In online businesses, personalization of site content is crucial for providing a better user experience and increasing customer engagement. Machine learning algorithms are often used to analyze customer data such as browsing behavior, purchase history to tailor the website content to each individual customer's preferences and needs. However, measuring the success of these personalized experiences can be challenging. While the ultimate goal is to convert customer visits into purchase sessions, tracking individual customer behavior can be difficult at scale. As a result, businesses often rely on aggregate metrics such as site-wide conversion rates, sales, and revenue to evaluate the effectiveness of their personalization efforts. However, it's important to understand individual customers' experiences with these ML-optimized pages. To address this, we propose a supervised ML model that quantifies customer engagement while browsing auto-optimized web pages, by building a customer site interaction score (CSI score). We first introduce a novel representation of customer click logs as a tree data structure induced by the webpage's DOM structure. Then, we propose a novel attention model on the tree structure that performs vertical attention across the depth of the tree and horizontal attention across the sequence of trees to summarize customer interactions. The effectiveness of the proposed approach is evaluated using click logs obtained from the e-commerce domain.

Finding the right product on e-commerce websites with millions of products is a daunting task for a large set of customers. On the search page, product attribute filters a.k.a. "refinements" emerge as a convenient navigational option for customers to narrow down the search results along product attributes of their choice (e.g., Material:Cotton, Color:Black for 'shirt'). However, on mobile devices, refinements are not easily discoverable due to lack of screen space. To improve discoverability, contextually relevant refinements are suggested in-line on search page by refinement recommendation systems. Existing works on refinement recommendations primarily rely on the search context as input, and are trained using aggregated refinement preferences 'explicitly' expressed by customers. These solutions fail to capture 'implicit' preferences expressed during the customer shopping mission through in-session browsing activity. In this paper, we propose a session-based recommendation system (SBRS) which recommends refinements by inferring product attribute preferences of customers based on the sequence of products viewed earlier in the session. For the task of refinement recommendation, we propose a) AttriBERT, a model which extends BERT architecture to learn from the attribute values of products and b) a novel product representation strategy, which represents each product as a dictionary of attribute:value pairs (e.g., RAM Size:64GB). We evaluate our approach on RecSys 2022 Challenge and Amazon e-commerce datasets. Our approach consistently outperforms various state-of-the-art sequence models on the task of session-based refinement recommendation.