Prediction of User Interests for Providing Relevant Information Using Relevance Feedback and Re-ranking

2015 ◽  
Vol 11 (4) ◽  
pp. 55-71 ◽  
Author(s):  
L. Sai Ramesh ◽  
S. Ganapathy ◽  
R. Bhuvaneshwari ◽  
K. Kulothungan ◽  
V. Pandiyaraju ◽  
...  
Keyword(s):  
Relevant Information ◽  
User Profiles ◽  
User Interest ◽  
Search System ◽  
New Approach ◽  
User Interests ◽  
Proposed Model ◽  
Search For Information ◽  
Browsing Behavior ◽  
Made In

Predicting user interest based on their browsing pattern is useful in relevant information retrieval. In such a scenario, queries must be unambiguous and precise. For a broad-topic and ambiguous query, different users may with different interests may search for information from the internet. The inference and analysis of user search goals using rules will be helpful to enhance the relevancy and user experience. A major deficiency of generic search system is that they have static model which is to be applied for all the users and hence are not adaptable to individual users. User interest is important when performing clustering so that it is possible to enhance the personalization. In this paper, a new approach is proposed to infer user interests based on their queries and fast profile logs and to provide relevant information to users based on personalization. For this purpose, a framework is designed to analyze different user profiles and interests while query processing including relevance analysis. Implicit Feedback sessions are also constructed from user profiles based on mouse and button clicks made in their current and past queries. In addition, browsing behaviors of users are analyzed using rules and also using the feedback sessions. Temporary documents are generated in this work for representing the feedback sessions effectively. Finally, personalization is made based on browsing behavior and relevant information is provided to the users. From the experiments conducted in this work, it is observed that the proposed model provide most accurate and relevant contents to the users when compared with other related work.

scholarly journals Recommendation Based on Users’ Long-Term and Short-Term Interests with Attention

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Qiaoqiao Tan ◽  
Fang’ai Liu
Keyword(s):  
User Behavior ◽  
User Interest ◽  
Short Term ◽  
User Interests ◽  
User Behaviors ◽  
Current State ◽  
Proposed Model ◽  
Dependence Problem ◽  
Behavior Sequences

Recommendations based on user behavior sequences are becoming more and more common. Some studies consider user behavior sequences as interests directly, ignoring the mining and representation of implicit features. However, user behaviors contain a lot of information, such as consumption habits and dynamic preferences. In order to better locate user interests, this paper proposes a Bi-GRU neural network with attention to model user’s long-term historical preferences and short-term consumption motivations. First, a Bi-GRU network is established to solve the long-term dependence problem in sequences, and attention mechanism is introduced to capture user interest changes related to the target item. Then, user’s short-term interaction trajectory based on self-attention is modeled to distinguish the importance of each potential feature. Finally, combined with long-term and short-term interests, the next behavior is predicted. We conducted extensive experiments on Amazon and MovieLens datasets. The experimental results demonstrate that the proposed model outperforms current state-of-the-art models in Recall and NDCG indicators. Especially in MovieLens dataset, compared with other RNN-based models, our proposed model improved at least 2.32% at Recall@20, which verifies the effectiveness of modeling long-term and short-term interest of users, respectively.

Intelligent recognition of semantic relationships based on antonymy

2020 ◽  
Vol 16 (3) ◽  
pp. 263-290
Author(s):  
Hui Guan ◽  
Chengzhen Jia ◽  
Hongji Yang
Keyword(s):  
Semantic Similarity ◽  
New Approach ◽  
Word Similarity ◽  
Semantic Relationships ◽  
Proposed Model ◽  
Path Distance ◽  
The Hierarchical Structure ◽  
Thinking Process ◽  
Similarity Measuring ◽  
Intelligent Recognition

Since computing semantic similarity tends to simulate the thinking process of humans, semantic dissimilarity must play a part in this process. In this paper, we present a new approach for semantic similarity measuring by taking consideration of dissimilarity into the process of computation. Specifically, the proposed measures explore the potential antonymy in the hierarchical structure of WordNet to represent the dissimilarity between concepts and then combine the dissimilarity with the results of existing methods to achieve semantic similarity results. The relation between parameters and the correlation value is discussed in detail. The proposed model is then applied to different text granularity levels to validate the correctness on similarity measurement. Experimental results show that the proposed approach not only achieves high correlation value against human ratings but also has effective improvement to existing path-distance based methods on the word similarity level, in the meanwhile effectively correct existing sentence similarity method in some cases in Microsoft Research Paraphrase Corpus and SemEval-2014 date set.

