Collaborator Recommender System
Nowadays, a lot of scientists’ works aim to improve the quality of people’s life but it could be quite complicated without building a successful collaboration. Productive partnerships can increase research efficiency in many cases and make a huge impact on society. However, today there is no clear way to find such collaborators. In this paper, we propose a recommender system for the scientists from the Higher School of Economics university to help them find co-authors for their prospective studies.
In this paper we show how several similarity measures can be combined for finding similarity between a pair of users for performing Collaborative Filtering in Recommender Systems. Through aggregation of several measures we find super similar and super dissimilar user pairs and assign a different similarity value for these types of pairs. We also introduce another type of similarity relationship which we call medium similar user pairs and use traditional JMSD for assigning similarity values for them. By experimentation with real data we show that our method for finding similarity by aggregation performs better than each of the similarity metrics. Moreover, as we apply all the traditional metrics in the same setting, we can assess their relative performance
This volume contains the papers presented at the ACM RecSys Challenge 2015 workshop held on September 16, 2015, in Vienna, Austria. The challenge offered participants the opportunity to work on a large-scale e-commerce dataset from a big retailer in Europe. Participants tackled the problem of predicting what items a user intends to purchase, if any, given a click sequence performed during an activity session on the e-commerce website. The challenge was launched on November 15, 2014, and ran for seven months, attracting 850 teams from 49 countries which submitted a total of 5,437 solutions. The winners were determined based on the final ranking of the scores at the end of the challenge. However, in order to receive the monetary prize, the participants were required to submit, and have accepted, a paper detailing the applied algorithms, and attend the challenge's workshop. There were 22 submissions, and each submission was reviewed by at least two program committee members. The following table contains a summary of the 12 accepted papers and the corresponding score and rank in the final leaderboard.
We propose a new approach for Collaborative filtering which is based on Boolean Matrix Factorisation (BMF) and Formal Concept Analysis. In a series of experiments on real data (MovieLens dataset) we compare the approach with an SVD-based one in terms of Mean Average Error (MAE). One of the experimental consequences is that it is enough to have a binary-scaled rating data to obtain almost the same quality in terms of MAE by BMF as for the SVD-based algorithm in case of non-scaled data.
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering in order to incorporate structural information into predictive model. Nowadays, a family of automated graph feature engineering techniques have been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation.
Our survey aims to describe the core concepts of graph embeddings, and provide several taxonomies for their description. First, we start with methodological approach, and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability to of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different embedding and graph properties are connected to the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization.
As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.
We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence network and use its embedding for further generalizing author attributes. Standard graph feature engineering and network embedding methods were combined for constructing co-author recommender system formulated as LP problem and prediction of future graph structure. We evaluate our survey on the dataset containing temporal information on National Research University Higher School of Economics over 25 years of research articles indexed in Russian Science Citation Index and Scopus. Our model of network representation shows better performance for stated binary classification tasks on several co-authorship networks.
In this paper, we study network feature engineering for the problem of future co-author recommendation, also called collaborator recommender system. We present a system, which uses authors' research interests and existing collaboration information to predict missing and most probable in the future links in the co-authorship network. The recommender system is stated as a link prediction problem for the current network and for new edges that appear next year. From machine learning point of view, both problems are treated as binary classification. We evaluate our research on our University researchers co-authorship network, while also mentioning results on sub-network of publications indexed in Scopus. Our approach has high accuracy and provides scalable solution for any significantly large co-authorship network.
This volume contains the papers that were presented at the ACM Recommender Systems Challenge Workshop 20181 which was held at ACM RecSys 2018, the 12th ACM Conference on Recommender Systems. The authors of these papers participated in the RecSys Challenge 2018 by designing and implementing recommender system algorithms for automatic music playlist continuation. We received 24 paper submissions, each of which received between two and four reviews from recognized experts in the area of recommender systems, information retrieval, and music. We eventually accepted 16 for presentation in the workshop.