Papers Collaborative Filtering
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Authorship Identification for Literary Book Recommendations
Book recommender systems can help promote the practice of reading for pleasure, which has been declining in recent years. One factor that influences reading preferences is writing style. We propose a system that recommen…
Collaborative FilteringRecommendation SystemsAn Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weak…
Collaborative FilteringRecommendation SystemsTopic ModelsAlgorithm Selection for Collaborative Filtering: the influence of graph metafeatures and multicriteria metatargets
To select the best algorithm for a new problem is an expensive and difficult task. However, there are automatic solutions to address this problem: using Metalearning, which takes advantage of problem characteristics (i.e…
Collaborative Filteringfeature selectionA Line in the Sand: Recommendation or Ad-hoc Retrieval?
The popular approaches to recommendation and ad-hoc retrieval tasks are largely distinct in the literature. In this work, we argue that many recommendation problems can also be cast as ad-hoc retrieval tasks. To demonstr…
Collaborative FilteringLearning-To-RankMusic RecommendationRetrieval+1Deep Content-User Embedding Model for Music Recommendation
Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach. However, the majority of previous studies proposed a hybrid model where collaborati…
Collaborative FilteringmodelMusic Auto-TaggingMusic Recommendation+1Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence
User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to r…
Collaborative FilteringRecommendation SystemsItem Recommendation with Variational Autoencoders and Heterogenous Priors
In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation of implicit feedback. We extend VAEs to c…
Collaborative FilteringA Distributed Collaborative Filtering Algorithm Using Multiple Data Sources
Collaborative Filtering (CF) is one of the most commonly used recommendation methods. CF consists in predicting whether, or how much, a user will like (or dislike) an item by leveraging the knowledge of the user's prefer…
Collaborative FilteringRecommendation SystemsAnalysis and Modeling of Behavioral Changes in a News Service
Information is transmitted through websites, and immediate reactions to various kinds of information are required. Hence, efforts by users to select information themselves have increased, which is fueling further improve…
ClusteringCollaborative FilteringDiversityRecommendation SystemsA Hybrid Variational Autoencoder for Collaborative Filtering
In today's day and age when almost every industry has an online presence with users interacting in online marketplaces, personalized recommendations have become quite important. Traditionally, the problem of collaborativ…
ClusteringCollaborative FilteringMovie RecommendationRecommendation SystemsA Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion
Recommending venues plays a critical rule in satisfying users' needs on location-based social networks. Recent studies have explored the idea of adopting collaborative ranking (CR) for recommendation, combining the idea …
Collaborative FilteringCollaborative RankingLearning-To-RankCompetitive Analysis System for Theatrical Movie Releases Based on Movie Trailer Deep Video Representation
Audience discovery is an important activity at major movie studios. Deep models that use convolutional networks to extract frame-by-frame features of a movie trailer and represent it in a form that is suitable for predic…
Collaborative FilteringThe Importance of Song Context and Song Order in Automated Music Playlist Generation
The automated generation of music playlists can be naturally regarded as a sequential task, where a recommender system suggests a stream of songs that constitute a listening session. In order to predict the next song in …
Collaborative FilteringRecommendation SystemsLocal Popularity and Time in top-N Recommendation
Items popularity is a strong signal in recommendation algorithms. It strongly affects collaborative filtering approaches and it has been proven to be a very good baseline in terms of results accuracy. Even though we miss…
Collaborative FilteringRecommendation SystemsLearning Thematic Similarity Metric from Article Sections Using Triplet Networks
In this paper we suggest to leverage the partition of articles into sections, in order to learn thematic similarity metric between sentences. We assume that a sentence is thematically closer to sentences within its secti…
ArticlesClusteringCollaborative FilteringDocument Summarization+6Kernelized Synaptic Weight Matrices
In this paper we introduce a novel neural network architecture, in which weight matrices are re-parametrized in terms of low-dimensional vectors, interacting through kernel functions. A layer of our network can be i…
Collaborative FilteringData VisualizationRecommendation SystemsPlay Duration based User-Entity Affinity Modeling in Spoken Dialog System
Multimedia streaming services over spoken dialog systems have become ubiquitous. User-entity affinity modeling is critical for the system to understand and disambiguate user intents and personalize user experiences. Howe…
Collaborative FilteringPersonalizing Similar Product Recommendations in Fashion E-commerce
In fashion e-commerce platforms, product discovery is one of the key components of a good user experience. There are numerous ways using which people find the products they desire. Similar product recommendations is one …
Collaborative FilteringProbabilistic Ensemble of Collaborative Filters
Collaborative filtering is an important technique for recommendation. Whereas it has been repeatedly shown to be effective in previous work, its performance remains unsatisfactory in many real-world applications, especia…
Collaborative FilteringNext Check-ins Prediction via History and Friendship on Location-Based Social Networks
With the thriving of location-based social networks, a large number of user check-in data have been accumulated. Tasks such as the prediction of the next check-in location can be addressed through the usage of LBSN data.…
Collaborative FilteringPrediction