Deriving item features relevance from collaborative domain knowledge

An Item based recommender system works by computing a similarity between items, which can exploit past user interactions (collaborative filtering) or item features (content based filtering). Collaborative algorithms have been proven to achieve better recommendation quality then content based algorithms in a variety of scenarios, being more effective in modeling user behaviour. However, they can not be applied when items have no interactions at all, i.e. cold start items. Content based algorithms, which are applicable to cold start items, often require a lot of feature engineering in order to generate useful recommendations. This issue is specifically relevant as the content descriptors become large and heterogeneous. The focus of this paper is on how to use a collaborative models domain-specific knowledge to build a wrapper feature weighting method which embeds collaborative knowledge in a content based algorithm. We present a comparative study for different state of the art algorithms and present a more general model. This machine learning approach to feature weighting shows promising results and high flexibility

Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

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 in many session-based recommendation domains user identifiers are hard to come by, there are also domains in which user profiles are readily available. We propose a seamless way to personalize RNN models with cross-session information transfer and devise a Hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions. Results on two industry datasets show large improvements over the session-only RNNs

Modeling response times in the Google ROADEF/EURO Challenge

In this paper, we extend the machine reassignment model proposed by Google for the ROADEF/EURO Challenge. The aim of the challenge is to develop algorithms for the efficient solutions of data-center consolidation problems. The problem stated in the challenge mainly focus on dependability requirements and does not take into account performance requirements (end-to-end response times). We extend the Google problem definition by modeling and constraining end-to-end response times. We provide experimental results to show the effectiveness of this extension. Copyright is held by author/owner(s)

Cross-domain recommendations without overlapping data: Myth or reality?

Cross-domain recommender systems adopt different tech- niques to transfer learning from source domain to target domain in order to alleviate the sparsity problem and im- prove accuracy of recommendations. Traditional techniques require the two domains to be linked by shared character- istics associated to either users or items. In collaborative filtering (CF) this happens when the two domains have over- lapping users or item (at least partially). Recently, Li et al. [7] introduced codebook transfer (CBT), a cross-domain CF technique based on co-clustering, and presented experimen- tal results showing that CBT is able to transfer knowledge between non-overlapping domains. In this paper, we dis- prove these results and show that CBT does not transfer knowledge when source and target domains do not overlap

Eigenvalue analogy for confidence estimation in item-based recommender systems

Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current literature does not provide any theoretical explanation of the conditions under which item-based recommendations will succeed or fail. We investigate the existence of an ideal item-based CF method able to make perfect recommendations. This CF model is formalized as an eigenvalue problem, where estimated ratings are equivalent to the true (unknown) ratings multiplied by a user-specific eigenvalue of the similarity matrix. Preliminary experiments show that the magnitude of the eigenvalue is proportional to the accuracy of recommendations for that user and therefore it can provide reliable measure of confidence

User interface patterns in recommendation-empowered content intensive multimedia applications

Design Patterns (DPs) are acknowledged as powerful conceptual tools to improve design quality and to reduce time and cost of the development process by effect of the reuse of “good” design solutions. In many fields (e.g., software engineering, web engineering, interface design) patterns are widely used by practitioners and are also investigated from a research perspective. Still, they have been seldom explored in the arena of Recommender Systems (RSs). RSs provide suggestions (“recommendations”) for items that are likely to be appropriate for the user profile, and are increasingly adopted in content-intensive multimedia applications to complement traditional forms of search in large information spaces. This paper explores RSs through the lens of User Interface (UI) Design Patterns. We have performed a systematic analysis of 54 recommendation-empowered content-intensive multimedia applications, in order to: (i) discover the occurrences of existing domain independent UI patterns; (ii) identify frequently adopted UI solutions that are not modelled by existing patterns, and define a set of new UI patterns, some of which are specific of the interfaces for recommendation features while others can be useful also in a broader context. The results of our inspection have been discussed with and evaluated by a team of experts, leading to a consolidated set of 14 new patterns that are reported in the paper. Reusing pattern-based design solutions instead of building new solutions from scratch enables novice and expert designers to build good UIs for Recommendation-empowered content intensive multimedia applications more effectively, and ultimately can improve the UX experience in this class of systems. From a broader perspective, our work can stimulate future research bridging Recommender Systems, Web Engineering and Interface Design by means of Design Patterns, and highlights new research directions also discussed in the paper

Benchmarking: A methodology for ensuring the relative quality of recommendation systems in software engineering

This chapter describes the concepts involved in the process of benchmarking of recommendation systems. Benchmarking of recommendation systems is used to ensure the quality of a research system or production system in comparison to other systems, whether algorithmically, infrastructurally, or according to any sought-after quality. Specifically, the chapter presents evaluation of recommendation systems according to recommendation accuracy, technical constraints, and business values in the context of a multi-dimensional benchmarking and evaluation model encompassing any number of qualities into a final comparable metric. The focus is put on quality measures related to recommendation accuracy, technical factors, and business values. The chapter first introduces concepts related to evaluation and benchmarking of recommendation systems, continues with an overview of the current state of the art, then presents the multi-dimensional approach in detail. The chapter concludes with a brief discussion of the introduced concepts and a summary