Personalization in LLMs often relies on costly human feedback or interaction logs, limiting scalability and neglecting deeper user attributes. To reduce the reliance on human annotations, we introduce GRAVITY (Generative Response with Aligned Values, Interests, and Traits of You), a framework for generating synthetic, profile-grounded preference data that captures users' interests, values, beliefs, and personality traits. By integrating demographic, cultural, and psychological frameworks -- including Hofstede's cultural dimensions, Schwartz's basic values, the World Values Survey, and Big Five OCEAN traits -- GRAVITY synthesizes preference pairs to guide personalized content generation. We evaluate GRAVITY on book descriptions for 400 Amazon users, comparing it to prompt-based conditioning, standard fine-tuning, and naive synthetic pair generation. Profile-grounded synthetic data consistently improves generation, especially across multiple cultures (USA, Brazil, Japan, India), achieving over 4% higher preference gains across baselines, with user studies showing that GRAVITY outputs are preferred over 86% of the time. Our results show that scenario-grounded synthetic data can capture richer user variation, reduce reliance on costly annotation, and produce more engaging, user-centered content, offering a scalable path for LLM personalization.

The affective attitude of liking a recommended item reflects just one category in a wide spectrum of affective phenomena that also includes emotions such as entranced or intrigued, moods such as cheerful or buoyant, as well as more fine-grained affective states, such as "pleasantly surprised by the conclusion". In this paper, we introduce a novel recommendation task that can leverage a virtually unbounded range of affective states sought explicitly by the user in order to identify items that, upon consumption, are likely to induce those affective states. Correspondingly, we create a large dataset of user preferences containing expressions of fine-grained affective states that are mined from book reviews, and propose a Transformer-based architecture that leverages such affective expressions as input. We then use the resulting dataset of affective states preferences, together with the linked users and their histories of book readings, ratings, and reviews, to train and evaluate multiple recommendation models on the task of matching recommended items with affective preferences. Experiments show that the best results are obtained by models that can utilize textual descriptions of items and user affective preferences.

In my previous article, I have written about a content-based recommendation engine using TF-IDF for Goodreads data. In this article, I am using the same Goodreads data and build the recommendation engine using word2vec. Like the previous article, I am going to use the same book description to recommend books. The algorithm that we use always struggles to handle raw text data and it only understands the data in numeric form. In order to make it understand, we need to convert the raw text into numeric form.

Our last book list of 2018 focused on the leadership skills that IT and business executives would need to guide their organizations into the future of technology. In this list, we focus on what that future may entail. Whether you are more interested in the here and now – like how to make agile and DevOps work – or emerging technology predictions for humanity and business in the age of AI, algorithms, and robots, there's a book on this list for you. Book description (via Amazon): "How will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances? "Architects of Intelligence" contains a series of in-depth, one-to-one interviews where New York Times bestselling author, Martin Ford, uncovers the truth behind these questions from some of the brightest minds in the Artificial Intelligence community."

If you are still taking a "wait and see" approach to artificial intelligence, you may already be falling behind your peers in IT. Although just 15 percent of enterprises are using AI, 31 percent said that it was on the agenda for the next 12 months, according to Adobe's February 2018 "Digital Trends" report. That number will only go up as machine learning and AI technology continues to change how we work, live, and think. Along these lines, Mary Meeker's 2018 Internet trends report cited AI as a hot growth area for IT spending. There is plenty of hype-filled speculation about what the future of AI will look like.