15 research outputs found
Personalized Search Via Neural Contextual Semantic Relevance Ranking
Existing neural relevance models do not give enough consideration for query
and item context information which diversifies the search results to adapt for
personal preference. To bridge this gap, this paper presents a neural learning
framework to personalize document ranking results by leveraging the signals to
capture how the document fits into users' context. In particular, it models the
relationships between document content and user query context using both
lexical representations and semantic embeddings such that the user's intent can
be better understood by data enrichment of personalized query context
information. Extensive experiments performed on the search dataset, demonstrate
the effectiveness of the proposed method.Comment: Contextual, Personalization, Search, Semantics, LLM, embeddin
A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
Users may strive to formulate an adequate textual query for their information
need. Search engines assist the users by presenting query suggestions. To
preserve the original search intent, suggestions should be context-aware and
account for the previous queries issued by the user. Achieving context
awareness is challenging due to data sparsity. We present a probabilistic
suggestion model that is able to account for sequences of previous queries of
arbitrary lengths. Our novel hierarchical recurrent encoder-decoder
architecture allows the model to be sensitive to the order of queries in the
context while avoiding data sparsity. Additionally, our model can suggest for
rare, or long-tail, queries. The produced suggestions are synthetic and are
sampled one word at a time, using computationally cheap decoding techniques.
This is in contrast to current synthetic suggestion models relying upon machine
learning pipelines and hand-engineered feature sets. Results show that it
outperforms existing context-aware approaches in a next query prediction
setting. In addition to query suggestion, our model is general enough to be
used in a variety of other applications.Comment: To appear in Conference of Information Knowledge and Management
(CIKM) 201
Why People Search for Images using Web Search Engines
What are the intents or goals behind human interactions with image search
engines? Knowing why people search for images is of major concern to Web image
search engines because user satisfaction may vary as intent varies. Previous
analyses of image search behavior have mostly been query-based, focusing on
what images people search for, rather than intent-based, that is, why people
search for images. To date, there is no thorough investigation of how different
image search intents affect users' search behavior.
In this paper, we address the following questions: (1)Why do people search
for images in text-based Web image search systems? (2)How does image search
behavior change with user intent? (3)Can we predict user intent effectively
from interactions during the early stages of a search session? To this end, we
conduct both a lab-based user study and a commercial search log analysis.
We show that user intents in image search can be grouped into three classes:
Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals
different user behavior patterns under these three intents, such as first click
time, query reformulation, dwell time and mouse movement on the result page.
Based on user interaction features during the early stages of an image search
session, that is, before mouse scroll, we develop an intent classifier that is
able to achieve promising results for classifying intents into our three intent
classes. Given that all features can be obtained online and unobtrusively, the
predicted intents can provide guidance for choosing ranking methods immediately
after scrolling
Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
Users try to articulate their complex information needs during search
sessions by reformulating their queries. To make this process more effective,
search engines provide related queries to help users in specifying the
information need in their search process. In this paper, we propose a
customized sequence-to-sequence model for session-based query suggestion. In
our model, we employ a query-aware attention mechanism to capture the structure
of the session context. is enables us to control the scope of the session from
which we infer the suggested next query, which helps not only handle the noisy
data but also automatically detect session boundaries. Furthermore, we observe
that, based on the user query reformulation behavior, within a single session a
large portion of query terms is retained from the previously submitted queries
and consists of mostly infrequent or unseen terms that are usually not included
in the vocabulary. We therefore empower the decoder of our model to access the
source words from the session context during decoding by incorporating a copy
mechanism. Moreover, we propose evaluation metrics to assess the quality of the
generative models for query suggestion. We conduct an extensive set of
experiments and analysis. e results suggest that our model outperforms the
baselines both in terms of the generating queries and scoring candidate queries
for the task of query suggestion.Comment: Accepted to be published at The 26th ACM International Conference on
Information and Knowledge Management (CIKM2017
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
The Role of the User\u27s Browsing and Query History for Improving MPC-generated Query Suggestions
In this paper we use the user\u27s recent web browsing history in order to provide better query suggestions in an information retrieval system. We have built a Chrome browser plugin that collects each web page visited by a user and submits it to our query suggestion server for indexing, thus building a personal history profile for each user. We then analyze if future queries submitted by a user to the search engine can be predicted from web pages visited by that user inthe past (i.e. his recent browsing history) or from queries submitted by that user in the past (i.e. his recent query history). The contribution of this paper is a method of using this personal history profile for reordering the query suggestions offered by Google when the user searches information on Google, moving query suggestions more relevant to the user\u27s information need to the front positions in the Google provided query suggestions list. We have collected browsing history log data for over 4 months from several users who installed our Chrome plugin on their local computers and then we performed an offline evaluation test that shows that our personalized query suggestion system improves the MRR (i.e. Mean Reciprocal Rank) score of Google query suggestions by approximately 0.04 (i.e. improves Google\u27s MRR score by 12 percents)
Why people search for images using web search engines
What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1) Why do people search for images in text-based Web image search systems? (2) How does image search behavior
Constructing an Interaction Behavior Model for Web Image Search
User interaction behavior is a valuable source of implicit relevance
feedback. In Web image search a different type of search result presentation is
used than in general Web search, which leads to different interaction
mechanisms and user behavior. For example, image search results are
self-contained, so that users do not need to click the results to view the
landing page as in general Web search, which generates sparse click data. Also,
two-dimensional result placement instead of a linear result list makes browsing
behaviors more complex. Thus, it is hard to apply standard user behavior models
(e.g., click models) developed for general Web search to Web image search.
In this paper, we conduct a comprehensive image search user behavior analysis
using data from a lab-based user study as well as data from a commercial search
log. We then propose a novel interaction behavior model, called grid-based user
browsing model (GUBM), whose design is motivated by observations from our data
analysis. GUBM can both capture users' interaction behavior, including cursor
hovering, and alleviate position bias. The advantages of GUBM are two-fold: (1)
It is based on an unsupervised learning method and does not need manually
annotated data for training. (2) It is based on user interaction features on
search engine result pages (SERPs) and is easily transferable to other
scenarios that have a grid-based interface such as video search engines. We
conduct extensive experiments to test the performance of our model using a
large-scale commercial image search log. Experimental results show that in
terms of behavior prediction (perplexity), and topical relevance and image
quality (normalized discounted cumulative gain (NDCG)), GUBM outperforms
state-of-the-art baseline models as well as the original ranking. We make the
implementation of GUBM and related datasets publicly available for future
studies.Comment: 10 page