Parallel pairwise learning to rank for collaborative filtering

Yağcı, A. Murat; Aytekin, Tevfik; Gürgen, Fikret

doi:10.1002/cpe.5141

Cited by 3 publications

(2 citation statements)

References 44 publications

(79 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, a few approaches employ optimization techniques to devise a training model in the same manner as classifiers create a training model using marked data. The CF, which has become one of the most popular and widely used applied models in recommender systems (RSs), refers to connecting the applier's data with that of similar applications based on purchasing customs to recommend for the next basket [41,42]. When Amazon recommends an item to a client based on their previous buying history and buying activity of those who purchased that same item, it uses CF techniques.…”

Section: Collaborative Filteringmentioning

confidence: 99%

A Systematic Study on a Customer’s Next-Items Recommendation Techniques

et al. 2022

View full text Add to dashboard Cite

A customer’s next-items recommender system (NIRS) can be used to predict the purchase list of a customer in the next visit. The recommendations made by these systems support businesses by increasing their revenue and providing a more personalized shopping experience to customers. The main objective of this paper is to provide a systematic literature review of the domain to analyze the recent techniques and assist future research. The paper examined 90 selected studies to answer the research questions concerning the key aspects of NIRSs. To this end, the main contribution of the paper is that it provides detailed insight into the use of conventional and deep learning techniques, the popular datasets, and specialized metrics for developing and evaluating these systems. The study reveals that conventional machine learning techniques have been quite popular for developing NIRSs in the past. However, more recent works have mainly focused on deep learning techniques due to their enhanced ability to learn sequential and temporal information. Some of the challenges in developing NIRSs that need further investigation are related to cold start, data sparsity, and cross-domain recommendations.

show abstract

Section: Collaborative Filteringmentioning

confidence: 99%

A Systematic Study on a Customer’s Next-Items Recommendation Techniques

et al. 2022

View full text Add to dashboard Cite

show abstract

“…CF, one of the most popular and widely implemented methods in RSs, is a technique utilized for linking the data of an applier with the data of other alike appliers depending on buying patterns to produce directions for the user for prospective shopping [71,75,85,[107][108][109][110][111][112][113][114]. Amazon uses CF techniques for making its recommendations depending on a client's previous buying and buying of those that bought the same products.…”

Section: Overview Of Collaborative Filtering Mechanismmentioning

confidence: 99%

A Systematic Study on the Recommender Systems in the E-Commerce

et al. 2020

View full text Add to dashboard Cite

Electronic commerce or e-commerce includes the service and good exchange through electronic support like the Internet. It plays a crucial role in today's business and users' experience. Also, e-commerce platforms produce a vast amount of information. So, Recommender Systems (RSs) are a solution to overcome the information overload problem. They provide personalized recommendations to improve user satisfaction. The present article illustrates a comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of the e-commerce recommender systems. We reviewed the selected papers to identify the gaps and significant issues of the RSs' traditional methods, which guide the researchers to do future work. So, we provided the traditional techniques, challenges, and open issues concerning traditional methods of the field of review based on the selected papers. This review includes five categories of the RSs' algorithms, including Content-Based Filtering (CBF), Collaborative Filtering (CF), Demographic-Based Filtering (DBF), hybrid filtering, and Knowledge-Based Filtering (KBF). Also, the salient points of each selected paper are briefly reported. The publication time of the selected papers ranged from 2008 to 2019. Also, we provided a comparison table of important issues of the selected papers as well as the tables of advantages and disadvantages. Moreover, we provided a comparative table of metrics and review issues for the selected papers. And finally, the conclusions can, to a great extent, provide valuable guidelines for future studies.

show abstract

A Meta-Algorithm for Improving Top-N Prediction Efficiency of Matrix Factorization Models in Collaborative Filtering

Yağcı

Aytekin

Gürgen

2019

Int. J. Patt. Recogn. Artif. Intell.

View full text Add to dashboard Cite

Matrix factorization models often reveal the low-dimensional latent structure in high-dimensional spaces while bringing space efficiency to large-scale collaborative filtering problems. Improving training and prediction time efficiencies of these models are also important since an accurate model may raise practical concerns if it is slow to capture the changing dynamics of the system. For the training task, powerful improvements have been proposed especially using SGD, ALS, and their parallel versions. In this paper, we focus on the prediction task and combine matrix factorization with approximate nearest neighbor search methods to improve the efficiency of top-N prediction queries. Our efforts result in a meta-algorithm, MMFNN, which can employ various common matrix factorization models, drastically improve their prediction efficiency, and still perform comparably to standard prediction approaches or sometimes even better in terms of predictive power. Using various batch, online, and incremental matrix factorization models, we present detailed empirical analysis results on many large implicit feedback datasets from different application domains.

show abstract

Parallel pairwise learning to rank for collaborative filtering

Cited by 3 publications

References 44 publications

A Systematic Study on a Customer’s Next-Items Recommendation Techniques

A Systematic Study on a Customer’s Next-Items Recommendation Techniques

A Systematic Study on the Recommender Systems in the E-Commerce

A Meta-Algorithm for Improving Top-N Prediction Efficiency of Matrix Factorization Models in Collaborative Filtering

Contact Info

Product

Resources

About