79,817 research outputs found
Personalized Emphasis Framing for Persuasive Message Generation
In this paper, we present a study on personalized emphasis framing which can
be used to tailor the content of a message to enhance its appeal to different
individuals. With this framework, we directly model content selection decisions
based on a set of psychologically-motivated domain-independent personal traits
including personality (e.g., extraversion and conscientiousness) and basic
human values (e.g., self-transcendence and hedonism). We also demonstrate how
the analysis results can be used in automated personalized content selection
for persuasive message generation
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Evaluation of a personalized digital library based on cognitive styles: Adaptivity vs. adaptability
Personalization can be addressed by adaptability and adaptivity, which have different advantages and disadvantages. This study investigates how digital library users react to these two techniques. More specifically, we develop a
personalized digital library to suit the needs of different cognitive styles based on the findings of our previous work (Frias-Martinez, et al., in press). The personalized digital library includes two versions: adaptive version and
adaptable version. The results showed that users not only performed better in the adaptive version, but also they perceived more positively to the adaptive version. In addition, cognitive styles have great effects on users’ responses
to adaptability and adaptivity. These results provide guidance for designers to select suitable techniques to develop personalized digital libraries
Context Models For Web Search Personalization
We present our solution to the Yandex Personalized Web Search Challenge. The
aim of this challenge was to use the historical search logs to personalize
top-N document rankings for a set of test users. We used over 100 features
extracted from user- and query-depended contexts to train neural net and
tree-based learning-to-rank and regression models. Our final submission, which
was a blend of several different models, achieved an NDCG@10 of 0.80476 and
placed 4'th amongst the 194 teams winning 3'rd prize
Assessing the Effectiveness and Usability of Personalized Internet Search through a Longitudinal Evaluation
This paper discusses a longitudinal user evaluation of Prospector, a personalized Internet meta-search engine capable of personalized re-ranking of search results. Twenty-one participants used Prospector as their primary search engine for 12 days, agreed to have their interaction with the system logged, and completed three questionnaires. The data logs show that the personalization provided by Prospector is successful: participants preferred re-ranked results that appeared higher up. However, the questionnaire results indicated that people would prefer to use Google instead (their search engine of choice). Users would, nevertheless, consider employing a personalized search engine to perform searches with terms that require disambiguation and/or contextualization. We conclude the paper with a discussion on the merit of combining system- and user-centered evaluation for the case of personalized systems
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
Session-based recommendations are highly relevant in many modern on-line
services (e.g. e-commerce, video streaming) and recommendation settings.
Recently, Recurrent Neural Networks have been shown to perform very well in
session-based settings. While in many session-based recommendation domains user
identifiers are hard to come by, there are also domains in which user profiles
are readily available. We propose a seamless way to personalize RNN models with
cross-session information transfer and devise a Hierarchical RNN model that
relays end evolves latent hidden states of the RNNs across user sessions.
Results on two industry datasets show large improvements over the session-only
RNNs
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The role of human factors in stereotyping behavior and perception of digital library users: A robust clustering approach
To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception
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