Search query variation poses a challenge in e-commerce search, as equivalent search intents can be expressed through different queries with surface-level differences. This paper introduces a framework to recognize and leverage query equivalence to enhance searcher and business outcomes. The proposed approach addresses three key problems: mapping queries to vector representations of search intent, identifying nearest neighbor queries expressing equivalent or similar intent, and optimizing for user or business objectives. The framework utilizes both surface similarity and behavioral similarity to determine query equivalence. Surface similarity involves canonicalizing queries based on word inflection, word order, compounding, and noise words. Behavioral similarity leverages historical search behavior to generate vector representations of query intent. An offline process is used to train a sentence similarity model, while an online nearest neighbor approach supports processing of unseen queries. Experimental evaluations demonstrate the effectiveness of the proposed approach, outperforming popular sentence transformer models and achieving a Pearson correlation of 0.85 for query similarity. The results highlight the potential of leveraging historical behavior data and training models to recognize and utilize query equivalence in e-commerce search, leading to improved user experiences and business outcomes. Further advancements and benchmark datasets are encouraged to facilitate the development of solutions for this critical problem in the e-commerce domain.

In the face of a challenging macroeconomic climate, the UK digital advertising market remains remarkably strong, expected to reach $35.43bn by the end of this year. With advertisers increasingly relying on digital channels for driving brand awareness and sales, platforms like Connected TV (CTV), digital audio and digital out-of-home (DOOH) are picking up a larger slice of the ad spend pie. In contrast to just a few years ago, this investment would have traditionally been allocated to offline media. This is not to say that the industry is without its problems however. The economic situation, amongst other geopolitical pressures, is having an adverse effect on the sector, forcing media planners to think more short-term and reactively.

Data science is an ever-evolving field, and staying on top of the latest trends and technologies is essential for success. As we look ahead to 2023, there are several key areas that data scientists should focus on to stay competitive and advance their careers. In this blog, we will outline a comprehensive roadmap for data scientists to follow in 2023. Before diving into advanced techniques, it's essential to have a strong foundation in the fundamentals of data science. This includes skills such as programming, statistics, and data manipulation.

It's time for Fortune 1000 companies to rethink their investments in data, analytics, and AI. Of course, companies should be investing in these critical business capabilities and differentiators. What they need to take a hard look at is how they're investing, and whether these investments are leading to the kinds of gains and the levels of business value that companies are aspiring to achieve. Responses to a recently released survey of Fortune 1000 and global data and business leaders show that data, analytics, and AI efforts have stalled -- or even backslid. Since 2012, when I launched the survey to investigate organizations' investments in data initiatives, the survey has expanded into related topics such as analytics, AI and machine learning, the role of the Chief Data Officer, and data ethics.

If you've ever gotten a letter from a bank that explained how different financial issues influenced a credit application, you've seen explainable AI at work -- a computer used math and a set of complex formulas to calculate a score and determine whether to approve or deny your application. In making that decision, some data points were either more or less important. Maybe your long history of on-time payments or your low amount of debt contributed to your application's approval. Similarly, explainable AI shows humans how it arrived at a decision by evaluating different inputs in its calculations. While that might sound obscure or only relevant to the most hardcore data people, explainable AI brings significant business advantages that anyone interested in applying AI should consider.

Intelligent solutions enable self-service for both customers and support agents, allowing them to ask questions using their own words as if they were speaking to a person. This shift started with digital devices and the multiple customer engagement channels those devices have enabled. Organizations are required to provide timely, meaningful customer communications and responses to increasingly complicated customer questions. Technology will continue to change how financial organizations operate and engage with increasingly digital-savvy customers, so understanding the various emerging technology solutions is imperative to ensuring loyalty and improving customer satisfaction, while achieving operational efficiency in the post-pandemic era. Today's consumers, especially millennials and Gen Z, want to be self-sufficient.

With the rise of digitization, we're gathering more and more data that, if used to its full potential, will help businesses counter uncertainty and make business outcomes more predictable. Nowadays, companies face countless challenges -- inflation, supply chain delays, natural disasters, and global pandemics. The most valuable part of AI is its ability to take in huge amounts of data and calculate every possible outcome, then make recommendations based on a variety of parameters. It can also offer solutions to lessen these problems without the need for human interference. Combined with a fully integrated end-to-end ERP system, AI can be a critical factor in streamlining business processes.

In Part 1 of the series "Leveraging Agility to Create Economies of Learning Mindset", I discussed the precarious nature of the CDO role given the expectations of building the organization's data and analytic capabilities while simultaneously delivering short-term business impact. CDO Data-to-Business Innovation Dilemma: Deliver meaningful and relevant business outcomes in the short-term while simultaneously and continuously building and transforming the organization's data and analytics assets and capabilities. The key to addressing the CDO Data-to-Business Innovation Dilemma is to view the development of the organization's data and analytics plan as a journey, not an event. But the data and analytics journey is fraught with unknown challenges and new technology and business developments that only surface as the data science and business teams move along the data and analytics journey. Like the movie "Jason and the Argonauts", your data and analytics journey must be prepared for whatever is thrown at them (like that darn skeleton army), and then pivot and adjust accordingly (Figure 1).

This post is brought to you by NVIDIA and CIO. The views and opinions expressed herein are those of the author and do not necessarily represent the views and opinions of NVIDIA. CIOs seeking big wins in high business-impacting areas where there's significant room to improve performance should review their data science, machine learning (ML), and AI projects. A recent IDC report on AI projects in India[1] reported that 30-49% of AI projects failed for about one-third of organizations, and another study from Deloitte casts 50% of respondents' organizational performance in AI as starters or underachievers. That same study found 94% of respondents say AI is critical to success over the next five years. Executives see the AI opportunity for competitive differentiation and are looking for leaders to deliver successful outcomes.

Many banking organizations have a "voice of the customer" program for capturing insights via surveys and such other signals as speech analytics and digital behaviors. Many have also implemented a "voice of the employee" program relying on similar signals. Yet few bring these together in a meaningful way. They are missing an opportunity. Business units with top quartile employee engagement see customer ratings that are 10% higher compared to units in the bottom quartile, according to a Bain & Co. survey from 2016.