1,783 research outputs found
Modélisation des comportements de recherche basé sur les interactions des utilisateurs
Les utilisateurs de systèmes d'information divisent normalement les tâches en une séquence de plusieurs étapes pour les résoudre. En particulier, les utilisateurs divisent les tâches de recherche en séquences de requêtes, en interagissant avec les systèmes de recherche pour mener à bien le processus de recherche d'informations. Les interactions des utilisateurs sont enregistrées dans des journaux de requêtes, ce qui permet de développer des modèles pour apprendre automatiquement les comportements de recherche à partir des interactions des utilisateurs avec les systèmes de recherche. Ces modèles sont à la base de multiples applications d'assistance aux utilisateurs qui aident les systèmes de recherche à être plus interactifs, faciles à utiliser, et cohérents. Par conséquent, nous proposons les contributions suivantes : un modèle neuronale pour apprendre à détecter les limites des tâches de recherche dans les journaux de requête ; une architecture de regroupement profond récurrent qui apprend simultanément les représentations de requête et regroupe les requêtes en tâches de recherche ; un modèle non supervisé et indépendant d'utilisateur pour l'identification des tâches de recherche prenant en charge les requêtes dans seize langues ; et un modèle de tâche de recherche multilingue, une approche non supervisée qui modélise simultanément l'intention de recherche de l'utilisateur et les tâches de recherche. Les modèles proposés améliorent les méthodes existantes de modélisation, en tenant compte de la confidentialité des utilisateurs, des réponses en temps réel et de l'accessibilité linguistique. Le respect de la vie privée de l'utilisateur est une préoccupation majeure, tandis que des réponses rapides sont essentielles pour les systèmes de recherche qui interagissent avec les utilisateurs en temps réel, en particulier dans la recherche par conversation. Dans le même temps, l'accessibilité linguistique est essentielle pour aider les utilisateurs du monde entier, qui interagissent avec les systèmes de recherche dans de nombreuses langues. Les contributions proposées peuvent bénéficier à de nombreuses applications d'assistance aux utilisateurs, en aidant ces derniers à mieux résoudre leurs tâches de recherche lorsqu'ils accèdent aux systèmes de recherche pour répondre à leurs besoins d'information.Users of information systems normally divide tasks in a sequence of multiple steps to solve them. In particular, users divide search tasks into sequences of queries, interacting with search systems to carry out the information seeking process. User interactions are registered on search query logs, enabling the development of models to automatically learn search patterns from the users' interactions with search systems. These models underpin multiple user assisting applications that help search systems to be more interactive, user-friendly, and coherent. User assisting applications include query suggestion, the ranking of search results based on tasks, query reformulation analysis, e-commerce applications, retrieval of advertisement, query-term prediction, mapping of queries to search tasks, and so on. Consequently, we propose the following contributions: a neural model for learning to detect search task boundaries in query logs; a recurrent deep clustering architecture that simultaneously learns query representations through self-training, and cluster queries into groups of search tasks; Multilingual Graph-Based Clustering, an unsupervised, user-agnostic model for search task identification supporting queries in sixteen languages; and Language-agnostic Search Task Model, an unsupervised approach that simultaneously models user search intent and search tasks. Proposed models improve on existing methods for modeling user interactions, taking into account user privacy, realtime response times, and language accessibility. User privacy is a major concern in Ethics for intelligent systems, while fast responses are critical for search systems interacting with users in realtime, particularly in conversational search. At the same time, language accessibility is essential to assist users worldwide, who interact with search systems in many languages. The proposed contributions can benefit many user assisting applications, helping users to better solve their search tasks when accessing search systems to fulfill their information needs
Attend to You: Personalized Image Captioning with Context Sequence Memory Networks
We address personalization issues of image captioning, which have not been
discussed yet in previous research. For a query image, we aim to generate a
descriptive sentence, accounting for prior knowledge such as the user's active
vocabularies in previous documents. As applications of personalized image
captioning, we tackle two post automation tasks: hashtag prediction and post
generation, on our newly collected Instagram dataset, consisting of 1.1M posts
from 6.3K users. We propose a novel captioning model named Context Sequence
