Identifying and labeling search tasks via query-based hawkes processes

Abstract

We consider a search task as a set of queries that serve the same user information need. Analyzing search tasks from user query streams plays an important role in building a set of modern tools to improve search engine performance. In this paper, we propose a probabilistic method for identifying and labeling search tasks based on the following intuitive observations: queries that are issued temporally close by users in many sequences of queries are likely to belong to the same search task, meanwhile, different users having the same information needs tend to submit topically coherent search queries. To capture the above intuitions, we directly model query temporal patterns using a special class of point processes called Hawkes processes, and combine topic models with Hawkes processes for simultaneously identifying and labeling search tasks. Essentially, Hawkes processes utilize their self-exciting properties to identify search tasks if influence exists among a sequence of queries for individual users, while the topic model exploits query co-occurrence across different users to discover the latent information needed for labeling search tasks. More importantly, there is mutual reinforcement between Hawkes processes and the topic model in the unified model that enhances the performance of both. We evaluate our method based on both synthetic data and real-world query log data. In addition, we also apply our model to query clustering and search task identification. By comparing with state-of-the-art methods, the results demonstrate that the improvement in our proposed approach is consistent and promising.

The Social Dynamics of Language Change in Online Networks

“…Researchers usually estimate all M 2 influence parameters of a Hawkes process (e.g., [38,51]). However, in our setting, M > 10 6 , so there are O(10 12 ) influence parameters.…”

Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach

“…• QC-HTC/QC-WCC [20]: is series of methods viewed search task identi cation as the problem of best approximating the manually annotated tasks, and proposed both clustering and heuristic algorithms to solve the problem. • LDA-Hawkes [17]: a probabilistic method for identifying and labeling search tasks that model query temporal patterns using a special class of point process called Hawkes processes, and combine topic model with Hawkes processes for simultaneously identifying and labeling search tasks. • LDA Time-Window(TW): is model assumes queries belong to the same search task only if they lie in a xed or exible time window, and uses LDA to cluster queries into topics based on the query co-occurrences within the same time window.…”

User Modeling for a Personal Assistant

“…Li et al [16] also consider in-session tasks. They use query words, query co-occurrence, and the temporal sequence of queries as their main signals.…”

“…In previous work [23,16], researchers often had human raters completely annotate search histories for a small number of users, and used that as training data. There are two reasons why this was not an option for us.…”