Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach

Internet based recruiting platforms decrease advertisement cost, but they suffer from information overload problem. The job recommendation systems (JRS) have achieved success in e-recruitment process but still they are not able to capture the complexity of matching between candidates’ desires and organizations’ requirements. Thus, we propose a hybrid JRS which combines recommendations of content-based filtering (CBF) and collaborative filtering (CF) to overcome their individual major shortcomings namely overspecialization and over-fitting. In proposed system, CBF model makes recommendations based on candidates’ skills identified from past jobs in which they have applied and CF model makes recommendations based on jobs in which similar users have applied and also those jobs in which that user has applied frequently together in very similar contexts using Word2Vec’s skip-gram model. We used k-Nearest Neighbors technique and Pearson Correlation Coefficient. The recall of our proposed model is found to be 63.97% on a data set which had nearly 1900+ jobs and 23,000 job applicants

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Joint Information Extraction and Reasoning: A Scalable Statistical Relational Learning Approach

10.3115/v1/p15-1035 ◽

2015 ◽

Cited By ~ 2

Author(s):

William Yang Wang ◽

William W Cohen

Keyword(s):

Information Extraction ◽

Relational Learning ◽

Statistical Relational Learning ◽

Learning Approach

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Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review

Collaborative Filtering Using Data Mining and Analysis - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-0489-4.ch014 ◽

2017 ◽

pp. 250-269

Author(s):

Lediona Nishani ◽

Marenglen Biba

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Word Of Mouth ◽

State Of The Art ◽

Relational Learning ◽

Statistical Relational Learning ◽

Public Opinion Surveys ◽

Making Choices ◽

Key Issues ◽

New Item

People nowadays base their behavior by making choices through word of mouth, media, public opinion, surveys, etc. One of the most prominent techniques of recommender systems is Collaborative filtering (CF), which utilizes the known preferences of several users to develop recommendation for other users. CF can introduce limitations like new-item problem, new-user problem or data sparsity, which can be mitigated by employing Statistical Relational Learning (SRLs). This review chapter presents a comprehensive scientific survey from the basic and traditional techniques to the-state-of-the-art of SRL algorithms implemented for collaborative filtering issues. Authors provide a comprehensive review of SRL for CF tasks and demonstrate strong evidence that SRL can be successfully implemented in the recommender systems domain. Finally, the chapter is concluded with a summarization of the key issues that SRLs tackle in the collaborative filtering area and suggest further open issues in order to advance in this field of research.

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Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review

Natural Language Processing ◽

10.4018/978-1-7998-0951-7.ch033 ◽

2020 ◽

pp. 688-707

Author(s):

Lediona Nishani ◽

Marenglen Biba

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Word Of Mouth ◽

State Of The Art ◽

Relational Learning ◽

Statistical Relational Learning ◽

Public Opinion Surveys ◽

Making Choices ◽

Key Issues ◽

New Item

People nowadays base their behavior by making choices through word of mouth, media, public opinion, surveys, etc. One of the most prominent techniques of recommender systems is Collaborative filtering (CF), which utilizes the known preferences of several users to develop recommendation for other users. CF can introduce limitations like new-item problem, new-user problem or data sparsity, which can be mitigated by employing Statistical Relational Learning (SRLs). This review chapter presents a comprehensive scientific survey from the basic and traditional techniques to the-state-of-the-art of SRL algorithms implemented for collaborative filtering issues. Authors provide a comprehensive review of SRL for CF tasks and demonstrate strong evidence that SRL can be successfully implemented in the recommender systems domain. Finally, the chapter is concluded with a summarization of the key issues that SRLs tackle in the collaborative filtering area and suggest further open issues in order to advance in this field of research.

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Short-term building energy model recommendation system: A meta-learning approach

Applied Energy ◽

10.1016/j.apenergy.2016.03.112 ◽

2016 ◽

Vol 172 ◽

pp. 251-263 ◽

Cited By ~ 45

Author(s):

Can Cui ◽

Teresa Wu ◽

Mengqi Hu ◽

Jeffery D. Weir ◽

Xiwang Li

Keyword(s):

Recommendation System ◽

Building Energy ◽

Energy Model ◽

Learning Approach ◽

Short Term ◽

System A ◽

Meta Learning

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A novel Based-Approach Composed of Clustering Algorithm & Cosine Similarity for Products Recommendation

International Journal of Education and Information Technologies ◽

10.46300/9109.2020.14.16 ◽

2020 ◽

Vol 14 ◽

Keyword(s):

Collaborative Filtering ◽

Clustering Algorithm ◽

Recommendation System ◽

Cosine Similarity ◽

New Approach ◽

System A ◽

Weak Points ◽

Cosine Similarity Measure ◽

High Level ◽

Content Based Filtering

There are huge tons of transactions being accomplished online every day. This implies that ecommerce is facing the problem of data and information overloads. While customers are shopping via websites, they spend a lot of time to search for the required products based on their needs. This problem can easily be alleviated by having an accurate recommendation system based on a strong algorithm and confident measures in this regard. There are two main techniques for products recommendation; content-based filtering and collaborative filtering. If one of these two techniques implemented on the e-commerce system, a lot of limitations and weak points will appear. This paper aims at generating an optimal list of product, which, in turn, generates an accurate and reliable list of items. The new approach is composed of three components; clustering algorithm, user-based collaborative filtering, and the Cosine similarity measure. This approach implemented using a real dataset of past experienced users. The accuracy of the search results is a matter to users, it recommends the most appropriate products to users of the e-commerce website. This approach shows trustworthy results and achieved a high level of accuracy for recommending products to users.

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