Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Persuasive messages, popularity cohesion, and message diffusion in social media marketing

Purpose – There is a paucity of literature considering horizontal collaboration among grocery retailers, suppliers, and third-party logistics (3PL) providers. This paper seeks to investigate benefits of and barriers to the use of fourth-party logistics (4PL) management as a catalyst for horizontal collaboration.

Design/methodology/approach – Three suppliers, three logistics service providers (LSPs), and one grocery retailer participated in semi-structured interviews for this exploratory qualitative study.

Findings – Large LSPs can establish 4PL management but the significant investment required to do so is a deterrent. Interviewees believed 4PL would negatively influence the grocery retailer-supplier dynamic but simultaneously would provide key potential benefits. Retaining supply chain control means more to grocery retailers than cost efficiencies realised through horizontal collaboration.

Research limitations/implications – Fierce competition among major grocery chains means that most are unwilling to participate in studies of their systems, which restricts the research scope.

Practical implications – Some stakeholders want deeper integration into grocery supply networks, and the 4PL model could apply to diverse sectors and circumstances. This study shows that barriers to such integration are created by power plays among lead stakeholders in grocery retailing that inhibit horizontal collaboration regardless of cost or other benefits.

Originality/value – The study investigates an under-researched aspect of horizontal supply chain collaboration in the highly relevant retail grocery sector: a high volume, mass market industry that requires an enormous logistics infrastructure and highly embedded networks of relationships.

/pdf/using-fourth-party-logistics-management-to-improve-4x1ng9pmp3.pdf

Using fourth‐party logistics management to improve horizontal collaboration among grocery retailers

Recommendation systems (RSs) have garnered immense interest for applications in e-commerce and digital media. Traditional approaches in RSs include such as collaborative filtering (CF) and content-based filtering (CBF) through these approaches that have certain limitations, such as the necessity of prior user history and habits for performing the task of recommendation. To minimize the effect of such limitation, this article proposes a hybrid RS for the movies that leverage the best of concepts used from CF and CBF along with sentiment analysis of tweets from microblogging sites. The purpose to use movie tweets is to understand the current trends, public sentiment, and user response of the movie. Experiments conducted on the public database have yielded promising results.

Movie Recommendation System Using Sentiment Analysis From Microblogging Data

Multi-attribute online reverse auctions: recent research trends

We propose a novel latent factor model for recommending product size fits {Small, Fit, Large} to customers. Latent factors for customers and products in our model correspond to their physical true size, and are learnt from past product purchase and returns data. The outcome for a customer, product pair is predicted based on the difference between customer and product true sizes, and efficient algorithms are proposed for computing customer and product true size values that minimize two loss function variants. In experiments with Amazon shoe datasets, we show that our latent factor models incorporating personas, and leveraging return codes show a 17-21% AUC improvement compared to baselines. In an online A/B test, our algorithms show an improvement of 0.49% in percentage of Fit transactions over control.

https://dl.acm.org/doi/pdf/10.1145/3109859.3109891

Recommending Product Sizes to Customers

Lack of calibrated product sizing in popular categories such as apparel and shoes leads to customers purchasing incorrect sizes, which in turn results in high return rates due to fit issues. We address the problem of product size recommendations based on customer purchase and return data. We propose a novel approach based on Bayesian logit and probit regression models with ordinal categories Small, Fit, Largeto model size fits as a function of the difference between latent sizes of customers and products. We propose posterior computation based on mean-field variational inference, leveraging the Polya-Gamma augmentation for the logit prior, that results in simple updates, enabling our technique to efficiently handle large datasets. Our Bayesian approach effectively deals with issues arising from noise and sparsity in the data providing robust recommendations. Offline experiments with real-life shoe datasets show that our model outperforms the state-of-the-art in 5 of 6 datasets. and leads to an improvement of 17-26% in AUC over baselines when predicting size fit outcomes.

Bayesian Models for Product Size Recommendations

In this paper, we focus on subspace learning problems on the Grassmann manifold. Interesting applications in this setting include low-rank matrix completion and low-dimensional multivariate regression, among others. Motivated by privacy concerns, we aim to solve such problems in a decentralized setting where multiple agents have access to (and solve) only a part of the whole optimization problem. The agents communicate with each other to arrive at a consensus, i.e., agree on a common quantity, via the gossip protocol. We propose a novel cost function for subspace learning on the Grassmann manifold, which is a weighted sum of several sub-problems (each solved by an agent) and the communication cost among the agents. The cost function has a finite-sum structure. In the proposed modeling approach, different agents learn individual local subspaces but they achieve asymptotic consensus on the global learned subspace. The approach is scalable and parallelizable. Numerical experiments show the efficacy of the proposed decentralized algorithms on various matrix completion and multivariate regression benchmarks.

/pdf/a-riemannian-gossip-approach-to-subspace-learning-on-4dlxj6ou77.pdf

A Riemannian gossip approach to subspace learning on Grassmann manifold

Microblogging websites such as Twitter are increasingly being used by businesses/campaigners for timely dissemination of information to their followers. The diffusion of a tweet depends on several factors: the activity of the follower nodes, the responsiveness of follower nodes to tweets from the source node, the out-degree of the follower nodes, the content of recent related tweets seen by the follower node, etc. Using such factors, in this paper, we propose a framework to measure the effectiveness of an information campaign over Twitter. We consider a positive as well as a negative metric to measure the impact of a tweet: while retweets are used to measure the positive impact, the lack of a timely response from an active follower node is taken as a potential negative impact. We investigate the scheduling of tweets to increase the net positive impact while keeping the net negative impact below a desired level. We propose and study several scheduling algorithms by casting the problem in a Markov Decision Process (MDP) framework. In order to compare our algorithms, we estimate the model parameters from tweet data collected using the Twitter API from an arbitrarily selected node and its 6837 followers over several months. For this dataset, we find that if successive tweets in the campaign are novel, then substantial gains over user activity based scheduling can be obtained by scheduling tweets in time slots where the ratio of the expected positive and negative metrics is high. We call this the MaxRatio policy and we show that it is optimal under certain conditions. In cases where we are not certain about the response of users to successive related tweets, we identify another algorithm (which we call MaxReach) as a robust alternative.

https://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/viewFile/2771/3297

Atul Saroop

Papers

Recommending Product Sizes to Customers

Bayesian Models for Product Size Recommendations

A Riemannian gossip approach to subspace learning on Grassmann manifold

Timing Tweets to Increase Effectiveness of Information Campaigns.

On the diffusion of messages in on-line social networks