A Symbolic Hybrid Approach to Face the New User Problem in Recommender Systems

2004 ◽  
pp. 1011-1016 ◽  
Author(s):  
Byron Bezerra ◽  
Francisco de A. T. de Carvalho
Keyword(s):  
Recommender Systems ◽  
Hybrid Approach

A Hybrid Filtering Approach for an Improved Context-aware Recommender System

2019 ◽  
Vol 13 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Mugdha Sharma ◽  
Laxmi Ahuja ◽  
Vinay Kumar
Keyword(s):  
Recommender Systems ◽  
Performance Improvement ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Context Aware ◽  
Post Evaluation ◽  
Contextual Modeling ◽  
Filter Approach ◽  
Single Approach ◽  
Post Filter

Background: The domain of context-aware recommender approaches has made a 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. Objective: 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 if any single approach is chosen. The goal of this work is to identify and propose a new hybrid approach which can include contextual information to improve the current movie recommender systems. Method: Post evaluation of various patents related to recommender systems, 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. Results: The performance of the proposed system is measured in terms of precision of the system and ranking of the recommended movies to the user. The results of experimental setup also demonstrate that the proposed system improves the precision and ranking of the recommendations provided to the user. Conclusion: 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. The proposed system will be vital for movie ticketing brands for the promotional purposes and various online content providers to recommend the accurate movies to their users.

scholarly journals Examining the Usefulness of Quality Scores for Generating Learning Object Recommendations in Repositories of Open Educational Resources

2020 ◽  
Vol 10 (13) ◽  
pp. 4638 ◽  
Author(s):  
Aldo Gordillo ◽  
Daniel López-Fernández ◽  
Katrien Verbert
Keyword(s):  
Recommender Systems ◽  
User Study ◽  
Hybrid Approach ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Learning Object ◽  
Personalized Recommendation ◽  
Effective Learning ◽  
Pedagogical Quality

Open educational resources (OER) can contribute to democratize education by providing effective learning experiences with lower costs. Nevertheless, the massive amount of resources currently available in OER repositories makes it difficult for teachers and learners to find relevant and high-quality content, which is hindering OER use and adoption. Recommender systems that use data related to the pedagogical quality of the OER can help to overcome this problem. However, studies analyzing the usefulness of these data for generating OER recommendations are very limited and inconclusive. This article examines the usefulness of using pedagogical quality scores for generating OER recommendations in OER repositories by means of a user study that compares the following four different recommendation approaches: a traditional content-based recommendation technique, a quality-based non-personalized recommendation technique, a hybrid approach that combines the two previous techniques, and random recommendations. This user study involved 53 participants and 400 OER whose quality was evaluated by reviewers using the Learning Object Review Instrument (LORI). The main finding of this study is that pedagogical quality scores can enhance traditional content-based OER recommender systems by allowing them to recommend OER with more quality without detriment to relevance.

A Hybrid Recommender System for Rating Prediction of Arabic Reviews

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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