Semantic Term-Term Coupling based Feature Enhancement of User Profiles in Recommendation Systems

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0

Content-based recommender system is a subclass of information systems that recommends an item to the user based on its description. It suggests items such as news, documents, articles, webpages, journals, and more to users as per their inclination by comparing the key features of the items with key terms or features of user interest profiles. This paper proposes the new methodology using Non-IIDness based semantic term-term coupling from the content referred by users to enhance recommendation results. In the proposed methodology, the semantic relationship is analyzed by estimating the explicit and implicit relationship between terms. It associates terms that are semantically related in real world or are used inter-changeably such as synonyms. The underestimated features of user profiles have been enhanced after term-term relation analysis which results in improved similarity estimation of relevant items with the user profiles.The experimentation result proves that the proposed methodology improves the overall search and retrieval results as compared to the state-of-art algorithms.

Author(s):  
Pushpendra Singh ◽  
P.N. Hrisheekesha ◽  
Vinai Kumar Singh

Content based image retrieval (CBIR) is one of the field for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, color, shape, and other information. In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the literature suggests that the precision and recall for the gained results using single content descriptor is not significant. The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes.


2019 ◽  
Vol 11 (10) ◽  
pp. 216 ◽  
Author(s):  
Mehmet Ali Ertürk ◽  
Muhammed Ali Aydın ◽  
Muhammet Talha Büyükakkaşlar ◽  
Hayrettin Evirgen

Internet of Things (IoT) expansion led the market to find alternative communication technologies since existing protocols are insufficient in terms of coverage, energy consumption to fit IoT needs. Low Power Wide Area Networks (LPWAN) emerged as an alternative cost-effective communication technology for the IoT market. LoRaWAN is an open LPWAN standard developed by LoRa Alliance and has key features i.e., low energy consumption, long-range communication, builtin security, GPS-free positioning. In this paper, we will introduce LoRaWAN technology, the state of art studies in the literature and provide open opportunities.


2018 ◽  
Vol 45 (1) ◽  
pp. 42-49
Author(s):  
Mojca Cajnko

AbstractThe introduction to the article presents writing as a semiotic system by applying some key terms from Eco’s semiotics (1979) to Hittite writing. It then draws on modern case theory to describe case as a syntactic-semantic relationship which may be expressed by various means. The main part of the paper presents the different ways in which case could be expressed in Hittite writing. This paper is therefore only the beginning of research on case in Hittite writing. It offers a theoretical basis for further studies on this topic which will enable a more comprehensive understanding of the complex case system in Hittite.


2022 ◽  
Vol 9 (3) ◽  
pp. 0-0

This paper presents the work done on recommendations of healthcare related journal papers by understanding the semantics of terms from the papers referred by users in past. In other words, user profiles based on user interest within the healthcare domain are constructed from the kind of journal papers read by the users. Multiple user profiles are constructed for each user based on different categories of papers read by the users. The proposed approach goes to the granular level of extrinsic and intrinsic relationship between terms and clusters highly semantically related relevant domain terms where each cluster represents a user interest area. The semantic analysis of terms is done starting from co-occurrence analysis to extract the intra-couplings between terms and then the inter-couplings are extracted from the intra-couplings and then finally clusters of highly related terms are formed. The experiments showed improved precision for the proposed approach as compared to the state-of-the-art technique with a mean reciprocal rank of 0.76.


2020 ◽  
Vol 34 (01) ◽  
pp. 156-163 ◽  
Author(s):  
Zequn Lyu ◽  
Yu Dong ◽  
Chengfu Huo ◽  
Weijun Ren

Click-through rate (CTR) prediction is a core task in the field of recommender system and many other applications. For CTR prediction model, personalization is the key to improve the performance and enhance the user experience. Recently, several models are proposed to extract user interest from user behavior data which reflects user's personalized preference implicitly. However, existing works in the field of CTR prediction mainly focus on user representation and pay less attention on representing the relevance between user and item, which directly measures the intensity of user's preference on target item. Motivated by this, we propose a novel model named Deep Match to Rank (DMR) which combines the thought of collaborative filtering in matching methods for the ranking task in CTR prediction. In DMR, we design User-to-Item Network and Item-to-Item Network to represent the relevance in two forms. In User-to-Item Network, we represent the relevance between user and item by inner product of the corresponding representation in the embedding space. Meanwhile, an auxiliary match network is presented to supervise the training and push larger inner product to represent higher relevance. In Item-to-Item Network, we first calculate the item-to-item similarities between user interacted items and target item by attention mechanism, and then sum up the similarities to obtain another form of user-to-item relevance. We conduct extensive experiments on both public and industrial datasets to validate the effectiveness of our model, which outperforms the state-of-art models significantly.


Author(s):  
L. Sai Ramesh ◽  
S. Ganapathy ◽  
R. Bhuvaneshwari ◽  
K. Kulothungan ◽  
V. Pandiyaraju ◽  
...  

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.


2016 ◽  
Vol 21 (6) ◽  
pp. 5-11
Author(s):  
E. Randolph Soo Hoo ◽  
Stephen L. Demeter

Abstract Referring agents may ask independent medical evaluators if the examinee can return to work in either a normal or a restricted capacity; similarly, employers may ask external parties to conduct this type of assessment before a hire or after an injury. Functional capacity evaluations (FCEs) are used to measure agility and strength, but they have limitations and use technical jargon or concepts that can be confusing. This article clarifies key terms and concepts related to FCEs. The basic approach to a job analysis is to collect information about the job using a variety of methods, analyze the data, and summarize the data to determine specific factors required for the job. No single, optimal job analysis or validation method is applicable to every work situation or company, but the Equal Employment Opportunity Commission offers technical standards for each type of validity study. FCEs are a systematic method of measuring an individual's ability to perform various activities, and results are matched to descriptions of specific work-related tasks. Results of physical abilities/agilities tests are reported as “matching” or “not matching” job demands or “pass” or “fail” meeting job criteria. Individuals who fail an employment physical agility test often challenge the results on the basis that the test was poorly conducted, that the test protocol was not reflective of the job, or that levels for successful completion were inappropriate.


2004 ◽  
Vol 9 (2) ◽  
pp. 1-16
Author(s):  
Christopher R. Brigham ◽  
Kathryn Mueller ◽  
Douglas Van Zet ◽  
Debra J. Northrup ◽  
Edward B. Whitney ◽  
...  

Abstract [Continued from the January/February 2004 issue of The Guides Newsletter.] To understand discrepancies in reviewers’ ratings of impairments based on different editions of the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides), users can usefully study the history of the revisions as successive editions attempted to provide a comprehensive, valid, reliable, unbiased, and evidence-based system. Some shortcomings of earlier editions have been addressed in the AMA Guides, Fifth Edition, but problems remain with each edition, largely because of the limited scientific evidence available. In the context of the history of the different editions of the AMA Guides and their development, the authors discuss and contextualize a number of key terms and principles including the following: definitions of impairment and normal; activities of daily living; maximum medical improvement; impairment percentages; conversion of regional impairments; combining impairments; pain and other subjective complaints; physician judgment; and causation analysis; finally, the authors note that impairment is not synonymous with disability or work interference. The AMA Guides, Fifth Edition, contrasts impairment evaluations and independent medical evaluations (this was not done in previous editions) and discusses impairment evaluations, rules for evaluations, and report standards. Upper extremity and lower extremity impairment evaluations are discussed in terms of clinical assessments and rating processes, analyzing important changes between editions and problematic areas (eg, complex regional pain syndrome).


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