Intention Mining Research Articles

Classification Model for Intent Mining in Personal Website Based on Support Vector Machine

With the rapid growth of personal website influence, the advertisement placing has become an important investment in personal websites. But in order to accurate the advertisement placing, the specific quest for the specific users with their specific interesting need to be concerned. Acquiring, preprocessing and classifying consumption intention of the released information that published in the personal websites is the main task of this essay. We regard consumption intention mining as a binary classification problem, and extract multi-dimensional features from the raw corpus. Finally, we propose models based on SVM, Naive Bayes and deep learning to solve the consumption intention classification problem. The experimental result shows that the deep learning based method achieves the highest F-measure.

Mining and ranking users’ intents behind queries

How to understand intents behind user queries is crucial towards improving the performance of Web search systems. NTCIR-11 IMine task focuses on this problem. In this paper, we address the NTCIR-11 IMine task with two phases referred to as Query Intent Mining (QIM) and Query Intent Ranking (QIR). (I) QIM is intended to mine users' potential intents by clustering short text fragments related to the given query. (II) QIR focuses on ranking those mined intents in a proper way. Two challenges exist in handling these tasks. (II) How to precisely estimate the intent similarity between user queries which only consist of a few words. (2) How to properly rank intents in terms of multiple factors, e.g. relevance, diversity, intent drift and so on. For the first challenge, we first investigate two interesting phenomena by analyzing query logs and document datasets, namely "Same-Intent-Co-Click" (SICC) and "Same-Intent-Similar-Rank" (SISR). SICC means that when users issue different queries, these queries represent the same intent if they click on the same URL. SISR means that if two queries denote the same intent, we should get similar search results when issuing them to a search engine. Then, we propose similarity functions for QIM based on the two phenomena. For the second challenge, we propose a novel intent ranking model which considers multiple factors as a whole. We perform extensive experiments and an interesting case study on the Chinese dataset of NTCIR-11 IMine task. Experimental results demonstrate the effectiveness of our proposed approaches in terms of both QIM and QIR.

Query intent mining with multiple dimensions of web search data

Understanding the users' latent intents behind the search queries is critical for search engines. Hence, there has been an increasing attention on studying how to effectively mine the intents of search queries by analyzing search engine query log. However, we observe that the information richness of query log is not fully utilized so far and the information underuse heavily limits the performance of the existing methods. In this paper, we tackle the problem of query intent mining by taking full advantage of the information richness of query log from a multi-dimensional perspective. Specifically, we capture the latent relations between search queries via three different dimensions: the URL dimension, the session dimension and the term dimension. We first propose the Result-Oriented Framework (ROF), which is easy to implement and significantly improves both the precision and the recall of query intent mining. We further propose the Topic-Oriented Framework (TOF), in order to significantly reduce the online time and memory consumptions for query intent mining. TOF employs the Query Log Topic Model (QLTM) that derives the latent topics from query log to integrate the information of the three dimensions in a principled way. The latent topics that are considered as low-dimensional descriptions of the query relations and serve as the basis of efficient online query intent mining. We conduct extensive experiments on a major commercial search engine query log. Experimental results show that the two frameworks significantly outperform the state-of-the-art methods with respect to a variety of metrics.

Mining User Consumption Intention from Social Media Using Domain Adaptive Convolutional Neural Network

Social media platforms are often used by people to express their needs and desires. Such data offer great opportunities to identify users’ consumption intention from user-generated contents, so that better tailored products or services can be recommended. However, there have been few efforts on mining commercial intents from social media contents. In this paper, we investigate the use of social media data to identify consumption intentions for individuals. We develop a Consumption Intention Mining Model (CIMM) based on convolutional neural network (CNN), for identifying whether the user has a consumption intention. The task is domain-dependent, and learning CNN requires a large number of annotated instances, which can be available only in some domains. Hence, we investigate the possibility of transferring the CNN mid-level sentence representation learned from one domain to another by adding an adaptation layer. To demonstrate the effectiveness of CIMM, we conduct experiments on two domains. Our results show that CIMM offers a powerful paradigm for effectively identifying users’ consumption intention based on their social media data. Moreover, our results also confirm that the CNN learned in one domain can be effectively transferred to another domain. This suggests that a great potential for our model to significantly increase effectiveness of product recommendations and targeted advertising.

Intentional Process Mining

Understanding people's goals is a challenging issue that is met in many different areas such as security, sales, information retrieval, etc. Intention Mining aims at uncovering intentions from observations of actual activities. While most Intention Mining techniques proposed so far focus on mining individual intentions to analyze web engine queries, this paper proposes a generic technique to mine intentions from activity traces. The proposed technique relies on supervised learning and generates intentional models specified with the Map formalism. The originality of the contribution lies in the demonstration that it is actually possible to reverse engineer the underlying intentional plans built by people when in action, and specify them in models e.g. with intentions at different levels, dependencies, links with other concepts, etc. After an introduction on intention mining, the paper presents the Supervised Map Miner Method and reports two controlled experiments that were undertaken to evaluate precision, recall and F-Score. The results are promising since the authors were able to find the intentions underlying the activities as well as the corresponding map process model with satisfying accuracy, efficiency and performance.

Intent mining in search query logs for automatic search script generation

Capturing users' information needs is essential in decreasing the barriers in information access. This paper mines sequences of actions called search scripts from search query logs which keep large-scale users' search experiences. Search scripts can be applied to guide users to satisfy their information needs, improve the search effectiveness of retrieval systems, recommend advertisements at suitable places, and so on. Information quality, query ambiguity, topic diversity, and document relevancy are four major challenging issues in search script mining. In this paper, we determine the relevance of URLs for a query, adopt the Open Directory Project (ODP) categories to disambiguate queries and URLs, explore various features and clustering algorithms for intent clustering, identify critical actions from each intent cluster to form a search script, generate a nature language description for each action, and summarize a topic for each search script. Experiments show that the complete link hierarchical clustering algorithm with the features of query terms, relevant URLs, and disambiguated ODP categories performs the best. Applying the intent clusters created by the best model to intent boundary identification achieves an $$F$$ score of 0.6666. The intent clusters then are applied to generate search scripts.