User Intention Identification Research Articles

WITHDRAWN: A proposal of bidirectional grid long short term memory based model for user intention identification in on-line search query using text

Abstract Whenever any search query is entered in to a search engine by the user, the set of web pages which are deemed to be relevant for the query being made are identified with help of an index and some algorithms to rank the web pages order at the time of display. Any result set of search query would be effective if it matches exactly with the user requirements and for this to happen understanding user intention behind the search query entered plays vital role. Research in Machine learning algorithms has shown tremendous promising results in identifying user intention. In this paper authors have done a brief survey of work related to working and application of machine learning methods in identifying user intention behind query entered and based on observations, the authors propose a Bi Directional Grid Long-Short Term Memory RNN frame work for enhancing the efficiency of user intention identification.

Applying deep neural networks for user intention identification

The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations in identifying user’s purchase intentions. The prior works have focused on using only the CNN model to perform the feature extraction without retaining the sequence correlation. Moreover, many recent studies have applied classical feature representation techniques followed by a machine learning classifier. We examine the intention review identification problem using a deep learning model with an emphasis on maintaining the sequence correlation and also to retain information for a long time span. The proposed method consists of the convolutional neural network along with long short-term memory for efficient detection of intention in a given review, i.e., whether the review is an intent vs non-intent. The experimental results depict that the performance of the proposed system is better with respect to the baseline techniques with an accuracy of 92% for Dataset1 and 94% for Dataset2. Moreover, statistical analysis also depicts the effectiveness of the proposed method with respect to the comparing methods.

A Cross-Distribution User Intention Identification Based on Topic Transfer

At present, in the field of financial science, so as to make intelligent recommendation, the potential financial intention is usually analyzed by the behavior patterns of users. However, due to the diversity of user’ behavior and distribution on different platforms, the feature of users’ financial behavior are also composed of weak features. In this paper, a cross-distributed user’ behavior recognition method based on topic transfer model is proposed. To training a transfer model with APP data in source domain, the model can weaken the influence of different distributions and recognize the intention of the target user. Finally, based on the user’s APP online data, we prove that the model based on topic transfer has better performance on cross-distributed data.

Context Analysis of Customer Requests using a Hybrid Adaptive Neuro Fuzzy Inference System and Hidden Markov Models in the Natural Language Call Routing Problem

Abstract The context analysis of customer requests in a natural language call routing problem is investigated in the paper. One of the most significant problems in natural language call routing is a comprehension of client request. With the aim of finding a solution to this issue, the Hybrid HMM and ANFIS models become a subject to an examination. Combining different types of models (ANFIS and HMM) can prevent misunderstanding by the system for identification of user intention in dialogue system. Based on these models, the hybrid system may be employed in various language and call routing domains due to nonusage of lexical or syntactic analysis in classification process.

Context Sensitive Search String Composition Algorithm using User Intention to Handle Ambiguous Keywords

<p>Finding the required URL among the first few result pages of a search engine is still a challenging task. This may require number of reformulations of the search string thus adversely affecting user's search time. Query ambiguity and polysemy are major reasons for not obtaining relevant results in the top few result pages. Efficient query composition and data organization are necessary for getting effective results. Context of the information need and the user intent may improve the autocomplete feature of existing search engines. This research proposes a Funnel Mesh-5 algorithm (FM5) to construct a search string taking into account context of information need and user intention with three main steps 1) Predict user intention with user profiles and the past searches via weighted mesh structure 2) Resolve ambiguity and polysemy of search strings with context and user intention 3) Generate a personalized disambiguated search string by query expansion encompassing user intention and predicted query. Experimental results for the proposed approach and a comparison with direct use of search engine are presented. A comparison of FM5 algorithm with K Nearest Neighbor algorithm for user intention identification is also presented. The proposed system provides better precision for search results for ambiguous search strings with improved identification of the user intention. Results are presented for English language dataset as well as Marathi (an Indian language) dataset of ambiguous search strings.</p><p> </p>

Myoelectric control of prosthetic hands: state-of-the-art review.

Myoelectric signals (MES) have been used in various applications, in particular, for identification of user intention to potentially control assistive devices for amputees, orthotic devices, and exoskeleton in order to augment capability of the user. MES are also used to estimate force and, hence, torque to actuate the assistive device. The application of MES is not limited to assistive devices, and they also find potential applications in teleoperation of robots, haptic devices, virtual reality, and so on. The myoelectric control-based prosthetic hand aids to restore activities of daily living of amputees in order to improve the self-esteem of the user. All myoelectric control-based prosthetic hands may not have similar operations and exhibit variation in sensing input, deciphering the signals, and actuating prosthetic hand. Researchers are focusing on improving the functionality of prosthetic hand in order to suit the user requirement with the different operating features. The myoelectric control differs in operation to accommodate various external factors. This article reviews the state of the art of myoelectric prosthetic hand, giving description of each control strategy.

A Service Evolution Supporting Smart Meeting Room System

Obtaining user requirement changes promptly has become a key of the rapid service evolution. Carl K. Chang et al proposed a Situ frame which can promptly obtain user requirement changes through user intention changes identified by user behavior. This paper designs a Smart Meeting Room System(SMRS) based on Situ framework. According to the user action sequence segmenting problem while inferring user intention in SMRS, this paper proposes a user action sequence segmenting approach based on scenario and max entropy, which can effectively support user intention identification. The approach is illustrated by a user action sequence segmenting process instance at the end of this paper.

Topic verification for dialogue systems based on Bayesian hypothesis testing

A novel approach to the task of verifying the uttered topic for simple dialogue systems is presented. Confidence measures generated by in-parallel topic-adapted automatic speech recognisers are used. Recognition performance, identification of user intention and detection of user-initiated topic change are greatly enhanced, most particularly for difficult topics, such as proper names or confirmations.