A poster demonstration accompanying our paper on revenue maximization in recommender systems, to be presented at 2015 Very Large Data Bases conference (VLDB).
This document provides an introduction to ensemble learning techniques. It defines ensemble learning as combining the predictions of multiple machine learning models. The main ensemble methods described are bagging, boosting, and voting. Bagging involves training models on random subsets of data and combining results by majority vote. Boosting iteratively trains models to focus on misclassified examples from previous models. Voting simply averages the predictions of different model types. The document discusses how these techniques are implemented in scikit-learn and provides examples of decision tree bagging on the Iris dataset.
Ensemble methods are machine learning techniques that combine multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Bagging creates multiple bootstrap samples of the data and trains a model on each sample, then averages the predictions to reduce variance. Boosting converts weak learners into strong ones by iteratively reweighting samples and focusing on incorrectly predicted instances. It aims to reduce bias and variance.
This document discusses operations research and the assignment problem. It defines operations research as applying scientific methods to optimize systems. The assignment problem aims to minimize the cost or time of assigning jobs to people. For maximization problems, the Hungarian method cannot be directly applied, so values are subtracted from the maximum to convert it to a minimization problem. An example shows assigning classes to professors, with the optimal solution being C1 to P2, C2 to P1, etc. for a total efficiency of 330.
Models of Operational research, Advantages & disadvantages of Operational res...Sunny Mervyne Baa
This document discusses operational research models and their advantages and disadvantages. It describes several common OR models including linear programming, network flow programming, integer programming, nonlinear programming, dynamic programming, stochastic programming, combinatorial optimization, stochastic processes, discrete time Markov chains, continuous time Markov chains, queuing, and simulation. It notes advantages of OR in developing better systems, control, and decisions. However, it also lists limitations such as dependence on computers, inability to quantify all factors, distance between managers and researchers, costs of money and time, and challenges implementing OR solutions.
This document provides an agenda for a meetup on data science topics. The meetup will be held once a month, with the next one on June 14th. It aims to provide the best networking and learning platform in Bangalore for areas like data science, big data, machine learning. The agenda includes introductions, an overview of the model building lifecycle, data exploration and feature engineering techniques, and modeling techniques like logistic regression, decision trees, random forests, and SVM. Teams will be formed to predict whether bids are from humans or robots using these techniques. Resources for implementing the techniques in Python and R are also provided.
This document presents 15 quantitative techniques and tools: Linear Programming, Queuing Theory, Inventory Control Method, Net Work Analysis, Replacement Problems, Sequencing, Integer Programming, Assignment Problems, Transportation Problems, Decision Theory and Game Theory, Markov Analysis, Simulation, Dynamic Programming, Goal Programming, and Symbolic Logic. It provides a brief overview of each technique, describing its purpose and typical applications.
This document contains answers to assignment questions on operations research. It defines operations research and describes types of operations research models including physical and mathematical models. It also outlines the phases of operations research including the judgment, research, and action phases. Additionally, it provides explanations and examples of linear programming problems and their graphical solution method, as well as addressing how to solve degeneracies in transportation problems and explaining the MODI optimality test procedure.
Use of quantitative techniques in economicsBalaji P
1. The document discusses three quantitative techniques used in economics: comparative static analysis, linear programming, and game theory.
2. Comparative static analysis compares economic equilibrium before and after changes in exogenous parameters like demand or supply. It examines how endogenous variables like price and quantity adjust.
3. Linear programming identifies the optimal allocation of limited resources to maximize profits or minimize costs. It formulates the problem as mathematical equations and graphs to find the best solution.
4. Game theory analyzes strategic decision-making in competitive situations. It models interactions between players and outcomes using payoff matrices and extensive forms to determine optimal strategies.
This document provides an introduction to ensemble learning techniques. It defines ensemble learning as combining the predictions of multiple machine learning models. The main ensemble methods described are bagging, boosting, and voting. Bagging involves training models on random subsets of data and combining results by majority vote. Boosting iteratively trains models to focus on misclassified examples from previous models. Voting simply averages the predictions of different model types. The document discusses how these techniques are implemented in scikit-learn and provides examples of decision tree bagging on the Iris dataset.
