Recommender systems aim at enhancing user experience on the Web by employing the results of users behavior analysis for recommending items. However, user behavior is usually influenced by various aspects. Even though visual stimuli greatly influence ...

We present a research tool for user preference elicitation that collects both explicit user feedback and unobtrusively acquired facial expressions. The concrete implementation is a web-based user interface where the user is presented with two music ...

With the increase of data collected and computation power available, modern recommender systems are ever facing new challenges. While complex models are developed in academia, industry practice seems to focus on relatively simple techniques that can ...

Deep Learning is one of the next big things in Recommendation Systems technology. The past few years have seen the tremendous success of deep neural networks in a number of complex machine learning tasks such as computer vision, natural language ...

Farfetch is a global fashion marketplace with a catalog that, at any time, has over 200 000 products spanning over 2000 brands from luxury boutiques all around the world. Finding the right product to the right customer is a challenge that, we, as Data ...

Time plays a key role in recommendation. Handling it properly is especially critical when using recommender systems in real-world applications, which may not be as clear when doing research with historical data. In this talk, we will discuss some of the ...

We propose an encoder-decoder neural architecture to model user session and intent using browsing and purchasing data from a large e-commerce company.

We begin by identifying the source-target transition pairs between items within each session. Then, the ...

Accurate model-based Collaborative Filtering (CF) approaches, such as Matrix Factorization (MF), tend to be black-box machine learning models that lack interpretability and do not provide a straightforward explanation for their outputs. Yet explanations ...

Many recommendation algorithms suffer from popularity bias in their output: popular items are recommended frequently and less popular ones rarely, if at all. However, less popular, long-tail items are precisely those that are often desirable ...

In this paper we describe a deployed recommender system to predict insurance products for new and existing customers. Our goal is to give our customers personalized recommendations based on what other similar people with similar portfolios have, in ...

The recommendation challenge can be posed as the problem of predicting either item ratings or item rankings. The latter approach has proven more effective. Pairwise learning-to-rank techniques have been relatively successful. Hence, they are popularly ...

Many users of e-commerce portals commonly use customer reviews for making purchase decisions. But a product may have tens or hundreds of diverse reviews leading to information overload on the customer. The main objective of our work is to develop a ...

In many real-life recommendation settings, user profiles and past activities are not available. The recommender system should make predictions based on session data, e.g. session clicks and descriptions of clicked items. Conventional recommendation ...

This work addresses the problem of selecting Tensor Factorization algorithms for the Context-aware Filtering recommendation task using a metalearning approach. The most important challenge of applying metalearning on new problems is the development of ...

Developing a real-world recommender system, i.e. for use in large-scale online retail, poses a number of different challenges. Interestingly, only a small part of these challenges are of algorithmic nature, such as how to select the most accurate model ...

Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. While ...

Factorization Machines (FMs) are a model class capable of learning pairwise (and in general higher order) feature interactions from high dimensional, sparse data. In this paper we adopt an elementary view on FMs. Specifically, we view FMs as a sum of ...

Recently, many e-commerce websites have encouraged their users to rate shopping items and write review texts. This review information has been very useful for understanding user preferences and item properties, as well as enhancing the capability to ...

Knowledge Graphs have proven to be extremely valuable to recommender systems, as they enable hybrid graph-based recommendation models encompassing both collaborative and content information. Leveraging this wealth of heterogeneous information for top-N ...

Recently, deep learning methods have been shown to improve the performance of recommender systems over traditional methods, especially when review text is available. For example, a recent model, DeepCoNN, uses neural nets to learn one latent ...