Papers Collaborative Filtering

Learning Distributed Representations from Reviews for Collaborative Filtering

Recent work has shown that collaborative filter-based recommender systems can be improved by incorporating side information, such as natural language reviews, as a way of regularizing the derived product representations.…

Privacy-preserving Targeted Advertising

Recommendation systems form the center piece of a rapidly growing trillion dollar online advertisement industry. Even with numerous optimizations and approximations, collaborative filtering (CF) based approaches require …

Self-Attentive Neural Collaborative Filtering

This paper has been withdrawn as we discovered a bug in our tensorflow implementation that involved accidental mixing of vectors across batches. This lead to different inference results given different batch sizes which …

Handling Cold-Start Collaborative Filtering with Reinforcement Learning

A major challenge in recommender systems is handling new users, whom are also called $\textit{cold-start}$ users. In this paper, we propose a novel approach for learning an optimal series of questions with which to inter…

An Estimation and Analysis Framework for the Rasch Model

The Rasch model is widely used for item response analysis in applications ranging from recommender systems to psychology, education, and finance. While a number of estimators have been proposed for the Rasch model over t…

How Much Are You Willing to Share? A "Poker-Styled" Selective Privacy Preserving Framework for Recommender Systems

Most industrial recommender systems rely on the popular collaborative filtering (CF) technique for providing personalized recommendations to its users. However, the very nature of CF is adversarial to the idea of user pr…

Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation

With the rise of e-commerce, people are accustomed to writing their reviews after receiving the goods. These comments are so important that a bad review can have a direct impact on others buying. Besides, the abundant in…

EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet

Nowadays, social media have become a platform where people can easily express their opinions and emotions about any topic such as politics, movies, music, electronic products and many others. On the other hand, politicia…

Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations

With the emergence of Web 2.0, tag recommenders have become important tools, which aim to support users in finding descriptive tags for their bookmarked resources. Although current algorithms provide good results in term…

Learning Restricted Boltzmann Machines via Influence Maximization

Graphical models are a rich language for describing high-dimensional distributions in terms of their dependence structure. While there are algorithms with provable guarantees for learning undirected graphical models in a…

A Hybrid Approach to Music Playlist Continuation Based on Playlist-Song Membership

Automated music playlist continuation is a common task of music recommender systems, that generally consists in providing a fitting extension to a given playlist. Collaborative filtering models, that extract abstract pat…

Addressing the Item Cold-start Problem by Attribute-driven Active Learning

In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g…

NPE: Neural Personalized Embedding for Collaborative Filtering

Matrix factorization is one of the most efficient approaches in recommender systems. However, such algorithms, which rely on the interactions between users and items, perform poorly for "cold-users" (users with little hi…

Collaborative Item Embedding Model for Implicit Feedback Data

Collaborative filtering is the most popular approach for recommender systems. One way to perform collaborative filtering is matrix factorization, which characterizes user preferences and item attributes using latent vect…

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms - especially collaborative filtering (CF)-based approaches with shallow or deep mo…

A Large Multilingual and Multi-domain Dataset for Recommender Systems

Collaborative Memory Network for Recommendation Systems

Recommendation systems play a vital role to keep users engaged with personalized content in modern online platforms. Deep learning has revolutionized many research fields and there is a recent surge of interest in applyi…

Learning from multivariate discrete sequential data using a restricted Boltzmann machine model

A restricted Boltzmann machine (RBM) is a generative neural-network model with many novel applications such as collaborative filtering and acoustic modeling. An RBM lacks the capacity to retain memory, making it inapprop…

A Hybrid Recommendation Method Based on Feature for Offline Book Personalization

Recommendation system has been widely used in different areas. Collaborative filtering focuses on rating, ignoring the features of items itself. In order to effectively evaluate customers preferences on books, taking int…

A multi-level collaborative filtering method that improves recommendations

Collaborative filtering is one of the most used approaches for providing recommendations in various online environments. Even though collaborative recommendation methods have been widely utilized due to their simplicity …