Papers Collaborative Filtering

Computable News Ecosystems: Roles for Humans and Machines

Integrating Topic Models and Latent Factors for Recommendation

Nowadays, we have large amounts of online items in various web-based applications, which makes it an important task to build effective personalized recommender systems so as to save users' efforts in information seeking.…

Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering

Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new item. The question is how to build a seed s…

Big Data analytics. Three use cases with R, Python and Spark

Management and analysis of big data are systematically associated with a data distributed architecture in the Hadoop and now Spark frameworks. This article offers an introduction for statisticians to these technologies b…

Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences

Performing arts organizations aim to enrich their communities through the arts. To do this, they strive to match their performance offerings to the taste of those communities. Success relies on understanding audience pre…

Top-N Recommendation on Graphs

Recommender systems play an increasingly important role in online applications to help users find what they need or prefer. Collaborative filtering algorithms that generate predictions by analyzing the user-item rating m…

Enhancing LambdaMART Using Oblivious Trees

Learning to rank is a machine learning technique broadly used in many areas such as document retrieval, collaborative filtering or question answering. We present experimental results which suggest that the performance of…

Neural Autoregressive Collaborative Filtering for Implicit Feedback

This paper proposes implicit CF-NADE, a neural autoregressive model for collaborative filtering tasks using implicit feedback ( e.g. click, watch, browse behaviors). We first convert a users implicit feedback into a like…

Ask the GRU: Multi-Task Learning for Deep Text Recommendations

In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation app…

Data Poisoning Attacks on Factorization-Based Collaborative Filtering

Recommendation and collaborative filtering systems are important in modern information and e-commerce applications. As these systems are becoming increasingly popular in the industry, their outputs could affect business …

Collaborative Filtering with Recurrent Neural Networks

We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long shor…

Exploring Trust-Aware Neighbourhood in Trust-based Recommendation

Traditional Recommender Systems (RS) do not consider any personal user information beyond rating history. Such information, on the other hand, is widely available on social networking sites (Facebook, Twitter). As a resu…

Dynamic Collaborative Filtering with Compound Poisson Factorization

Model-based collaborative filtering analyzes user-item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most collaborative filtering a…

An Integrated Recommender Algorithm for Rating Prediction

Recommender system is currently widely used in many e-commerce systems, such as Amazon, eBay, and so on. It aims to help users to find items which they may be interested in. In literature, neighborhood-based collaborativ…

News Citation Recommendation with Implicit and Explicit Semantics

Which Tumblr Post Should I Read Next?

User Embedding for Scholarly Microblog Recommendation

Beyond Movie Recommendations: Solving the Continuous Cold Start Problem in E-commerceRecommendations

Many e-commerce websites use recommender systems or personalized rankers to personalize search results based on their previous interactions. However, a large fraction of users has no prior inter-actions, making it imposs…

Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks

Conventional collaborative filtering techniques treat a top-n recommendations problem as a task of generating a list of the most relevant items. This formulation, however, disregards an opposite - avoiding recommendation…

Incomplete Pivoted QR-based Dimensionality Reduction

High-dimensional big data appears in many research fields such as image recognition, biology and collaborative filtering. Often, the exploration of such data by classic algorithms is encountered with difficulties due to …