Memory Network (CSMN). Its unique updates over previous memory network models
include (i) exploiting memory as a repository for multiple types of context
information, (ii) appending previously generated words into memory to capture
long-term information without suffering from the vanishing gradient problem,
and (iii) adopting CNN memory structure to jointly represent nearby ordered
memory slots for better context understanding. With quantitative evaluation and
user studies via Amazon Mechanical Turk, we show the effectiveness of the three
novel features of CSMN and its performance enhancement for personalized image
captioning over state-of-the-art captioning models.Comment: Accepted paper at CVPR 201
Hybrid Bayesian Eigenobjects: Combining Linear Subspace and Deep Network Methods for 3D Robot Vision
We introduce Hybrid Bayesian Eigenobjects (HBEOs), a novel representation for
3D objects designed to allow a robot to jointly estimate the pose, class, and
full 3D geometry of a novel object observed from a single viewpoint in a single
practical framework. By combining both linear subspace methods and deep
convolutional prediction, HBEOs efficiently learn nonlinear object
representations without directly regressing into high-dimensional space. HBEOs
also remove the onerous and generally impractical necessity of input data
voxelization prior to inference. We experimentally evaluate the suitability of
HBEOs to the challenging task of joint pose, class, and shape inference on
novel objects and show that, compared to preceding work, HBEOs offer
dramatically improved performance in all three tasks along with several orders
of magnitude faster runtime performance.Comment: To appear in the International Conference on Intelligent Robots
(IROS) - Madrid, 201
Efficient Neural Query Auto Completion
Query Auto Completion (QAC), as the starting point of information retrieval
tasks, is critical to user experience. Generally it has two steps: generating
completed query candidates according to query prefixes, and ranking them based
on extracted features. Three major challenges are observed for a query auto
completion system: (1) QAC has a strict online latency requirement. For each
keystroke, results must be returned within tens of milliseconds, which poses a
significant challenge in designing sophisticated language models for it. (2)
For unseen queries, generated candidates are of poor quality as contextual
information is not fully utilized. (3) Traditional QAC systems heavily rely on
handcrafted features such as the query candidate frequency in search logs,
lacking sufficient semantic understanding of the candidate.
In this paper, we propose an efficient neural QAC system with effective
context modeling to overcome these challenges. On the candidate generation
side, this system uses as much information as possible in unseen prefixes to
generate relevant candidates, increasing the recall by a large margin. On the
candidate ranking side, an unnormalized language model is proposed, which
effectively captures deep semantics of queries. This approach presents better
ranking performance over state-of-the-art neural ranking methods and reduces
95\% latency compared to neural language modeling methods. The empirical
results on public datasets show that our model achieves a good balance between
accuracy and efficiency. This system is served in LinkedIn job search with
significant product impact observed.Comment: Accepted at CIKM 202
Trie-NLG: Trie Context Augmentation to Improve Personalized Query Auto-Completion for Short and Unseen Prefixes
Query auto-completion (QAC) aims at suggesting plausible completions for a
given query prefix. Traditionally, QAC systems have leveraged tries curated
from historical query logs to suggest most popular completions. In this
context, there are two specific scenarios that are difficult to handle for any
QAC system: short prefixes (which are inherently ambiguous) and unseen
prefixes. Recently, personalized Natural Language Generation (NLG) models have
been proposed to leverage previous session queries as context for addressing
these two challenges. However, such NLG models suffer from two drawbacks: (1)
some of the previous session queries could be noisy and irrelevant to the user
intent for the current prefix, and (2) NLG models cannot directly incorporate
historical query popularity. This motivates us to propose a novel NLG model for
QAC, Trie-NLG, which jointly leverages popularity signals from trie and
personalization signals from previous session queries. We train the Trie-NLG
model by augmenting the prefix with rich context comprising of recent session
queries and top trie completions. This simple modeling approach overcomes the
limitations of trie-based and NLG-based approaches and leads to
state-of-the-art performance. We evaluate the Trie-NLG model using two large
QAC datasets. On average, our model achieves huge ~57% and ~14% boost in MRR
over the popular trie-based lookup and the strong BART-based baseline methods,
respectively. We make our code publicly available.Comment: Accepted at Journal Track of ECML-PKDD 202
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