Ensemble methods are machine learning techniques that combine multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Bagging creates multiple bootstrap samples of the data and trains a model on each sample, then averages the predictions to reduce variance. Boosting converts weak learners into strong ones by iteratively reweighting samples and focusing on incorrectly predicted instances. It aims to reduce bias and variance.
This document discusses operations research and the assignment problem. It defines operations research as applying scientific methods to optimize systems. The assignment problem aims to minimize the cost or time of assigning jobs to people. For maximization problems, the Hungarian method cannot be directly applied, so values are subtracted from the maximum to convert it to a minimization problem. An example shows assigning classes to professors, with the optimal solution being C1 to P2, C2 to P1, etc. for a total efficiency of 330.
Models of Operational research, Advantages & disadvantages of Operational res...Sunny Mervyne Baa
This document discusses operational research models and their advantages and disadvantages. It describes several common OR models including linear programming, network flow programming, integer programming, nonlinear programming, dynamic programming, stochastic programming, combinatorial optimization, stochastic processes, discrete time Markov chains, continuous time Markov chains, queuing, and simulation. It notes advantages of OR in developing better systems, control, and decisions. However, it also lists limitations such as dependence on computers, inability to quantify all factors, distance between managers and researchers, costs of money and time, and challenges implementing OR solutions.
This document provides an agenda for a meetup on data science topics. The meetup will be held once a month, with the next one on June 14th. It aims to provide the best networking and learning platform in Bangalore for areas like data science, big data, machine learning. The agenda includes introductions, an overview of the model building lifecycle, data exploration and feature engineering techniques, and modeling techniques like logistic regression, decision trees, random forests, and SVM. Teams will be formed to predict whether bids are from humans or robots using these techniques. Resources for implementing the techniques in Python and R are also provided.
This document presents 15 quantitative techniques and tools: Linear Programming, Queuing Theory, Inventory Control Method, Net Work Analysis, Replacement Problems, Sequencing, Integer Programming, Assignment Problems, Transportation Problems, Decision Theory and Game Theory, Markov Analysis, Simulation, Dynamic Programming, Goal Programming, and Symbolic Logic. It provides a brief overview of each technique, describing its purpose and typical applications.
This document contains answers to assignment questions on operations research. It defines operations research and describes types of operations research models including physical and mathematical models. It also outlines the phases of operations research including the judgment, research, and action phases. Additionally, it provides explanations and examples of linear programming problems and their graphical solution method, as well as addressing how to solve degeneracies in transportation problems and explaining the MODI optimality test procedure.
Use of quantitative techniques in economicsBalaji P
1. The document discusses three quantitative techniques used in economics: comparative static analysis, linear programming, and game theory.
2. Comparative static analysis compares economic equilibrium before and after changes in exogenous parameters like demand or supply. It examines how endogenous variables like price and quantity adjust.
3. Linear programming identifies the optimal allocation of limited resources to maximize profits or minimize costs. It formulates the problem as mathematical equations and graphs to find the best solution.
4. Game theory analyzes strategic decision-making in competitive situations. It models interactions between players and outcomes using payoff matrices and extensive forms to determine optimal strategies.
1.Irm Forum 2011 Vm Workshop Precis For Linked InRedAmber
The document discusses how to select an optimum solution for cutting costs through a value management workshop. It outlines the agenda, rules, and process to be followed in the workshop, including defining value, performing a functional analysis, creating a value hierarchy to prioritize objectives, identifying options, and evaluating options through a weighted scoring system to determine the most promising option. The workshop is to be led by Mike Walker, a risk management consultant specializing in reducing business and project risks.
A lot of people talk about Data Mining, Machine Learning and Big Data. It clearly must be important, right?
A lot of people are also trying to sell you snake oil - sometimes half-arsed and overpriced products or solutions promising a world of insight into your customers or users if you handover your data to them. Instead, trying to understanding your own data and what you could do with it, should be the first thing you’d be looking at.
