Papers Collaborative Filtering
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Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems
Like other social systems, in collaborative filtering a small number of "influential" users may have a large impact on the recommendations of other users, thus affecting the overall behavior of the system. Identifying in…
Collaborative FilteringRecommendation SystemsCompositional Coding for Collaborative Filtering
Efficiency is crucial to the online recommender systems. Representing users and items as binary vectors for Collaborative Filtering (CF) can achieve fast user-item affinity computation in the Hamming space, in recent yea…
Collaborative FilteringRecommendation SystemsEmbarrassingly Shallow Autoencoders for Sparse Data
Combining simple elements from the literature, we define a linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems. We show that its training objective has a closed-fo…
Collaborative FilteringRecommendation SystemsOn the Difficulty of Evaluating Baselines: A Study on Recommender Systems
Numerical evaluations with comparisons to baselines play a central role when judging research in recommender systems. In this paper, we show that running baselines properly is difficult. We demonstrate this issue on two …
Collaborative FilteringRecommendation SystemsTemporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization
Real-world relational data are seldom stationary, yet traditional collaborative filtering algorithms generally rely on this assumption. Motivated by our sales prediction problem, we propose a factor-based algorithm tha…
Collaborative FilteringMovie RecommendationRecommendation SystemsVariational Autoencoders for Sparse and Overdispersed Discrete Data
Many applications, such as text modelling, high-throughput sequencing, and recommender systems, require analysing sparse, high-dimensional, and overdispersed discrete (count-valued or binary) data. Although probabilistic…
Collaborative FilteringMulti-Label LearningRecommendation SystemsHerding Effect based Attention for Personalized Time-Sync Video Recommendation
Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for video recommendat…
Collaborative FilteringClustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model
Giving or recommending appropriate content based on the quality of experience is the most important and challenging issue in recommender systems. As collaborative filtering (CF) is one of the most prominent and popular t…
ClusteringCollaborative FilteringRecommendation SystemsCollaborative Filtering via High-Dimensional Regression
While the SLIM approach obtained high ranking-accuracy in many experiments in the literature, it is also known for its high computational cost of learning its parameters from data. For this reason, we focus in this paper…
Collaborative FilteringregressionVocal Bursts Intensity PredictionRelational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the coll…
Collaborative FilteringRecommendation SystemsRelationAdaptive Matrix Completion for the Users and the Items in Tail
Recommender systems are widely used to recommend the most appealing items to users. These recommendations can be generated by applying collaborative filtering methods. The low-rank matrix completion method is the state-o…
Collaborative FilteringLow-Rank Matrix CompletionMatrix CompletionRecommendation SystemsLearning from Sets of Items in Recommender Systems
Most of the existing recommender systems use the ratings provided by users on individual items. An additional source of preference information is to use the ratings that users provide on sets of items. The advantages of …
Collaborative FilteringRecommendation SystemsHCFContext: Smartphone Context Inference via Sequential History-based Collaborative Filtering
Mobile context determination is an important step for many context aware services such as location-based services, enterprise policy enforcement, building or room occupancy detection for power or HVAC operation, etc. Esp…
Collaborative FilteringPrivacy PreservingA Neural Influence Diffusion Model for Social Recommendation
Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering(CF) provides a way to learn user and item embeddings from the user-item interaction …
Collaborative FilteringRecommendation SystemsA Personalized Preference Learning Framework for Caching in Mobile Networks
This paper comprehensively studies a content-centric mobile network based on a preference learning framework, where each mobile user is equipped with a finite-size cache. We consider a practical scenario where each user …
Collaborative FilteringScaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions
Recommender system research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap, we propose to generate large-scale user/item inter…
Collaborative FilteringRecommendation SystemsDual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assume that social effects from friend users…
Collaborative FilteringGraph AttentionRecommendation SystemsPersonalized Neural Embeddings for Collaborative Filtering with Text
Collaborative filtering (CF) is a core technique for recommender systems. Traditional CF approaches exploit user-item relations (e.g., clicks, likes, and views) only and hence they suffer from the data sparsity issue. It…
Collaborative FilteringRecommendation SystemsWord EmbeddingsKnowledge Graph Convolutional Networks for Recommender Systems
To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these …
Click-Through Rate PredictionCollaborative FilteringLink PredictionMusic Recommendation+1User-based collaborative filtering approach for content recommendation in OpenCourseWare platforms
A content recommender system or a recommendation system represents a subclass of information filtering systems which seeks to predict the user preferences, i.e. the content that would be most likely positively "rated" by…
Collaborative FilteringRecommendation Systems