Second Order Online Collaborative Filtering

Asian Conference on Machine Learning 5th ACML 2013, November 13-15, Canberra</p

Sparse online collaborative filtering with dynamic regularization

Abstract(#br)Collaborative filtering (CF) approaches are widely applied in recommender systems. Traditional CF approaches have high costs to train the models and cannot capture changes in user interests and item popularity. Most CF approaches assume that user interests remain unchanged throughout the whole process. However, user preferences are always evolving and the popularity of items is always changing. Additionally, in a sparse matrix, the amount of known rating data is very small. In this paper, we propose a method of online collaborative filtering with dynamic regularization (OCF-DR), that considers dynamic information and uses the neighborhood factor to track the dynamic change in online collaborative filtering (OCF). The results from experiments on the MovieLens100K, MovieLens1M, and HetRec2011 datasets show that the proposed methods are significant improvements over several baseline approaches

Large-scale online feature selection for ultra-high dimensional sparse data

National Research Foundation (NRF) Singapore under International Research Centre @ Singapore Funding Initiativ

Soft confidence-weighted learning

Ministry of Education, Singapore under its Academic Research Funding Tier 1; Microsoft Research Gran

Evaluation of Citation Graph Thematic Dataset Construction and Paper Filtering Methods for Research Literature Recommendation

One of the main challenges faced by new researchers is immersing themselves in the existing literature relevant to their field of interest. The vastness and continuous growth of knowledge in their field can be overwhelming, making it difficult to identify the most pertinent research papers within their research themes. To address this issue, research paper recommender systems have emerged as valuable tools. These systems allow researchers to find relevant papers based on their specific interests or research themes by analyzing various aspects such as titles, abstracts, and full texts. The quality of the dataset used is crucial for the development, testing, and refinement of these systems to ensure optimal results. Dataset quality directly impacts the accuracy and reliability of a recommender system. In this thesis, I propose a novel approach for constructing datasets using citation graph networks. These networks consist of nodes representing research papers and edges representing citations between them. By leveraging citation graph networks, we gain a more comprehensive understanding of the relationships and influences among different papers compared to traditional methods that rely solely on keyword searches. To evaluate the effectiveness of the citation graph network method, I compared it with the traditional keyword search approach for dataset construction. Additionally, I assessed the effectiveness of three recommender system algorithms: user-based collaborative filtering, combined with PageRank and personalized PageRank algorithms. The experimental findings provide clear evidence that utilizing citation graph network datasets significantly enhances the efficacy of research paper recommender systems. This improvement simplifies the process of finding relevant literature for researchers, potentially accelerating scientific discovery

The use of machine learning algorithms in recommender systems: A systematic review

The final publication is available at Elsevier via https://doi.org/10.1016/j.eswa.2017.12.020 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics.Natural Sciences and Engineering Research Council of Canada (NSERC) Ontario Research Fund of the Ontario Ministry of Research, Innovation, and Scienc

Second Order Online Collaborative Filtering

Collaborative Filtering (CF) is one of the most successful learning techniques in building real-world recommender systems. Traditional CF algorithms are often based on batch machine learning methods which suffer from several critical drawbacks, e.g., extremely expensive model retraining cost whenever new samples arrive, unable to capture the latest change of user preferences over time, and high cost and slow reaction to new users or products extension. Such limitations make batch learning based CF methods unsuitable for real-world online applications where data often arrives sequentially and user preferences may change dynamically and rapidly. To address these limitations, we investigate online collaborative filtering techniques for building live recommender systems where the CF model can evolve on-the-fly over time. Unlike the regular first order CF algorithms (e.g., online gradient descent for CF) that converge slowly, in this paper, we present a new framework of second order online collaborative filtering, i.e., Confidence Weighted Online Collaborative Filtering (CWOCF), which applies the second order online optimization methodology to tackle the online collaborative filtering task. We conduct extensive experiments on several large-scale datasets, in which the encouraging results demonstrate that the proposed algorithms obtain significantly lower errors (both RMSE and MAE) than the state-of-the-art first order CF algorithms when receiving the same amount of training data in the online learning process