In this talk, we’ll introduce some basic terminology about Data and Text Mining as well as Machine Learning and will have a look at what you can on your own to understand more about your data and discover patterns in your data.
We study the problem of profit maximization in social networks through influence diffusion. We propose elegant model that describes the diffusion process, distinguishes between the states of being influenced and adopting a product. We then give efficient and effective algorithms to solve this NP-hard problem.
This document summarizes a research paper on maximizing profit through social influence propagation. It introduces a new Linear Threshold model with Valuations (LT-V) that incorporates monetary aspects like price and user valuations. The Profit Maximization (ProMax) problem is defined as selecting seed users and prices to maximize expected profit under LT-V. Three algorithms are proposed: All-OMP sets a single optimal price; FFS offers seeds free products; and PAGE greedily selects seeds and computes price optimally. Experiments on real networks show PAGE achieves significantly higher profits than the baselines by balancing immediate and potential profits from seeds.
Adversarial learning for neural dialogue generationKeon Kim
This document summarizes an adversarial learning approach for neural dialogue generation. The model uses a generator and discriminator, where the generator produces responses and the discriminator determines if they are human-like. The generator is trained to maximize rewards from the discriminator using policy gradients. Two methods are introduced to assign rewards at each generation step to address issues with the baseline approach. Teacher forcing is also used to directly expose the generator to human responses during training. The results showed this adversarial training approach generates higher quality responses than previous baselines.
Supercharge your AB testing with automated causal inference - Community Works...Egor Kraev
An A/B test consists of splitting the customers into a test and a control group, and choosing a large enough sample size to observe the average treatment effect (ATE) we are interested in, in spite of all the other factors driving outcome variance. With causal inference models, we can do better than that, by estimating the effect conditional on customer features (CATE), thus turning customer variability from noise to be averaged over to a valuable source of segmentation, and potentially requiring smaller sample sizes as a result. Unfortunately, there are many different models available for estimating CATE, with many parameters to tune and very different performance. In this talk, we will present our auto-causality library, which combines the three marvelous packages from Microsoft – DoWhy, EconML, and FLAML – to do fully automated selection and tuning of causal models based on out-of-sample performance, just like any other AutoML package does. We will describe the projects inside Wise currently starting to apply it, and present results on comparative model performance and out-of-sample segmentation on Wise CRM data.
From this presentation you will learn how to prioritize decision-making criteria with your team. You need to agree on criteria priorities in order to make decisions together.
This document provides an introduction to operations research. It defines operations research as seeking to improve problem solutions through analysis and mathematical models. It gives examples of common optimization problems involving transportation networks, resource allocation, and facility layout. The document classifies optimization problems as either unconstrained or constrained. It explains constrained problems involve an objective function and constraints. Finally, it outlines common solution methods for constrained optimization problems like linear programming.
The document provides guidance on addressing common issues that arise when segmenting data. It discusses 10 issues related to data preparation when forming customer segments, including how to handle missing data, different question types and scales. It also covers 5 additional issues that can occur with the resulting segments, such as the segmentation being driven by only a few variables. Across the 20 issues covered, the document provides recommendations on the best ways to approach each problem when performing segmentation analysis.
The document provides guidelines for training deep neural networks (DNNs). It discusses obtaining large, clean training datasets and using data augmentation. It recommends tanh or ReLU activation functions to avoid problems with sigmoid functions. The number of hidden units and layers should be optimized, and weights initialized randomly. Learning rates can use adaptive methods like Adam. Hyperparameter tuning is best done with random search instead of grid search. Mini-batch training provides faster learning than stochastic methods. Dropout helps prevent overfitting.
Software Development in the Brave New worldDavid Leip
The document discusses the agile software development methodology of Extreme Programming (XP). It provides an overview of XP, including its values, practices, and roles. It notes that XP focuses on communication, simplicity, feedback, and courage. Key practices include pair programming, user stories, planning iterations based on velocity, and daily stand-up meetings. The document also covers challenges and lessons learned with adopting XP.
