Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers

The popularity of e-commerce platforms continues to grow. Being able to understand, and predict customer behavior is essential for customizing the user experience through personalized result presentations, recommendations, and special offers. Previous work has considered a broad range of prediction models as well as features inferred from clickstream data to record session characteristics, and features inferred from user data to record customer characteristics. So far, most previous work in the area of purchase prediction has focused on known customers, largely ignoring anonymous sessions, i.e., sessions initiated by a non-logged-in or unrecognized customer. However, in the de-identified data from a large European e-commerce platform available to us, more than 50% of the sessions start as anonymous sessions. In this paper, we focus on purchase prediction for both anonymous and identified sessions on an e-commerce platform. We start with a descriptive analysis of purchase vs. non-purchase sessions. This analysis informs the definition of a feature-based model for purchase prediction for anonymous sessions and identified sessions; our models consider a range of session-based features for anonymous sessions, such as the channel type, the number of visited pages, and the device type. For identified user sessions, our analysis points to customer history data as a valuable discriminator between purchase and non-purchase sessions. Based on our analysis, we build two types of predictors: (1) a predictor for anonymous that beats a production-ready predictor by over 17.54% F1; and (2) a predictor for identified customers that uses session data as well as customer history and achieves an F1 of 96.20%. Finally, we discuss the broader practical implications of our findings.Comment: 10 pages, accepted at SIGIR eCommerce 202

Staphylococcus aureus enterotoxin b down-regulates the expression of transforming growth factor-beta (TGF-β) signaling transducers in human glioblastoma

Background: It has been revealed that Staphylococcus aureus enterotoxin B (SEB) may feature anti-cancer and anti-metastatic advantages due to its ability to modify cell immunity processes and signaling pathways. Glioblastoma is one of the most aggressive human cancers; it has a high mortality nature, which makes it an attractive area for the development of novel therapies. Objectives: We examined whether the SEB could exert its growth inhibitory effects on glioblastoma cells partially through the manipulation of a key tumor growth factor termed transforming growth factor-beta (TGF-β). Materials and Methods: A human primary glioblastoma cell line, U87, was treated with different concentrations of SEB. The cell quantity was measured by the MTT assay at different exposure times. For molecular assessments, total ribonucleic acid (RNA) was extracted from either non-treated or SEB-treated cells. Subsequently, the gene expression of TGF-β transducers, smad2/3, at the messenger RNA (mRNA) level, was analyzed via a quantitative real-time polymerase chain reaction (qPCR) using the SYBR Green method. Significant differences between cell viability and gene expression levels were determined (Prism 5.0 software) using a one-way analysis of variance (ANOVA) test. Results: We reported that SEB could effectively down-regulate smad2/3 expression in glioblastoma cells at concentrations as quantity as 1 µg/mL and 2 µg/mL (P < 0.05 and P < 0.01, respectively). The SEB concentrations effective at regulating smad2/3 expression were correlated with those used to inhibit the proliferation of glioblastoma cells. Our results also showed that SEB was able to decrease smad2/3 expression at the mRNA level in a concentration- and time-dependent manner. Conclusions: We suggested that SEB could represent an agent that can significantly decrease smad2/3 expression in glioblastoma cells, leading to moderate TGF-β growth signaling and the reduction of tumor cell proliferation. © 2016, Ahvaz Jundishapur University of Medical Sciences

Geometric Phase, Curvature, and Extrapotentials in Constrained Quantum Systems

We derive an effective Hamiltonian for a quantum system constrained to a submanifold (the constraint manifold) of configuration space (the ambient space) by an infinite restoring force. We pay special attention to how this Hamiltonian depends on quantities which are external to the constraint manifold, such as the external curvature of the constraint manifold, the (Riemannian) curvature of the ambient space, and the constraining potential. In particular, we find the remarkable fact that the twisting of the constraining potential appears as a gauge potential in the constrained Hamiltonian. This gauge potential is an example of geometric phase, closely related to that originally discussed by Berry. The constrained Hamiltonian also contains an effective potential depending on the external curvature of the constraint manifold, the curvature of the ambient space, and the twisting of the constraining potential. The general nature of our analysis allows applications to a wide variety of problems, such as rigid molecules, the evolution of molecular systems along reaction paths, and quantum strip waveguides.Comment: 27 pages with 1 figure, submitted to Phys. Rev.

Inflammation and breast cancer. Inflammatory component of mammary carcinogenesis in ErbB2 transgenic mice

This review addresses genes differentially expressed in the mammary gland transcriptome during the progression of mammary carcinogenesis in BALB/c mice that are transgenic for the rat neu (ERBB2, or HER-2/neu) oncogene (BALB-neuT664V-E mice). The Ingenuity knowledge database was used to characterize four functional association networks whose hub genes are directly linked to inflammation (specifically, the genes encoding IL-1β, tumour necrosis factor, interferon-γ, and monocyte chemoattractant protein-1/CC chemokine ligand-2) and are increasingly expressed during such progression. In silico meta-analysis in a human breast cancer dataset suggests that proinflammatory activation in the mammary glands of these mice reflects a general pattern of human breast cancer

Understanding Multi-Channel Customer Behavior in Retail

Online shopping is gaining popularity. Traditional retailers with physical stores adjust to this trend by allowing their customers to shop online as well as offline, in-store. Increasingly, customers can browse and purchase products across multiple shopping channels. Understanding how customer behavior relates to the availability of multiple shopping channels is an important prerequisite for many downstream machine learning tasks, such as recommendation and purchase prediction. However, previous work in this domain is limited to analyzing single-channel behavior only. In this paper, we provide the first insights into multi-channel customer behavior in retail based on a large sample of 2.8 million transactions originating from 300,000 customers of a food retailer in Europe. Our analysis reveals significant differences in customer behavior across online and offline channels, for example with respect to the repeat ratio of item purchases and basket size. Based on these findings, we investigate the performance of a next basket recommendation model under multi-channel settings. We find that the recommendation performance differs significantly for customers based on their choice of shopping channel, which strongly indicates that future research on recommenders in this area should take into account the particular characteristics of multi-channel retail shopping