Navigating vast e-commerce websites with extensive product cata- logs can be a daunting challenge for shoppers. To assist customers in finding the products they desire, e-commerce platforms provide product attribute filters, commonly referred to as "refinements." These refinements serve as a vital navigational aid, enabling cus- tomers to refine their search results based on specific product at- tributes such as material, color, size, brand, etc. 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 recommenda- tion systems. In the work, we discuss the evolution of refinement recommendations strategies i.e, a) search query-based classifica- tion approach, for a given search query we train a classification model, with the refinements as labels b) session-based classification approach, for the given sequence of session interactions we train a sequence classification model, with the refinements as labels and c) session-based generation approach, with the sequence of session interactions as input and output as the refinement name.

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.

Despite possessing the capacity for selective attention, we often fail to notice the obvious. We investigated participants' (n = 39) failures to detect salient changes in a change blindness experiment. Surprisingly, change detection success varied by over two-fold across participants. These variations could not be readily explained by differences in scan paths or fixated visual features. Yet, two simple gaze metrics-mean duration of fixations and the variance of saccade amplitudes-systematically predicted change detection success. We explored the mechanistic underpinnings of these results with a neurally-constrained model based on the Bayesian framework of sequential probability ratio testing, with a posterior odds-ratio rule for shifting gaze. The model's gaze strategies and success rates closely mimicked human data. Moreover, the model outperformed a state-of-the-art deep neural network (DeepGaze II) with predicting human gaze patterns in this change blindness task. Our mechanistic model reveals putative rational observer search strategies for change detection during change blindness, with critical real-world implications.