The document discusses the agile software development methodology of Extreme Programming (XP). It provides an overview of XP, including its values, practices, and roles. It notes that XP focuses on communication, simplicity, feedback, and courage. Key practices include pair programming, user stories, planning games, and frequent small releases. The document also covers challenges and lessons learned with adopting XP.
This document outlines key concepts in recommendation systems. It begins by defining the traditional recommender problem as predicting user ratings for items based on past behavior and relationships. It then discusses lessons learned from the Netflix Prize competition, including the effectiveness of singular value decomposition and the limitations of models designed only for rating prediction. The document outlines approaches beyond rating prediction, including ranking, similarity, social recommendations, and explore/exploit tradeoffs. It discusses optimizing recommendation pages and using higher-order models like tensor factorization. In summary, it provides an overview of traditional and modern approaches in recommendation systems.
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
Model-Based User Interface Optimization: Part IV: ADVANCED TOPICS - At SICSA ...Aalto University
The document discusses optimization techniques for user interfaces, focusing on metaheuristics and ant colony optimization. Metaheuristics provide intelligent, black-box optimization by learning and updating models of the problem environment through cooperation of multiple search agents. Ant colony optimization is well-suited for user interface design as layouts are constructed iteratively. The document outlines challenges like robustness to noise, multi-objective optimization, and dynamic problems. Techniques for addressing complex tasks include decomposition, screening, space reduction, and sub-space elimination.
Netflix uses a variety of techniques to provide personalized recommendations to users. Some key aspects include:
1. Netflix recommendations are generated using both offline and online techniques. Offline techniques allow for more complex computations but results may become stale, while online techniques can respond quickly but have stricter time constraints.
2. Recommendations are generated using a variety of data sources and machine learning models, including SVD, RBMs, gradient boosted trees, and other techniques. Both the data and models are important for generating high quality recommendations.
3. Netflix tests recommendations using both offline and online A/B testing techniques. Offline testing is used to evaluate new models and ideas before launching online tests involving real users
Recommender Systems from A to Z – Model TrainingCrossing Minds
This second meetup will be about training different models for our recommender system. We will review the simple models we can build as a baseline. After that, we will present the recommender system as an optimization problem and discuss different training losses. We will mention linear models and matrix factorization techniques. We will end the presentation with a simple introduction to non-linear models and deep learning.
By popular demand, here is a case study of my first Kaggle competition from about a year ago. Hope you find it useful. Thank you again to my fantastic team.
Data Analysis: Evaluation Metrics for Supervised Learning Models of Machine L...Md. Main Uddin Rony
This document discusses various machine learning evaluation metrics for supervised learning models. It covers classification, regression, and ranking metrics. For classification, it describes accuracy, confusion matrix, log-loss, and AUC. For regression, it discusses RMSE and quantiles of errors. For ranking, it explains precision-recall, precision-recall curves, F1 score, and NDCG. The document provides examples and visualizations to illustrate how these metrics are calculated and used to evaluate model performance.
Refutations on "Debunking the Myths of Influence Maximization: An In-Depth Be...Wei Lu
- The document examines flaws in the experimental design and methodology of the paper "Debunking the Myths of Influence Maximization: A Benchmarking Study".
- It identifies fundamental flaws that lead to incorrect conclusions, such as algorithms being held to different standards of optimality.
- It also finds unreproducible and critical experiments used to determine benchmarking parameters, and refutes over 10 misclaims made about previous influence maximization algorithms.
Social Recommendation with Strong and Weak TiesWei Lu
This document summarizes a paper on social recommendation with strong and weak ties. It begins by introducing social recommendation and techniques like rating prediction and top-N item recommendation. It then discusses how social ties have been studied in social science, defined in online social networks, and how they can be incorporated into recommendation models. Specifically, it presents methods to classify social ties as strong or weak based on metrics like Jaccard's coefficient. It also categorizes items based on whether they were consumed by a user's strong ties, weak ties, or neither, and proposes models like TBPR that integrate this social tie information to improve recommendations.
