime.