Application of Graph Embedding to Constructing Graph-based Recommender System
In this paper we show that for a given co-authorship network we could construct a recommender system for searching collaborators with similar research interests defined via keywords and topic modelling. We suggest new link embedding method and evaluate our model on National Research University Higher School of Economics (NRU HSE) co-authorship network.
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 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.
The evaluation of research performance increasingly relies on quantitative indicators determined by national science policies. We focus on two dimensions of research performance—productivity and excellence—as defined in the evaluation methodology of the Slovenian Research Agency. Our analysis focuses on the effects of two science policy factors—co-authorship collaboration and researcher funding—on the productivity and excellence of Slovenian researchers at the level of research disciplines. A multilevel analysis using a hierarchical linear model with regression analysis was applied to the data with several nested levels. As many variables have a semi-continuous distribution, a statistical model was used to address them. The results show a very strong positive effect of international co-authorship collaboration on productivity and excellence, while fragmentation of funding shows a negative impact only on excellence. We also include interviews with excellent Slovenian researchers regarding their views on scientific excellence and quantitative indicators.
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.