More Related Content
Similar to VLDB 2015 poster: Revenue Maximization in Recommender Systems
1.Irm Forum 2011 Vm Workshop Precis For Linked InRedAmber
The document discusses how to select an optimum solution for cutting costs through a value management workshop. It outlines the agenda, rules, and process to be followed in the workshop, including defining value, performing a functional analysis, creating a value hierarchy to prioritize objectives, identifying options, and evaluating options through a weighted scoring system to determine the most promising option. The workshop is to be led by Mike Walker, a risk management consultant specializing in reducing business and project risks.
A lot of people talk about Data Mining, Machine Learning and Big Data. It clearly must be important, right?
A lot of people are also trying to sell you snake oil - sometimes half-arsed and overpriced products or solutions promising a world of insight into your customers or users if you handover your data to them. Instead, trying to understanding your own data and what you could do with it, should be the first thing you’d be looking at.
In this talk, we’ll introduce some basic terminology about Data and Text Mining as well as Machine Learning and will have a look at what you can on your own to understand more about your data and discover patterns in your data.
We study the problem of profit maximization in social networks through influence diffusion. We propose elegant model that describes the diffusion process, distinguishes between the states of being influenced and adopting a product. We then give efficient and effective algorithms to solve this NP-hard problem.
This document summarizes a research paper on maximizing profit through social influence propagation. It introduces a new Linear Threshold model with Valuations (LT-V) that incorporates monetary aspects like price and user valuations. The Profit Maximization (ProMax) problem is defined as selecting seed users and prices to maximize expected profit under LT-V. Three algorithms are proposed: All-OMP sets a single optimal price; FFS offers seeds free products; and PAGE greedily selects seeds and computes price optimally. Experiments on real networks show PAGE achieves significantly higher profits than the baselines by balancing immediate and potential profits from seeds.
Adversarial learning for neural dialogue generationKeon Kim
This document summarizes an adversarial learning approach for neural dialogue generation. The model uses a generator and discriminator, where the generator produces responses and the discriminator determines if they are human-like. The generator is trained to maximize rewards from the discriminator using policy gradients. Two methods are introduced to assign rewards at each generation step to address issues with the baseline approach. Teacher forcing is also used to directly expose the generator to human responses during training. The results showed this adversarial training approach generates higher quality responses than previous baselines.
Supercharge your AB testing with automated causal inference - Community Works...Egor Kraev
An A/B test consists of splitting the customers into a test and a control group, and choosing a large enough sample size to observe the average treatment effect (ATE) we are interested in, in spite of all the other factors driving outcome variance. With causal inference models, we can do better than that, by estimating the effect conditional on customer features (CATE), thus turning customer variability from noise to be averaged over to a valuable source of segmentation, and potentially requiring smaller sample sizes as a result. Unfortunately, there are many different models available for estimating CATE, with many parameters to tune and very different performance. In this talk, we will present our auto-causality library, which combines the three marvelous packages from Microsoft – DoWhy, EconML, and FLAML – to do fully automated selection and tuning of causal models based on out-of-sample performance, just like any other AutoML package does. We will describe the projects inside Wise currently starting to apply it, and present results on comparative model performance and out-of-sample segmentation on Wise CRM data.
From this presentation you will learn how to prioritize decision-making criteria with your team. You need to agree on criteria priorities in order to make decisions together.
This document provides an introduction to operations research. It defines operations research as seeking to improve problem solutions through analysis and mathematical models. It gives examples of common optimization problems involving transportation networks, resource allocation, and facility layout. The document classifies optimization problems as either unconstrained or constrained. It explains constrained problems involve an objective function and constraints. Finally, it outlines common solution methods for constrained optimization problems like linear programming.
The document provides guidance on addressing common issues that arise when segmenting data. It discusses 10 issues related to data preparation when forming customer segments, including how to handle missing data, different question types and scales. It also covers 5 additional issues that can occur with the resulting segments, such as the segmentation being driven by only a few variables. Across the 20 issues covered, the document provides recommendations on the best ways to approach each problem when performing segmentation analysis.
The document provides guidelines for training deep neural networks (DNNs). It discusses obtaining large, clean training datasets and using data augmentation. It recommends tanh or ReLU activation functions to avoid problems with sigmoid functions. The number of hidden units and layers should be optimized, and weights initialized randomly. Learning rates can use adaptive methods like Adam. Hyperparameter tuning is best done with random search instead of grid search. Mini-batch training provides faster learning than stochastic methods. Dropout helps prevent overfitting.
Software Development in the Brave New worldDavid Leip
The document discusses the agile software development methodology of Extreme Programming (XP). It provides an overview of XP, including its values, practices, and roles. It notes that XP focuses on communication, simplicity, feedback, and courage. Key practices include pair programming, user stories, planning iterations based on velocity, and daily stand-up meetings. The document also covers challenges and lessons learned with adopting XP.
The document discusses the agile software development methodology of Extreme Programming (XP). It provides an overview of XP, including its values, practices, and roles. It notes that XP focuses on communication, simplicity, feedback, and courage. Key practices include pair programming, user stories, planning games, and frequent small releases. The document also covers challenges and lessons learned with adopting XP.
This document outlines key concepts in recommendation systems. It begins by defining the traditional recommender problem as predicting user ratings for items based on past behavior and relationships. It then discusses lessons learned from the Netflix Prize competition, including the effectiveness of singular value decomposition and the limitations of models designed only for rating prediction. The document outlines approaches beyond rating prediction, including ranking, similarity, social recommendations, and explore/exploit tradeoffs. It discusses optimizing recommendation pages and using higher-order models like tensor factorization. In summary, it provides an overview of traditional and modern approaches in recommendation systems.
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
Model-Based User Interface Optimization: Part IV: ADVANCED TOPICS - At SICSA ...Aalto University
The document discusses optimization techniques for user interfaces, focusing on metaheuristics and ant colony optimization. Metaheuristics provide intelligent, black-box optimization by learning and updating models of the problem environment through cooperation of multiple search agents. Ant colony optimization is well-suited for user interface design as layouts are constructed iteratively. The document outlines challenges like robustness to noise, multi-objective optimization, and dynamic problems. Techniques for addressing complex tasks include decomposition, screening, space reduction, and sub-space elimination.
Netflix uses a variety of techniques to provide personalized recommendations to users. Some key aspects include:
1. Netflix recommendations are generated using both offline and online techniques. Offline techniques allow for more complex computations but results may become stale, while online techniques can respond quickly but have stricter time constraints.
2. Recommendations are generated using a variety of data sources and machine learning models, including SVD, RBMs, gradient boosted trees, and other techniques. Both the data and models are important for generating high quality recommendations.
3. Netflix tests recommendations using both offline and online A/B testing techniques. Offline testing is used to evaluate new models and ideas before launching online tests involving real users
Recommender Systems from A to Z – Model TrainingCrossing Minds
This second meetup will be about training different models for our recommender system. We will review the simple models we can build as a baseline. After that, we will present the recommender system as an optimization problem and discuss different training losses. We will mention linear models and matrix factorization techniques. We will end the presentation with a simple introduction to non-linear models and deep learning.
By popular demand, here is a case study of my first Kaggle competition from about a year ago. Hope you find it useful. Thank you again to my fantastic team.
Data Analysis: Evaluation Metrics for Supervised Learning Models of Machine L...Md. Main Uddin Rony
This document discusses various machine learning evaluation metrics for supervised learning models. It covers classification, regression, and ranking metrics. For classification, it describes accuracy, confusion matrix, log-loss, and AUC. For regression, it discusses RMSE and quantiles of errors. For ranking, it explains precision-recall, precision-recall curves, F1 score, and NDCG. The document provides examples and visualizations to illustrate how these metrics are calculated and used to evaluate model performance.