scholarly journals A New Approach to Inform Restoration and Management Decisions for Sustainable Apiculture

2021 ◽  
Vol 13 (11) ◽  
pp. 6109
Author(s):  
Joanne Lee Picknoll ◽  
Pieter Poot ◽  
Michael Renton
Keyword(s):  
Floral Resources ◽  
Pollination Services ◽  
New Approach ◽  
Honey Production ◽  
Proposed Model ◽  
Colony Loss ◽  
Supply Potential ◽  
Australian Case ◽  
Stocking Rates

Habitat loss has reduced the available resources for apiarists and is a key driver of poor colony health, colony loss, and reduced honey yields. The biggest challenge for apiarists in the future will be meeting increasing demands for pollination services, honey, and other bee products with limited resources. Targeted landscape restoration focusing on high-value or high-yielding forage could ensure adequate floral resources are available to sustain the growing industry. Tools are currently needed to evaluate the likely productivity of potential sites for restoration and inform decisions about plant selections and arrangements and hive stocking rates, movements, and placements. We propose a new approach for designing sites for apiculture, centred on a model of honey production that predicts how changes to plant and hive decisions affect the resource supply, potential for bees to collect resources, consumption of resources by the colonies, and subsequently, amount of honey that may be produced. The proposed model is discussed with reference to existing models, and data input requirements are discussed with reference to an Australian case study area. We conclude that no existing model exactly meets the requirements of our proposed approach, but components of several existing models could be combined to achieve these needs.

scholarly journals A Survey of Arabic Named Entity Recognition and Classification

2014 ◽  
Vol 40 (2) ◽  
pp. 469-510 ◽  
Author(s):  
Khaled Shaalan
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Relevant Information ◽  
Arabic Language ◽  
Entity Recognition ◽  
Named Entities ◽  
Linguistic Resources ◽  
Named Entity ◽  
To Receive ◽  
Made In

As more and more Arabic textual information becomes available through the Web in homes and businesses, via Internet and Intranet services, there is an urgent need for technologies and tools to process the relevant information. Named Entity Recognition (NER) is an Information Extraction task that has become an integral part of many other Natural Language Processing (NLP) tasks, such as Machine Translation and Information Retrieval. Arabic NER has begun to receive attention in recent years. The characteristics and peculiarities of Arabic, a member of the Semitic languages family, make dealing with NER a challenge. The performance of an Arabic NER component affects the overall performance of the NLP system in a positive manner. This article attempts to describe and detail the recent increase in interest and progress made in Arabic NER research. The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the different Arabic linguistic resources are presented and the approaches used in Arabic NER field are explained. The features of common tools used in Arabic NER are described, and standard evaluation metrics are illustrated. In addition, a review of the state of the art of Arabic NER research is discussed. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification.

On the role of the anthropocentric factor in the realization of the derivational potential of names of ungulates as motivating lexical units

2021 ◽  
pp. 191-210
Author(s):  
Nikolay D. Golev ◽  
◽  
Irina P. Falomkina ◽  
Keyword(s):  
Native Speakers ◽  
Russian Language ◽  
Search System ◽  
Proposed Model ◽  
Data Capturing ◽  
Building Behavior ◽  
Building System ◽  
Google Search ◽  
The Russian Language

The paper is dedicated to describing the word-building system of the Russian language in terms of its vocabulary. Lexical factors are discussed influencing the formation of lexical units’ potential as motivating units of word-building processes and relations and the realization of this potential in language activities. Of most interest for the authors are anthropocentric determinants, most of which are coordinating the lexical system and, through its mediation, the word-building system with the worldview of native speakers of the Russian language. The proposed model of derivational development of vocabulary provides such coordination through studying the deep-seated process of conceptualization of the words that are the potential motivators of neologisms. This study identifies the word frequency as an external manifestation of conceptualization. The frequency data were obtained from Google search system statistical data. Capturing not only usual but also occasional and potential words, this source is an effective tool for studying word-building processes and their results. This study has unveiled the interrelation between the language worldview of native speakers of Russian and their “word-building behavior” in language activities. The worldview has been found, first of all, to be determined by the pragmatic factor, which primarily influences the usage of a word in the speech reflected by its frequency. The frequency ranks lexical units due to their derivational potential and thereby provides a researcher with a reliable instrument for its study.

