scholarly journals Enhancing Stability of Recommender System: An Ensemble based Information Retrieval Approach

2016 ◽  
Vol 9 (48) ◽  
Author(s):  
N. Saipraba ◽  
V. Subramaniyaswamy
Keyword(s):  
Information Retrieval ◽  
Recommender System

A Meta-Heuristic Algorithm Approximating Optimized Recommendations for E-Commerce Business Promotions

2020 ◽  
Vol 11 (2) ◽  
pp. 23-49
Author(s):  
Shalini Gupta ◽  
Veer Sain Dixit
Keyword(s):  
Information Retrieval ◽  
Recommender System ◽  
State Of The Art ◽  
Web Log Mining ◽  
Web Log ◽  
Personalized Services ◽  
Product Recommendations ◽  
Log Mining ◽  
Information Retrieval Methods

To provide personalized services such as online-product recommendations, it is usually necessary to model clickstream behavior of users if implicit preferences are taken into account. To accomplish this, web log mining is a promising approach that mines clickstream sessions and depicts frequent sequential paths that a customer follows while browsing e-commerce websites. Strong attributes are identified from the navigation behavior of users. These attributes reflect absolute preference (AP) of the customer towards a product viewed. The preferences are obtained only for the products clicked. These preferences are further refined by calculating the sequential preference (SP) of the user for the products. This paper proposes an intelligent recommender system known as SAPRS (sequential absolute preference-based recommender system) that embed these two approaches that are integrated to improve the quality of recommendation. The performance is evaluated using information retrieval methods. Extensive experiments were carried out to evaluate the proposed approach against state-of-the-art methods.

scholarly journals User Profiling for University Recommender System Using Automatic Information Retrieval

2016 ◽  
Vol 78 ◽  
pp. 5-12 ◽  
Author(s):  
Sumitkumar Kanoje ◽  
Debajyoti Mukhopadhyay ◽  
Sheetal Girase
Keyword(s):  
Information Retrieval ◽  
Recommender System ◽  
User Profiling ◽  
Automatic Information

scholarly journals Recommendation System: Techniques, Evaluation and Limitations

2019 ◽  
Vol 8 (6) ◽  
pp. 2097-2101
Keyword(s):  
Information Retrieval ◽  
Recommender System ◽  
Recommendation System ◽  
Information Overload ◽  
Recommendation Systems ◽  
Research Papers

For the benefits of the user in selecting items based on their interests, the recommendation technology is developed in different domains. A recommender system is one of the major techniques, that handles information overload problem of Information Retrieval by suggesting users with appropriate and relevant items. This paper surveys recommendation technology, the challenges and its solutions. Recommendation technology is applied in many areas like movies, videos, books, research papers, libraries, music, news, tourism, etc. This survey is useful for the further implementation and analysis of how users are adapting these technologies and how helpful it is for the user. This work also helps understand the different techniques of recommendation systems and how they can be evaluated.

P2PCF: A collaborative filtering based recommender system for peer to peer social networks

2021 ◽  
pp. 1-19
Author(s):  
Lyes Badis ◽  
Mourad Amad ◽  
Djamil Aïssani ◽  
Sofiane Abbar
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Social Network ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Cold Start ◽  
Peer To Peer ◽  
Public Concerns ◽  
Cold Start Problem ◽  
Filtering Approach

The recent privacy incidents reported in major media about global social networks raised real public concerns about centralized architectures. P2P social networks constitute an interesting paradigm to give back users control over their data and relations. While basic social network functionalities such as commenting, following, sharing, and publishing content are widely available, more advanced features related to information retrieval and recommendation are still challenging. This is due to the absence of a central server that has a complete view of the network. In this paper, we propose a new recommender system called P2PCF. We use collaborative filtering approach to recommend content in P2P social networks. P2PCF enables privacy preserving and tackles the cold start problem for both users and content. Our proposed approach assumes that the rating matrix is distributed within peers, in such a way that each peer only sees interactions made by her friends on her timeline. Recommendations are then computed locally within each peer before they are sent back to the requester. Our evaluations prove the effectiveness of our proposal compared to a centralized scheme in terms of recall and coverage.

scholarly journals Thesis Supervisor Recommendation with Representative Content and Information Retrieval

