5,875 research outputs found
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
Learning Personalized End-to-End Goal-Oriented Dialog
Most existing works on dialog systems only consider conversation content
while neglecting the personality of the user the bot is interacting with, which
begets several unsolved issues. In this paper, we present a personalized
end-to-end model in an attempt to leverage personalization in goal-oriented
dialogs. We first introduce a Profile Model which encodes user profiles into
distributed embeddings and refers to conversation history from other similar
users. Then a Preference Model captures user preferences over knowledge base
entities to handle the ambiguity in user requests. The two models are combined
into the Personalized MemN2N. Experiments show that the proposed model achieves
qualitative performance improvements over state-of-the-art methods. As for
human evaluation, it also outperforms other approaches in terms of task
completion rate and user satisfaction.Comment: Accepted by AAAI 201
Anticipating Information Needs Based on Check-in Activity
In this work we address the development of a smart personal assistant that is
capable of anticipating a user's information needs based on a novel type of
context: the person's activity inferred from her check-in records on a
location-based social network. Our main contribution is a method that
translates a check-in activity into an information need, which is in turn
addressed with an appropriate information card. This task is challenging
because of the large number of possible activities and related information
needs, which need to be addressed in a mobile dashboard that is limited in
size. Our approach considers each possible activity that might follow after the
last (and already finished) activity, and selects the top information cards
such that they maximize the likelihood of satisfying the user's information
needs for all possible future scenarios. The proposed models also incorporate
knowledge about the temporal dynamics of information needs. Using a combination
of historical check-in data and manual assessments collected via crowdsourcing,
we show experimentally the effectiveness of our approach.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM '17), 201
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
On most sponsored search platforms, advertisers bid on some keywords for
their advertisements (ads). Given a search request, ad retrieval module
rewrites the query into bidding keywords, and uses these keywords as keys to
select Top N ads through inverted indexes. In this way, an ad will not be
retrieved even if queries are related when the advertiser does not bid on
corresponding keywords. Moreover, most ad retrieval approaches regard rewriting
and ad-selecting as two separated tasks, and focus on boosting relevance
between search queries and ads. Recently, in e-commerce sponsored search more
and more personalized information has been introduced, such as user profiles,
long-time and real-time clicks. Personalized information makes ad retrieval
able to employ more elements (e.g. real-time clicks) as search signals and
retrieval keys, however it makes ad retrieval more difficult to measure ads
retrieved through different signals. To address these problems, we propose a
novel ad retrieval framework beyond keywords and relevance in e-commerce
sponsored search. Firstly, we employ historical ad click data to initialize a
hierarchical network representing signals, keys and ads, in which personalized
information is introduced. Then we train a model on top of the hierarchical
network by learning the weights of edges. Finally we select the best edges
according to the model, boosting RPM/CTR. Experimental results on our
e-commerce platform demonstrate that our ad retrieval framework achieves good
performance
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