Semantic Representation of a Geo-Social User Profile for a Personalised Information Retrieval

2021 ◽  
pp. 2150044
Author(s):  
Tahar Rafa ◽  
Samir Kechid
Keyword(s):  
Information Retrieval ◽  
Information Search ◽  
Semantic Representation ◽  
User Profile ◽  
Search Process ◽  
Search System ◽  
User Interactions ◽  
User Interests ◽  
Situational Contexts ◽  
User Query

The user-centred information retrieval needs to introduce semantics into the user modelling for a meaningful representation of user interests. The semantic representation of the user interests helps to improve the identification of the user’s future cognitive needs. In this paper, we present a semantic-based approach for a personalised information retrieval. This approach is based on the design and the exploitation of a user profile to represent the user and his interests. In this user profile, we combine an ontological semantics issued from WordNet ontology, and a personal semantics issued from the different user interactions with the search system and with his social and situational contexts of his previous searches. The personal semantics considers the co-occurrence relations between relevant components of the user profile as semantic links. The user profile is used to improve two important phases of the information search process: (i) expansion of the initial user query and (ii) adaptation of the search results to the user interests.

I Know What You Need: Investigating Document Retrieval Effectiveness with Partial Session Contexts

2022 ◽  
Vol 40 (3) ◽  
pp. 1-30
Author(s):  
Procheta Sen ◽  
Debasis Ganguly ◽  
Gareth J. F. Jones
Keyword(s):  
Relevant Information ◽  
Document Retrieval ◽  
Context Information ◽  
Information Need ◽  
Search System ◽  
Query Log ◽  
Sequence Modeling ◽  
Joint Embedding ◽  
One Step ◽  
A Current

Reducing user effort in finding relevant information is one of the key objectives of search systems. Existing approaches have been shown to effectively exploit the context from the current search session of users for automatically suggesting queries to reduce their search efforts. However, these approaches do not accomplish the end goal of a search system—that of retrieving a set of potentially relevant documents for the evolving information need during a search session. This article takes the problem of query prediction one step further by investigating the problem of contextual recommendation within a search session. More specifically, given the partial context information of a session in the form of a small number of queries, we investigate how a search system can effectively predict the documents that a user would have been presented with had he continued the search session by submitting subsequent queries. To address the problem, we propose a model of contextual recommendation that seeks to capture the underlying semantics of information need transitions of a current user’s search context. This model leverages information from a number of past interactions of other users with similar interactions from an existing search log. To identify similar interactions, as a novel contribution, we propose an embedding approach that jointly learns representations of both individual query terms and also those of queries (in their entirety) from a search log data by leveraging session-level containment relationships. Our experiments conducted on a large query log, namely the AOL, demonstrate that using a joint embedding of queries and their terms within our proposed framework of document retrieval outperforms a number of text-only and sequence modeling based baselines.

scholarly journals Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Aisong Qin ◽  
Qinghua Zhang ◽  
Qin Hu ◽  
Guoxi Sun ◽  
Jun He ◽  
...  
Keyword(s):  
Health Management ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Programming Algorithm ◽  
Wiener Model ◽  
New Approach ◽  
Degradation Modeling ◽  
Proposed Model ◽  
Useful Life ◽  
Degradation Indicator

