Personalized product search aims to retrieve and rank items that match users' preferences and search intent. Despite their effectiveness, existing approaches typically assume that users' query fully captures their real motivation. However, our analysis of a real-world e-commerce platform reveals that users often engage in relevant consultations before searching, indicating they refine intents through consultations based on motivation and need. The implied motivation in consultations is a key enhancing factor for personalized search. This unexplored area comes with new challenges including aligning contextual motivations with concise queries, bridging the category-text gap, and filtering noise within sequence history. To address these, we propose a Motivation-Aware Personalized Search (MAPS) method. It embeds queries and consultations into a unified semantic space via LLMs, utilizes a Mixture of Attention Experts (MoAE) to prioritize critical semantics, and introduces dual alignment: (1) contrastive learning aligns consultations, reviews, and product features; (2) bidirectional attention integrates motivation-aware embeddings with user preferences. Extensive experiments on real and synthetic data show MAPS outperforms existing methods in both retrieval and ranking tasks.

Personalized search represents a problem where retrieval models condition on historical user interaction data in order to improve retrieval results. However, personalization is commonly perceived as opaque and not amenable to control by users. Further, personalization necessarily limits the space of items that users are exposed to. Therefore, prior work notes a tension between personalization and users' ability for discovering novel items. While discovery of novel items in personalization setups may be resolved through search result diversification, these approaches do little to allow user control over personalization. Therefore, in this paper, we introduce an approach for controllable personalized search. Our model, CtrlCE presents a novel cross-encoder model augmented with an editable memory constructed from users historical items. Our proposed memory augmentation allows cross-encoder models to condition on large amounts of historical user data and supports interaction from users permitting control over personalization. Further, controllable personalization for search must account for queries which don't require personalization, and in turn user control. For this, we introduce a calibrated mixing model which determines when personalization is necessary. This allows system designers using CtrlCE to only obtain user input for control when necessary. In multiple datasets of personalized search, we show CtrlCE to result in effective personalization as well as fulfill various key goals for controllable personalized search.

It talks about linking the Google Assistant straight from the Google search bar and positioning it as your own personal Google – a way of pointing to personalized search. Google's recent announcement tells more about what the Google Assistant can do for you, emphasizing the number of models supported and how it makes over a million Actions possible. Google seems to be going beyond its original plans for Assistant and putting effort into expanding its powerful natural language search capabilities. The competitive arena of voice and personalized search has several major challengers like Amazon, Microsoft, and Apple, each vying for dominance of the smart assistant and smart device market. At this time, Google continues to depend on structured data markup and AMP to assist in providing content matches for the Assistant, meaning that search marketers who invest in both may find a welcome competitive edge. Improvements in Voice Interactions with Technology: Enrique Alfonseca, Staff Research Scientist for Google Assistant wrote a December 21, 2017 article titled the Evaluation of Speech for the Google Assistant. As Google often does, it clued publishers, webmasters, and business owners into new digital marketing changes ahead of time, especially as the mobile-first world comes alive. The information is helpful as search engine optimization spends offer the best ROI is SEO's remain fluid and adjust to maximize new opportunities. Industry wide adoption: Yesterday, January 10, 2017, DISH unveiled its compatibility with the Google Assistant.

Personalized recommendations have become a common feature of many modern online services. In particular on e-commerce sites, one value of such recommendations is that they help consumers find items of interest in large product assortments more quickly. Many of today's sites take advantage of modern recommendation technologies to create personalized item suggestions for consumers navigating the site. However, limited research exists on the use of personalization and recommendation technology when consumers rely on the site's catalog search functionality to discover relevant items. In this work we explore the value of personalizing search results on e-commerce sites using recommendation technology. We design and evaluate different personalization strategies using log data of an online retail site. Our results show that considering several item relevance signals within the recommendation process in parallel leads to the best ranking of the search results. Specifically, the factors taken into account include the users' general interests, their most recent browsing behavior, as well as the consideration of current sales trends.