Similar to VLDB 2015 poster: Revenue Maximization in Recommender Systems (20)
Refutations on "Debunking the Myths of Influence Maximization: An In-Depth Be...Wei Lu
- The document examines flaws in the experimental design and methodology of the paper "Debunking the Myths of Influence Maximization: A Benchmarking Study".
- It identifies fundamental flaws that lead to incorrect conclusions, such as algorithms being held to different standards of optimality.
- It also finds unreproducible and critical experiments used to determine benchmarking parameters, and refutes over 10 misclaims made about previous influence maximization algorithms.
Social Recommendation with Strong and Weak TiesWei Lu
This document summarizes a paper on social recommendation with strong and weak ties. It begins by introducing social recommendation and techniques like rating prediction and top-N item recommendation. It then discusses how social ties have been studied in social science, defined in online social networks, and how they can be incorporated into recommendation models. Specifically, it presents methods to classify social ties as strong or weak based on metrics like Jaccard's coefficient. It also categorizes items based on whether they were consumed by a user's strong ties, weak ties, or neither, and proposes models like TBPR that integrate this social tie information to improve recommendations.
From Competition to Complementarity: Comparative Influence Diffusion and Maxi...Wei Lu
VLDB'16 Research Paper.
Influence maximization is a well-studied problem that asks for a
small set of influential users from a social network, such that by targeting them as early adopters, the expected total adoption through influence cascades over the network is maximized. However, almost all prior work focuses on cascades of a single propagating entity or purely-competitive entities. In this work, we propose the Comparative Independent Cascade (Com-IC) model that covers the full spectrum of entity interactions from competition to complementarity. In Com-IC, users’ adoption decisions depend not only on edge-level information propagation, but also on a node-level automaton whose behavior is governed by a set of model parameters, enabling our model to capture not only competition, but also complementarity, to any possible degree. We study two natural optimization problems, Self Influence Maximization and Complementary Influence Maximization, in a novel setting with complementary
entities. Both problems are NP-hard, and we devise efficient
and effective approximation algorithms via non-trivial techniques
based on reverse-reachable sets and a novel “sandwich approximation” strategy. The applicability of both techniques extends beyond our model and problems. Our experiments show that the proposed algorithms consistently outperform intuitive baselines on four real-world social networks, often by a significant margin. In addition, we learn model parameters from real user action logs.
This document summarizes the paper "Show Me the Money: Dynamic Recommendations for Revenue Maximization" which addresses the problem of maximizing revenue from recommendations over time while accounting for saturation effects. It proposes a model connecting recommendations to expected revenue, and algorithms for near-optimal revenue-maximizing recommendations. These include a greedy heuristic and matroid-based approximations with analysis on real e-commerce datasets showing improved revenue over baselines.
We study influence maximization in which diffusion on each step may be delayed, and the objective is to maximize influence spread within a certain deadline. Both IC and LT models are extended, and efficient algorithms are proposed and evaluated.
This work appears in AAAI 2012. For the full version of the paper, please see: http://arxiv.org/abs/1204.3074
Optimal Recommendations under Attraction, Aversion, and Social InfluenceWei Lu
Published in ACM 2014 International Conference on Knowledge Discovery and Data Mining (SIGKDD 2014)
Abstract:
People's interests are dynamically evolving, often affected by external factors such as trends promoted by the media or adopted by their friends. In this work, we model interest evolution through dynamic interest cascades: we consider a scenario where a user's interests may be affected by (a) the interests of other users in her social circle, as well as (b) suggestions she receives from a recommender system. In the latter case, we model user reactions through either attraction or aversion towards past suggestions.
We study this interest evolution process, and the utility accrued by recommendations, as a function of the system's recommendation strategy. We show that, in steady state, the optimal strategy can be computed as the solution of a semi-definite program (SDP). Using datasets of user ratings, we provide evidence for the existence of aversion and attraction in real-life data, and show that our optimal strategy can lead to significantly improved recommendations over systems that ignore aversion and attraction.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.