Remaining useful life (RUL) prediction can provide early warnings of failure and has become a key component in the prognostics and health management of systems. Among the existing methods for RUL prediction, the Wiener-process-based method has attracted great attention owing to its favorable properties and flexibility in degradation modeling. However, shortcomings exist in methods of this type; for example, the degradation indicator and the first predicting time (FPT) are selected subjectively, which reduces the prediction accuracy. Toward this end, this paper proposes a new approach for predicting the RUL of rotating machinery based on an optimal degradation indictor. First, a genetic programming algorithm is proposed to construct an optimal degradation indicator using the concept of FPT. Then, a Wiener model based on the obtained optimal degradation indicator is proposed, in which the sensitivities of the dimensionless parameters are utilized to determine the FPT. Finally, the expectation of the predicted RUL is calculated based on the proposed model, and the estimated mean degradation path is explicitly derived. To demonstrate the validity of this model, several experiments on RUL prediction are conducted on rotating machinery. The experimental results indicate that the method can effectively improve the accuracy of RUL prediction.

Understanding the electrical behavior of the action potential in terms of elementary electrical sources

2015 ◽  
Vol 39 (1) ◽  
pp. 15-26 ◽  
Author(s):  
Javier Rodriguez-Falces
Keyword(s):  
Action Potential ◽  
Action Potentials ◽  
Present Report ◽  
Electrical Potential ◽  
Ionic Currents ◽  
New Approach ◽  
Electrical Potentials ◽  
Proposed Model ◽  
Ionic Mechanisms ◽  
Human Electrophysiology

A concept of major importance in human electrophysiology studies is the process by which activation of an excitable cell results in a rapid rise and fall of the electrical membrane potential, the so-called action potential. Hodgkin and Huxley proposed a model to explain the ionic mechanisms underlying the formation of action potentials. However, this model is unsuitably complex for teaching purposes. In addition, the Hodgkin and Huxley approach describes the shape of the action potential only in terms of ionic currents, i.e., it is unable to explain the electrical significance of the action potential or describe the electrical field arising from this source using basic concepts of electromagnetic theory. The goal of the present report was to propose a new model to describe the electrical behaviour of the action potential in terms of elementary electrical sources (in particular, dipoles). The efficacy of this model was tested through a closed-book written exam. The proposed model increased the ability of students to appreciate the distributed character of the action potential and also to recognize that this source spreads out along the fiber as function of space. In addition, the new approach allowed students to realize that the amplitude and sign of the extracellular electrical potential arising from the action potential are determined by the spatial derivative of this intracellular source. The proposed model, which incorporates intuitive graphical representations, has improved students' understanding of the electrical potentials generated by bioelectrical sources and has heightened their interest in bioelectricity.

Discovery and classification of user interests on social media

2017 ◽  
Vol 45 (3) ◽  
pp. 130-138 ◽  
Author(s):  
Basit Shahzad ◽  
Ikramullah Lali ◽  
M. Saqib Nawaz ◽  
Waqar Aslam ◽  
Raza Mustafa ◽  
...  
Keyword(s):  
Information Filtering ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Support Vector ◽  
User Interest ◽  
Socioeconomic Impacts ◽  
Content Type ◽  
Positive Correlation ◽  
User Interests ◽  
Twitter Users

Purpose Twitter users’ generated data, known as tweets, are now not only used for communication and opinion sharing, but they are considered an important source of trendsetting, future prediction, recommendation systems and marketing. Using network features in tweet modeling and applying data mining and deep learning techniques on tweets is gaining more and more interest. Design/methodology/approach In this paper, user interests are discovered from Twitter Trends using a modeling approach that uses network-based text data (tweets). First, the popular trends are collected and stored in separate documents. These data are then pre-processed, followed by their labeling in respective categories. Data are then modeled and user interest for each Trending topic is calculated by considering positive tweets in that trend, average retweet and favorite count. Findings The proposed approach can be used to infer users’ topics of interest on Twitter and to categorize them. Support vector machine can be used for training and validation purposes. Positive tweets can be further analyzed to find user posting patterns. There is a positive correlation between tweets and Google data. Practical implications The results can be used in the development of information filtering and prediction systems, especially in personalized recommendation systems. Social implications Twitter microblogging platform offers content posting and sharing to billions of internet users worldwide. Therefore, this work has significant socioeconomic impacts. Originality/value This study guides on how Twitter network structure features can be exploited in discovering user interests using tweets. Further, positive correlation of Twitter Trends with Google Trends is reported, which validates the correctness of the authors’ approach.

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