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

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

Alcune riflessioni sulla pratica regolatoria, con riferimento ad alcuni settori dell’industria dei trasporti

La regolazione dei mercati rappresenta un elemento fondamentale per il raggiungimento di un’organizzazione più efficiente del sistema dei trasporti insieme ad una distribuzione equa dei vantaggi derivanti dallo stesso. In tale ottica la tutela del consumatore, le norme legate alla qualità e alla sicurezza dei beni e servizi scambiati su un mercato e una consona valutazione delle esternalità sono solo alcuni degli elementi di criticità di cui il regolatore dovrebbe tenere conto. In molti paesi, a partire dagli anni ’80, il settore dei trasporti è stato coinvolto dal processo di privatizzazioni e liberalizzazioni che ha interessato molte public utilities e che ha portato, in particolare, alla creazione di mercati regolati in cui le infrastrutture sono tipicamente gestite in concessione, mentre i servizi, laddove non sia possibile l’introduzione di forme di concorrenza, vengono invece regolamentati e le tariffe determinate secondo regole pre-definite. Nel corso del tempo alcune forme di regolazione tariffaria sembrano essersi imposte senza che la loro adozione nel caso di un particolare settore sia stata adeguatamente valutata alla luce delle caratteristiche economico-tecnologiche del settore stesso. Lo studio qui proposto si prefigge di effettuare un confronto tra i due principali metodi di regolamentazione tariffaria (price cap e regolazione del tasso di rendimento, RoR) tipicamente applicati nei settori altamente regolati, in cui la concorrenza è limitata per motivi tecnici o legati alla sicurezza. L’articolo è organizzato come segue. Il primo paragrafo è dedicato ad una breve discussione della letteratura, mentre nel secondo paragrafo vengono analizzate criticamente le principali caratteristiche dei metodi di regolazione tariffaria ispirati al price cap e al RoR. Quindi, nel terzo paragrafo viene analizzato, alla luce della discussione teorica sviluppata precedentemente, il caso di due diversi settori dei trasporti (autostrade e ormeggio) regolati in Italia con metodi di tipo price cap e RoR. Infine,il quarto paragrafo è dedicato alle conclusioni e alla discussione di possibili sviluppi futuri della ricerca

Replication of collaborative filtering generative adversarial networks on recommender systems

CFGAN and its family of models (TagRec, MTPR, and CRGAN) learn to generate personalized and fake-but-realistic preferences for top-N recommendations by solely using previous interactions. The work discusses the impact of certain differences between the CFGAN framework and the model used in the original evaluation. The absence of random noise and the use of real user profiles as condition vectors leaves the generator prone to learn a degenerate solution in which the output vector is identical to the input vector, therefore, behaving essentially as a simple auto-encoder. This work further expands the experimental analysis comparing CFGAN against a selection of simple and well-known properly optimized baselines, observing that CFGAN is not consistently competitive against them despite its high computational cost

A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research

The design of algorithms that generate personalized ranked item lists is a central topic of research in the field of recommender systems. In the past few years, in particular, approaches based on deep learning (neural) techniques have become dominant in the literature. For all of them, substantial progress over the state-of-the-art is claimed. However, indications exist of certain problems in today's research practice, e.g., with respect to the choice and optimization of the baselines used for comparison, raising questions about the published claims. In order to obtain a better understanding of the actual progress, we have tried to reproduce recent results in the area of neural recommendation approaches based on collaborative filtering. The worrying outcome of the analysis of these recent works-all were published at prestigious scientific conferences between 2015 and 2018-is that 11 out of the 12 reproducible neural approaches can be outperformed by conceptually simple methods, e.g., based on the nearest-neighbor heuristics. None of the computationally complex neural methods was actually consistently better than already existing learning-based techniques, e.g., using matrix factorization or linear models. In our analysis, we discuss common issues in today's research practice, which, despite the many papers that are published on the topic, have apparently led the field to a certain level of stagnation.Comment: Source code and full results available at: https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluatio

Replication of recommender systems with impressions

Impressions are a novel data type in Recommender Systems containing the previously-exposed items, i.e., what was shown on-screen. Due to their novelty, the current literature lacks a characterization of impressions, and replications of previous experiments. Also, previous research works have mainly used impressions in industrial contexts or recommender systems competitions, such as the ACM RecSys Challenges. This work is part of an ongoing study about impressions in recommender systems. It presents an evaluation of impressions recommenders on current open datasets, comparing not only the recommendation quality of impressions recommenders against strong baselines, but also determining if previous progress claims can be replicated

Channel Characterization of Diffusion-based Molecular Communication with Multiple Fully-absorbing Receivers

In this paper an analytical model is introduced to describe the impulse response of the diffusive channel between a pointwise transmitter and a given fully-absorbing (FA) receiver in a molecular communication (MC) system. The presence of neighbouring FA nanomachines in the environment is taken into account by describing them as sources of negative molecules. The channel impulse responses of all the receivers are linked in a system of integral equations. The solution of the system with two receivers is obtained analytically. For a higher number of receivers the system of integral equations is solved numerically. It is also shown that the channel impulse response shape is distorted by the presence of the neighbouring FA interferers. For instance, there is a time shift of the peak in the number of absorbed molecules compared to the case without interference, as predicted by the proposed model. The analytical derivations are validated by means of particle based simulations

techno economic design of wind farms a methodology and multi scenario application

n/