2020 ◽  
Vol 6 (2) ◽  
pp. 143
Author(s):  
Maresha Caroline Wijanto ◽  
Rachmi Rachmadiany ◽  
Oscar Karnalim
Keyword(s):  
Higher Education ◽  
Information Retrieval ◽  
Vector Space ◽  
Recommender System ◽  
Cosine Similarity ◽  
Mean Average Precision ◽  
Average Precision ◽  
Easy Task ◽  
Academic Publications ◽  
Student Thesis

Background: In higher education in Indonesia, students are often required to complete a thesis under the supervision of one or more lecturers. Allocating a supervisor is not an easy task as the thesis topic should match a prospective supervisor’s field of expertise.Objective: This study aims to develop a thesis supervisor recommender system with representative content and information retrieval. The system accepts a student thesis proposal and replies with a list of potential supervisors in a descending order based on the relevancy between the prospective supervisor’s academic publications and the proposal.Methods: Unique to this, supervisor profiles are taken from previous academic publications. For scalability, the current research uses the information retrieval concept with a cosine similarity and a vector space model.Results: According to the accuracy and mean average precision (MAP), grouping supervisor candidates based on their broad expertise is effective in matching a potential supervisor with a student. Lowercasing is effective in improving the accuracy. Considering only top ten most frequent words for each lecturer’s profile is useful for the MAP.Conclusion:An arguably effective thesis supervisor recommender system with representative content and information retrieval is proposed. 

scholarly journals Online Recommender System

2021 ◽  
Vol 9 (VII) ◽  
pp. 2569-2577
Author(s):  
Prof. Ranjanroop Walia
Keyword(s):  
Information Retrieval ◽  
Recommender System ◽  
Recommendation System ◽  
Business Environment ◽  
Major Research ◽  
Recommendation Algorithm ◽  
Purchase Criteria ◽  
Sentiment Score ◽  
A Company ◽  
Comprehensive Study

As the size of the e-commerce market grows, the consequences of it are appearing throughout society.The business Environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the users subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user. Recommender system (RS) has emerged as a major research interest that Aims to help users to find items online by providing suggestions that Closely match their interest. This paper provides a comprehensive study on the RS covering the different recommendation approaches, associated issues, and techniques used for information retrieval.

Demonstration of the Use of Tape-Recorded Information from a High Vacuum Electron Microscope

1969 ◽  
Vol 27 ◽  
pp. 218-219
Author(s):  
Richard E. Hartman ◽  
Roberta S. Hartman ◽  
Peter L. Ramos
Keyword(s):  
Information Retrieval ◽  
Electron Microscope ◽  
Magnetic Tape ◽  
High Vacuum ◽  
Data Retrieval ◽  
Electronic Information ◽  
Electronic Data ◽  
Subsequent Processing ◽  
Continuous Record

We have long felt that some form of electronic information retrieval would be more desirable than conventional photographic methods in a high vacuum electron microscope for various reasons. The most obvious of these is the fact that with electronic data retrieval the major source of gas load is removed from the instrument. An equally important reason is that if any subsequent analysis of the data is to be made, a continuous record on magnetic tape gives a much larger quantity of data and gives it in a form far more satisfactory for subsequent processing.

Stain contamination and embedding in electron microscopy

1986 ◽  
Vol 44 ◽  
pp. 50-53
Author(s):  
Hilton H. Mollenhauer
Keyword(s):  
Electron Microscopy ◽  
Information Retrieval ◽  
Epoxy Resins ◽  
Image Contrast ◽  
Sample Preservation ◽  
Factors Associated ◽  
Amount Of Information ◽  
Electron Microscopical ◽  
Contrast Information

Many factors (e.g., resolution of microscope, type of tissue, and preparation of sample) affect electron microscopical images and alter the amount of information that can be retrieved from a specimen. Of interest in this report are those factors associated with the evaluation of epoxy embedded tissues. In this context, informational retrieval is dependant, in part, on the ability to “see” sample detail (e.g., contrast) and, in part, on tue quality of sample preservation. Two aspects of this problem will be discussed: 1) epoxy resins and their effect on image contrast, information retrieval, and sample preservation; and 2) the interaction between some stains commonly used for enhancing contrast and information retrieval.

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