Generating Personalized Recommendations via Large Language Models (LLMs)

Personalized recommendations used in many applications and websites are generated using techniques such as collaborative filtering, content-based filtering, reinforcement learning, etc. These are task-specific approaches. Large language models (LLMs) can generate predictions based on priming with specific input without the need for task-specific model tuning. However, LLMs have not been applied for making personalized recommendations because their maximum input size is smaller than the typical size of user histories used to personalize recommendations. This disclosure describes techniques to obtain personalized recommendations via LLMs by automatically augmenting a user command or query with relevant text phrases about the user. The set of relevant phrases that fit within the input limits of the LLM are extracted from a collection of phrases obtained from relevant historical and contextual information sources based on the embeddings generated based on the user command or query. Implementation of the techniques can improve the relevance and utility of personalized recommendations and can lead to increased user engagement with the recommended content

Local Detection of Topical Entities Using Machine Learning

Computer-implemented systems and methods for determining topics of displayed content are provided while maintaining user data privacy and security. Entity identification and topic determination models may be stored within a user computing device such that the user computing device may perform topic detection of content presently displayed on the user computing device to maintain user data privacy. Once a topic(s) is determined from the content, features within the user computing device may be enabled or tailored to a user based on the content being displayed

Leveraging Large Language Models in Conversational Recommender Systems

A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an unprecedented ability to converse naturally and incorporate world knowledge and common-sense reasoning into language understanding, unlocking the potential of this paradigm. However, effectively leveraging LLMs within a CRS introduces new technical challenges, including properly understanding and controlling a complex conversation and retrieving from external sources of information. These issues are exacerbated by a large, evolving item corpus and a lack of conversational data for training. In this paper, we provide a roadmap for building an end-to-end large-scale CRS using LLMs. In particular, we propose new implementations for user preference understanding, flexible dialogue management and explainable recommendations as part of an integrated architecture powered by LLMs. For improved personalization, we describe how an LLM can consume interpretable natural language user profiles and use them to modulate session-level context. To overcome conversational data limitations in the absence of an existing production CRS, we propose techniques for building a controllable LLM-based user simulator to generate synthetic conversations. As a proof of concept we introduce RecLLM, a large-scale CRS for YouTube videos built on LaMDA, and demonstrate its fluency and diverse functionality through some illustrative example conversations

Discovering Predictors of Mental Health Service Utilization with k-support Regularized Logistic Regression

International audienceMany epidemiological studies are undertaken with a use of large epidemiological databases, which involves the simultaneous evaluation of a large number of variables. Epidemiologists face a number of problems when dealing with large data sets: multicolinearity (when variables are correlated to each other), confounding factors (when risk factor is correlated with both exposure and outcome variable), and interactions (when the direction or magnitude of an association between two variables differs due to the effect of a third variable). Correct variable selection helps to address these issues and helps to obtain unbiased results. Selection of relevant variables is a complicated and a time consuming task. Flawed variable selection methods still prevail in the scientific literature; there is a need to demonstrate the usability of new algorithms using real data. In this paper we propose to use a novel machine learning method, k-support regularized logistic regression, for discovering predictors of mental health service utilization in the National Epidemiologic Survey for Alcohol and Related Conditions (NESARC). We show that k-support regularized logistic regression yields better prediction accuracy than 1 or 2 regularized logistic regression as well as several baseline methods on this task, and we qualitatively evaluate the top weighted variates. The selected variables are supported by related epidemiological research, and give important cues for public policy

Discovering Predictors of Mental Health Service Utilization with k-support Regularized Logistic Regression

© 2015 Published by Elsevier B.V. Many epidemiological studies are undertaken with a use of large epidemiological databases, which involves the simultaneous evaluation of a large number of variables. Epidemiologists face a number of problems when dealing with large data sets: multicolinearity (when variables are correlated to each other), confounding factors (when risk factor is correlated with both exposure and outcome variable), and interactions (when the direction or magnitude of an association between two variables differs due to the effect of a third variable). Correct variable selection helps to address these issues and helps to obtain unbiased results. Selection of relevant variables is a complicated and a time consuming task. Flawed variable selection methods still prevail in the scientific literature; there is a need to demonstrate the usability of new algorithms using real data. In this paper we propose to use a novel machine learning method, k-support regularized logistic regression, for discovering predictors of mental health service utilization in the National Epidemiologic Survey for Alcohol and Related Conditions (NESARC). We show that k-support regularized logistic regression yields better prediction accuracy than ℓ1 or ℓ2 regularized logistic regression as well as several baseline methods on this task, and we qualitatively evaluate the top weighted variates. The selected variables are supported by related epidemiological research, and give important cues for public policy.publisher: Elsevier articletitle: Discovering predictors of mental health service utilization with k-support regularized logistic regression journaltitle: Information Sciences articlelink: http://dx.doi.org/10.1016/j.ins.2015.03.069 content_type: article copyright: Copyright © 2015 Elsevier Inc. All rights reserved.status: publishe