A Context Based Recommender System through Collaborative Filtering and Word Embedding Techniques.

In the content-based approach, all Web pages related to attractions are modeled as vectors of real numbers using word embedding and document embedding techniques [1] . ... Introduction This report presents a description of the context-based recommender system that was developed by the FUM-IR team from the Ferdowsi University of Mashhad for the Contextual Suggestion track ...

dblp:conf/trec/KhorasaniSRE16 fatcat:35ne5giex5g4bgv2cwqtx4pvae

Some hybrid recommender systems have combined collaborative filtering and content-based approaches to build systems that are more robust. ... In this paper, we propose a hybrid recommender system, which combines Alternative Least Squares (ALS) based collaborative filtering with deep learning to enhance recommendation performance as well as overcome ... Based on this, there are four types of algorithms used in recommender systems, namely, collaborative filtering, content-based filtering, context-based filtering and hybrid filtering. ...

arXiv:2105.12876v1 fatcat:6q3wpnmkerd7pnnpsfhvbilxca

Multiple Versions

Our findings are threefold: (1) the quality of recommendations provided by user2vec is notably higher than current state-of-the-art techniques. (2) User representations generated through hybrid filtering ... We apply these methods to data from a large mobile ad exchange and additional app metadata acquired from the Apple App store and Google Play store. ... RELATED WORK Traditionally, user modeling is the domain of recommendation systems. The most prevalent techniques in this field are contentbased filtering (CBF) and collaborative filtering (CF). ...

arXiv:1801.00215v1 fatcat:avcszeivnjbjxkem6nfe4ctjci

Majorly, content and collaborative filtering techniques are employed in typical recommendation systems to find user preferences and provide final recommendations. ... After pre-processing, the TF–IDF and word embedding model is used for every pre-processed reviews to extract the features and contextual information. ... The most prominent strategies of the recommendation systems are the Content and Collaborative filtering method, Hybrid recommendation, Knowledge-based filtering, Demographic method and, Model-based technique ...

doi:10.1186/s40537-021-00534-7 fatcat:wcpwgwdmwbdtdl3osmmeqrngwi

DOAJ Szczepanski

Our focus is to investigate approaches and techniques used in the development of context-aware recommender systems for social networks and identify the research gaps, challenges, and opportunities in this ... In this research, we present a comprehensive review of context-aware recommender systems developed for social networks. ... ACKNOWLEDGEMENTS This work was supported by the Research Center of College of Computer and Information Sciences, King Saud University. The authors are grateful for this support. ...

doi:10.1109/access.2021.3072165 fatcat:i3igbxd44jhrzcyvynevpidcwq

DOAJ

The advancement of technologies has modernized learning within smart campuses and has emerged new context through communication between mobile devices. ... To tackle the sparsity problem, smart learning recommendation-based approach is proposed for inferring the format of the suitable Arabic document in a contextual situation. ... Ansari et al. (2016) proposed a hybrid (content-based and collaborative filtering) and context-specific recommendation system for interactive programming online learning for Persian-speaking users. ...

doi:10.4018/ijwltt.20211101.oa9 fatcat:7iebopwtcjf73l5znqij6zwn5i

This work aims to improve the quality of recommendation and the simplicity of recommendation explanation based on the word2vec graph embeddings model. ... According to the user profile, a recommender system intends to offer items to the user that may interest him. The recommendations have been applied successfully in various fields. ... Among the most popular collaborative systems we cited [11] :  Ringo: is a system based on collaborative filtering that recommends music albums and artists. ...

doi:10.14569/ijacsa.2021.0120876 fatcat:67zged2ubrabrdkthuoyn3md7a

Szczepanski

The purpose of this paper is to build a hybrid restaurant recommendation technique which is the combination of content-based filtering, user-based collaborative filtering and item-based collaborative filtering ... Collaborative filtering technique has two types, userbased collaborative filtering and item-based collaborative filtering [1]. ... Recommender Systems can be categorized into two main types: content-based recommender systems and collaborative filtering recommender systems. ...

doi:10.4108/eai.19-12-2018.156079 fatcat:vzq6zbyai5eq7ncbachotdsgdu

DOAJ

Even though word embeddings (i.e., vector representations of textual descriptions) have proven to be effective in many contexts, a content-based recommendation approach that employs them is still less ... Experiments show its effectiveness to perform a better ranking of the items, w.r.t. collaborative filtering, both when compared to a latent-factor-based approach (SVD) and to a classic neighborhood user-based ... The use of the LIR metric can be also extended to other contexts that do not use word embeddings, e.g., those based on a canonical term-document matrix. Future work. ...

dblp:conf/recsys/BorattoCFS16 fatcat:wlqgzo4ayvgzxjtninkuq7ltyy

This paper presents a deep learning based solution to this problem within the collaborative filtering with implicit feedback framework. ... The application of machine learning techniques to large-scale personalized recommendation problems is a challenging task. ... through a depth three version of the attention based collaborative filtering model. ...

arXiv:1901.04321v1 fatcat:qc7b5qg66zfhvhsq47nsw4hhbu

This paper proposes a Hybrid Model to address the sparsity problem, convolutional neural network and topic modeling for recommender system, which extract the contextual features of both items and users ... To handle data sparsity problem most recommender systems utilized deep learning techniques for in-depth analysis of item content to generate more accurate recommendations. ... National University of Science and Technology (NUST) for supporting this work. ...

doi:10.14569/ijacsa.2020.0110775 fatcat:scomusfihrfrzkyojygpl7ylqq

Szczepanski

Recently, word embedding algorithms have been applied to map the entities of recommender systems, such as users and items, to new feature spaces using textual element-context relations among them. ... Unlike many other domains, this approach has not achieved a desired performance in collaborative filtering problems, probably due to unavailability of appropriate textual data. ... Many prominent recommendation systems are using Collaborative Filtering (CF) for making recommendations ( [1] ). ...

arXiv:1811.01686v1 fatcat:mlyglao7qrfv3huunoym5hodtu

In this research, we have analyzed several papers and majority of them have used collaborative and content-based filtering techniques to implement recommender system. ... Various techniques have been used over the years to implement recommendation systems. ... Popular recommender systems use two main approaches in their implementation: Collaborative filtering and context-based filtering. Knowledge based recommender system have also been implemented. ...

doi:10.5815/ijieeb.2021.03.03 fatcat:nzqk7x6w5zf4zen52t4utfsoye

Open Access

In this proposed methodology, we compared document vectors with word embedding. Experiments show that word embedding method is more effective than the document vector.2 ... The primary purpose of this research study is to exploit the class NLP techniques to perform the information retrieval task for resume ranking based on job description similarity. ... Reference [9] , has worked on a recommended system which is using a hybrid approach. This system is a combination of methods, such as content-based filtering and collaborative filtering. ...

doi:10.5281/zenodo.5089433 fatcat:dx4tljt6wzalfor2ouoi7wbuw4

Open Access

Our system is a hybrid of collaborative-filtering and the content-based filtering. ... We propose a recommendation system based on the binary classification problem which takes as input a combination of the user, item and entity embeddings and computes the probability of the user clicking ... Two popular approaches of recommendation systems are collaborative filtering and contentbased filtering which form the basis for major recommendation systems. ...

dblp:conf/recsys/Verma19 fatcat:adquxd6a4vhitoovtpj2nnq42i

A Context Based Recommender System through Collaborative Filtering and Word Embedding Techniques

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