Improved Hybrid Approach of Filtering Using Classified Library Resources in Recommender System

The domain of context aware recommender approaches has made substantial advancement over the last decade, but many applications still do not include contextual information while providing recommendations. Contextual information is crucial for various application areas and should not be ignored. There are generally three algorithms which can be used to include context and those are: pre-filter approach, post-filter approach, and contextual modeling. Each of the algorithms has their own drawbacks. The proposed approach modifies the post filter approach to rectify its shortcomings and combines it with the pre-filter approach based on the importance of contextual attribute provided by the user. The results of experimental setup also demonstrate that the proposed system improves the precision and ranking of the recommendations provided to user. With the help of this hybrid approach, the proposed system eliminates the problem of sparsity which is present in the pre-filter algorithm, and has performance improvement over the traditional post-filter approach.

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Beyond Google: How do students conduct academic research?

First Monday ◽

10.5210/fm.v12i8.1998 ◽

2007 ◽

Cited By ~ 18

Author(s):

Alison J. Head

Keyword(s):

Social Sciences ◽

Exploratory Study ◽

Hybrid Approach ◽

Academic Research ◽

Discussion Groups ◽

The Internet ◽

Student Survey ◽

Quality Research ◽

Library Resources ◽

Student Discussion

This paper reports findings from an exploratory study about how students majoring in humanities and social sciences use the Internet and library resources for research. Using student discussion groups, content analysis, and a student survey, our results suggest students may not be as reliant on public Internet sites as previous research has reported. Instead, students in our study used a hybrid approach for conducting course-related research. A majority of students leveraged both online and offline sources to overcome challenges with finding, selecting, and evaluating resources and gauging professors' expectations for quality research.

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A hybrid approach to building face shape classifier for hairstyle recommender system

Expert Systems with Applications ◽

10.1016/j.eswa.2018.11.011 ◽

2019 ◽

Vol 120 ◽

pp. 14-32 ◽

Cited By ~ 4

Author(s):

Kitsuchart Pasupa ◽

Wisuwat Sunhem ◽

Chu Kiong Loo

Keyword(s):

Recommender System ◽

Hybrid Approach ◽

Face Shape

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Recommender system on social networking site with domain specific and sparse data

10.32920/ryerson.14661834.v1 ◽

2021 ◽

Author(s):

Chuy Chang Nian

Keyword(s):

Social Networking ◽

Recommender System ◽

Social Networking Sites ◽

Information Overload ◽

Learning To Rank ◽

Hybrid Approach ◽

Social Networking Site ◽

Chronological Order ◽

Domain Specific ◽

Recommender Algorithm

In many recent domain-specific social networking sites, posts are organized in chronological order, where later posts are shown first at the top, even though they might not be of everyone's interest. As a result, if users want to read posts that interest them, they will have to scroll down and sift through all the posts. To overcome this information overload problem and relieve users' burden, a recommender system is needed in social networking sites. In this thesis we propose a hybrid approach of Recommender System (RS) that combines both Collaborative Filtering and Content-based approach. Although each approach has their own weaknesses independently, by joining them together we can improve the accuracy of our recommendations. From our experiments, we noticed that using learning to rank algorithms in combining each recommender algorithm greatly enhances the system's performance.

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A Hybrid Recommender System for HCI Design Pattern Recommendations

Applied Sciences ◽

10.3390/app112210776 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10776

Author(s):

Amani Braham ◽

Maha Khemaja ◽

Félix Buendía ◽

Faiez Gargouri

Keyword(s):

Recommender System ◽

Interface Design ◽

Design Patterns ◽

Hybrid Approach ◽

Evaluation Study ◽

Design Problems ◽

Post Test ◽

Standard Solution ◽

Hybrid Recommender ◽

Selection Of

User interface design patterns are acknowledged as a standard solution to recurring design problems. The heterogeneity of existing design patterns makes the selection of relevant ones difficult. To tackle these concerns, the current work contributes in a twofold manner. The first contribution is the development of a recommender system for selecting the most relevant design patterns in the Human Computer Interaction (HCI) domain. This system introduces a hybrid approach that combines text-based and ontology-based techniques and is aimed at using semantic similarity along with ontology models to retrieve appropriate HCI design patterns. The second contribution addresses the validation of the proposed recommender system regarding the acceptance intention towards our system by assessing the perceived experience and the perceived accuracy. To this purpose, we conducted a user-centric evaluation experiment wherein participants were invited to fill pre-study and post-test questionnaires. The findings of the evaluation study revealed that the perceived experience of the proposed system’s quality and the accuracy of the recommended design patterns were assessed positively.

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User-Review Oriented Social Recommender System for Event Planning

Ingénierie des systèmes d information ◽

10.18280/isi.250514 ◽

2020 ◽

Vol 25 (5) ◽

pp. 669-675

Author(s):

Rahul Kumar Singh ◽

Pardeep Singh ◽

Gourav Bathla

Keyword(s):

Recommender System ◽

User Satisfaction ◽

Recommendation System ◽

Hybrid Approach ◽

Social Commerce ◽

System Framework ◽

User Comments ◽

Filtering Technique ◽

Event Based ◽

User Review

Recommender system is used to suggest product or topic based on user’s interest. Existing recommender system have focused on books, product, music etc. The problem in existing recommender system is that wedding/event based suggestions are not available. In the modern information era; storage, communication has been a challenge due to information veracity, volume, and velocity. Due to the constant and exponential growth of information, the utilization of information for context-oriented services is not productive. In this paper, a wedding planner recommender system framework has been proposed based on hybrid approach i.e., content based, collaborative filtering technique. The motive of proposed framework is to generate user-specific recommendations for different tasks related to the event specially wedding event, analyzed from the user comments on his social networking portal. Its main objective is to assist the user for organizing the events by suggesting specific vendors needed to arrange the event activities. Also, it would enhance the sales of location sensitive products in social commerce. The trial study conducted using a set of Facebook users is carried out to validate the proposed recommendation system framework. The success of the proposed framework is reported in terms of the level of user satisfaction achieved.

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A Hybrid Recommender System for Rating Prediction of Arabic Reviews

International Journal of Asian Language Processing ◽

10.1142/s2717554520500101 ◽

2020 ◽

pp. 2050010

Author(s):

Fouzi Harrag ◽

Abdulmalik Salman Al-Salman ◽

Alaa Alquahtani

Keyword(s):

Sentiment Analysis ◽

Recommender Systems ◽

Recommender System ◽

Opinion Mining ◽

Good Accuracy ◽

Hybrid Approach ◽

Data Sparsity ◽

Hybrid Recommender System ◽

Rating Prediction ◽

Hybrid Recommender

Recommender systems nowadays are playing an important role in the delivery of services and information to users. Sentiment analysis (also known as opinion mining) is the process of determining the attitude of textual opinions, whether they are positive, negative or neutral. Data sparsity is representing a big issue for recommender systems because of the insufficiency of user rating or absence of data about users or items. This research proposed a hybrid approach combining sentiment analysis and recommender systems to tackle the problem of data sparsity problems by predicting the rating of products from users’ reviews using text mining and NLP techniques. This research focuses especially on Arabic reviews, where the model is evaluated using Opinion Corpus for Arabic (OCA) dataset. Our system was efficient, and it showed a good accuracy of nearly 85% in predicting the rating from reviews.

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