This paper summarizes RecProfile '16, the first workshop on profiling user preferences for dynamic, online, and real-time recommendations, held in conjunction with RecSys '16, the 10th ACM conference on recommender systems. We describe the main themes ...

We believe that 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 tasks such as computer vision, natural language ...

The 4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE) is taking place in Boston on September 16th, 2016 in conjunction with the ACM RecSys 2016 conference. The workshop focuses on the acquisition and usage of emotions and ...

For many households the television is the central entertainment hub in their home, and the average TV viewer spends about half of their leisure time in front of a TV. At any given moment, a costumer has hundreds to thousands of entertainment choices ...

This turorial offers a rich blend of theory and practice regarding dimensionality reduction methods, to address the information overload problem in recommender systems. This problem affects our everyday experience while searching for knowledge on a ...

We bring to the fore of the recommender system research community, an inconvenient truth about the current state of understanding how recommender system algorithms and humans influence one another, both computationally and cognitively. Unlike the great ...

Matrix factorization (MF) models and their extensions are standard in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose a co-factorization model, ...

Most evaluations of novel algorithmic contributions assess their accuracy in predicting what was withheld in an offline evaluation scenario. However, several doubts have been raised that standard offline evaluation practices are not appropriate to ...

Implicit feedback is a key source of information for many recommendation and personalization approaches. However, using it typically requires multiple episodes of interaction and roundtrips to a recommendation engine. This adds latency and neglects the ...

In this paper we present ExpLOD, a framework which exploits the information available in the Linked Open Data (LOD) cloud to generate a natural language explanation of the suggestions produced by a recommendation algorithm. The methodology is based on ...

Recommending items that have rarely/never been viewed by users is a bottleneck for collaborative filtering (CF) based recommendation algorithms. To alleviate this problem, item content representation (mostly in textual form) has been used as auxiliary ...

Real-life recommender systems often face the daunting task of providing recommendations based only on the clicks of a user session. Methods that rely on user profiles -- such as matrix factorization -- perform very poorly in this setting, thus item-to-...

Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information ...

In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen ...

We propose Meta-Prod2vec, a novel method to compute item similarities for recommendation that leverages existing item metadata. Such scenarios are frequently encountered in applications such as content recommendation, ad targeting and web search. Our ...

We consider the problem of generating diverse, personalized recommendations such that a small set of recommended items covers a broad range of the user's interests. We represent items in a similarity graph, and we formulate the relevance/diversity trade-...

We believe that in the future, the most common form of recommender systems will be present in a personal assistant. We claim that such an intelligent agent must be personal, i.e., know its user's preferences and recommend relevant content, a dynamic ...

Click-through rate (CTR) prediction plays an important role in computational advertising. Models based on degree-2 polynomial mappings and factorization machines (FMs) are widely used for this task. Recently, a variant of FMs, field-aware factorization ...

Automated course recommendation can help deliver personalized and effective college advising and degree planning. Nearest neighbor and matrix factorization based collaborative filtering approaches have been applied to student-course grade data to help ...

Improving the performance of recommender systems using knowledge graphs is an important task. There have been many hybrid systems proposed in the past that use a mix of content-based and collaborative filtering techniques to boost the performance. More ...