Using NLP for machine learning of user profiles

Bloedorn, Eric; Mani, Inderjeet

doi:10.1016/s1088-467x(98)00003-1

Cited by 12 publications

(6 citation statements)

References 32 publications

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“…User profiling by user behavior has had a long history in many different contexts. Previous attempts at big data user profiling without deep neural networks have leaned upon external knowledge in the form of ontologies [50] and presented a general framework for using NLP in profiling [14].…”

Section: User Profiling With Nlpmentioning

confidence: 99%

Word Embeddings for User Profiling in Online Social Networks

Alekseev¹,

Nikolenko²

2017

CyS

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User profiling in social networks can be significantly augmented by using available full-text items such as posts or statuses and ratings (in the form of likes) that users give them. In this work, we apply modern natural language processing techniques based on word embeddings to several problems related to user profiling in social networks. First, we present an approach to create user profiles that measure a user's interest in various topics mined from the full texts of the items. As a result, we get a user profile that can be used, e.g., for cold start recommendations for items, targeted advertisement, and other purposes; our experiments show that the interests mining method performs on a level comparable with collaborative algorithms while at the same time being a cold start approach, i.e., it does not use the likes of an item being recommended. Second, we study the problem of predicting a user's demographic attributes such as age and gender based on his or her full-text items. We evaluate the efficiency of various age prediction algorithms based on word2vec word embeddings and conduct an extensive experimental evaluation, comparing these algorithms with each other and with classical baseline approaches.

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Section: User Profiling With Nlpmentioning

confidence: 99%

Word Embeddings for User Profiling in Online Social Networks

Alekseev¹,

Nikolenko²

2017

CyS

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“…Aunque algunos investigadores han utilizado tecnologías de procesamiento del lenguaje natural para elaborar un perfil del usuario, solo las han aplicado a dominios restringidos que utilizan WordNet o recursos más antiguos, y principalmente se han centrado en los problemas de recuperación y de la generalización de conceptos para responder proactivamente a las exigencias del usuario (Bloedorn & Mani, 1998).…”

Section: Agentes De Búsqueda Inteligentesunclassified

“…De tal modo que las limitaciones de búsquedas basadas en palabras clave se pueden superar si se utilizan sistemas de diálogos en lenguaje natural entre el usuario y el motor de búsqueda (Bloedorn & Mani, 1998). En este aspecto, el estudio que aquí presentamos propone un modelo de diálogo hombre máquina (del inglés Human Computer Interactions) en el cual la retroalimentación (del inglés feedback) que se da en interacciones dialógicas en lenguaje natural puede desempeñar un rol clave en la reducción de la sobrecarga de información y la obtención de información precisa sobre lo que el usuario realmente busca.…”

Section: Introductionunclassified

Disminución de la sobrecarga de información en la World Wide Web a partir de interacciones dialógicas hombre-computador

Cabrera

Atkinson

2009

Rev. signos

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“…In several systems, which perform learning processes in order to update the user profiles, mathematical formulas are used for the adaptation of the different weight values [3,9]. In our approach, the values contained in the Weights Vector T , is updated according to a formula depending on whether the user selects or ignores news items that contain the term.…”

Section: Weights Adaptationmentioning

confidence: 99%

Two-Level Automatic Adaptation of a Distributed User Profile for Personalized News Content Delivery

Papadogiorgaki

Papastathis

Nidelkou

et al. 2008

International Journal of Digital Multimedia Broadcasting

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This paper presents a distributed client-server architecture for the personalized delivery of textual news content to mobile users. The user profile consists of two separate models, that is, the long-term interests are stored in a skeleton profile on the server and the short-term interests in a detailed profile in the handset. The user profile enables a high-level filtering of available news content on the server, followed by matching of detailed user preferences in the handset. The highest rated items are recommended to the user, by employing an efficient ranking process. The paper focuses on a two-level learning process, which is employed on the client side in order to automatically update both user profile models. It involves the use of machine learning algorithms applied to the implicit and explicit user feedback. The system's learning performance has been systematically evaluated based on data collected from regular system users.

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Using NLP for machine learning of user profiles

Cited by 12 publications

References 32 publications

Word Embeddings for User Profiling in Online Social Networks

Word Embeddings for User Profiling in Online Social Networks

Disminución de la sobrecarga de información en la World Wide Web a partir de interacciones dialógicas hombre-computador

Two-Level Automatic Adaptation of a Distributed User Profile for Personalized News Content Delivery

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