1. Recommending Users: Whom to Follow on Federated Social Networks
Jan Trienes, Andrés Torres Cano, Djoerd Hiemstra
Popular social networks such as Facebook and Twitter generate follow recommendations by listing profiles a user may be interested to connect with. Federated social networks aim to resolve issues associated with the popular social networks - such as large-scale user-surveillance and the miss-use of user data to manipulate elections - by decentralizing authority and promoting privacy. Due to their recent emergence, recommender systems do not exist for federated social networks, yet.
2. Attentive Neural Architecture Incorporating Song Features For Music Recommendation
Noveen Sachdeva, Kartik Gupta, Vikram Pudi
Often the aim of these systems is to increase the time, the user spends on the platform and hence having a high commercial value. Prediction of the next song the user might like requires some kind of modeling of the user interests at the given point of time. In this direction, we propose a novel attentive neural architecture which in addition to the sequence of items selected by the user, uses the features of these items to better learn the user short-term preferences and recommend the next song to the user.
3. Bias Disparity in Recommendation Systems
Virginia Tsintzou, Evaggelia Pitoura, Panayiotis Tsaparas
More importantly, there are cases where recommenders may amplify such biases, leading to the phenomenon of bias disparity. In this short paper, we present a preliminary experimental study on synthetic data, where we investigate different conditions under which a recommender exhibits bias disparity, and the long-term effect of recommendations on data bias. We also consider a simple re-ranking algorithm for reducing bias disparity, and present some observations for data disparity on real data.
4. GEMRank: Global Entity Embedding For Collaborative Filtering
Arash Khoeini, Bita Shams, Saman Haratizadeh
Unlike many other domains, this approach has not achieved a desired performance in collaborative filtering problems, probably due to unavailability of appropriate textual data. It uses the concept of profile co-occurrence for defining relations among entities and applies a factorization method for embedding the users and items. The results show that GEMRank significantly outperforms the baseline algorithms in a variety of data sets with different degrees of density.
5. Deep Item-based Collaborative Filtering for Top-N Recommendation
Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, Richang Hong
Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. We treat this solution as a deep variant of ICF, thus term it as DeepICF.
6. Fast Non-Bayesian Poisson Factorization for Implicit-Feedback Recommendations
This work explores non-negative matrix factorization based on regularized Poisson models for recommender systems with implicit-feedback data. The properties of Poisson likelihood allow a shortcut for very fast computation and optimization over elements with zero-value when the latent-factor matrices are non-negative, making it a more suitable approach than squared loss for very sparse inputs such as implicit-feedback data. A simple and embarrassingly parallel optimization approach based on proximal gradients is presented, which in large datasets converges 2-3 orders of magnitude faster than its Bayesian counterpart (Hierarchical Poisson Factorization) fit through variational inference techniques, and 1 order of magnitude faster than implicit-ALS fit with the Conjugate Gradient method.