A multidimensional collaborative filtering fusion approach with dimensionality reduction

Multidimensional data are getting increasing attention from researchers for creating better recommender systems in recent years. Additional metadata provides algorithms with more details for better understanding the interaction between users and items. While neighbourhood-based Collaborative Filtering (CF) approaches and latent factor models tackle this task in various ways effectively, they only utilize different partial structures of data. In this paper, we seek to delve into different types of relations in data and to understand the interaction between users and items more holistically. We propose a generic multidimensional CF fusion approach for top-N item recommendations. The proposed approach is capable of incorporating not only localized relations of user-user and item-item but also latent interaction between all dimensions of the data. Experimental results show significant improvements by the proposed approach in terms of recommendation accuracy.

A collaborative filtering similarity measure based on singularities.

Recommender systems play an important role in reducing the negative impact of informa- tion overload on those websites where users have the possibility of voting for their prefer- ences on items. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to cal- culate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corroborate the excellent behaviour of the singularity measure proposed.

A collaborative filtering approach to mitigate the new user cold start problem.

The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommenda- tions received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system?s collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neu- ral learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave- one-out cross validation.

A Balanced Memory-Based Collaborative Filtering Similarity Measure.

Collaborative filtering recommender systems contribute to alleviating the problem of information overload that exists on the Internet as a result of the mass use of Web 2.0 applications. The use of an adequate similarity measure becomes a determining factor in the quality of the prediction and recommendation results of the recommender system, as well as in its performance. In this paper, we present a memory-based collaborative filtering similarity measure that provides extremely high-quality and balanced results; these results are complemented with a low processing time (high performance), similar to the one required to execute traditional similarity metrics. The experiments have been carried out on the MovieLens and Netflix databases, using a representative set of information retrieval quality measures.

An Approach to Collaborative Filtering by ARTMAP Neural Networks

Recommender systems are now widely used in e-commerce applications to assist customers to find relevant products from the many that are frequently available. Collaborative filtering (CF) is a key component of many of these systems, in which recommendations are made to users based on the opinions of similar users in a system. This paper presents a model-based approach to CF by using supervised ARTMAP neural networks (NN). This approach deploys formation of reference vectors, which makes a CF recommendation system able to classify user profile patterns into classes of similar profiles. Empirical results reported show that the proposed approach performs better than similar CF systems based on unsupervised ART2 NN or neighbourhood-based algorithm.

Collaborative Filtering with Semantic Neighbour Discovery

Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items – the subset of items co-rated by both users – typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process – a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.

Studio ed integrazione di un sistema di collaborative filtering su piattaforma e-commerce business to consumer

I sistemi di raccomandazione sono una tipologia di sistemi di filtraggio delle informazioni che cercano di prevedere la valutazione o la preferenza che l'utente potrebbe dare ad un elemento. Sono diventati molto comuni in questi ultimi anni e sono utilizzati da una vasta gamma di applicazioni, le più popolari riguardano film, musica, notizie, libri, articoli di ricerca e tag di social networking. Tuttavia, ci sono anche sistemi di raccomandazione per i ristoranti, servizi finanziari, assicurazioni sulla vita e persone (siti di appuntamenti online, seguaci di Twitter). Questi sistemi, tuttora oggetto di studi, sono già applicati in un'ampia gamma di settori, come ad esempio le piattaforme di scoperta dei contenuti, utilizzate on-line per aiutare gli utenti nella ricerca di trasmissioni televisive; oppure i sistemi di supporto alle decisioni che utilizzano sistemi di raccomandazione avanzati, basati sull'apprendimento delle conoscenze, per aiutare i fruitori del servizio nella soluzioni di problemi complessi. Inoltre, i sistemi di raccomandazione sono una valida alternativa agli algoritmi di ricerca in quanto aiutano gli utenti a scoprire elementi che potrebbero non aver trovato da soli. Infatti, sono spesso implementati utilizzando motori di ricerca che indicizzano dati non tradizionali.

Collaborative learning from distributed places and spaces: Exploring the use of virtual collaborative tools for local and global student teamwork projects

Educating responsive graduates. Graduate competencies include reliability, communication skills and ability to work in teams. Students using Collaborative technologies adapt to a new working environment, working in teams and using collaborative technologies for learning. Collaborative Technologies were used not simply for delivery of learning but innovatively to supplement and enrich research-based learning, providing a space for active engagement and interaction with resources and team. This promotes the development of responsive ‘intellectual producers’, able to effectively communicate, collaborate and negotiate in complex work environments. Exploiting technologies. Students use ‘new’ technologies to work collaboratively, allowing them to experience the reality of distributed workplaces incorporating both flexibility and ‘real’ time responsiveness. Students are responsible and accountable for individual and group work contributions in a highly transparent and readily accessible workspace. This experience provides a model of an effective learning tool. Navigating uncertainty and complexity. Collaborative technologies allows students to develop critical thinking and reflective skills as they develop a group product. In this forum students build resilience by taking ownership and managing group work, and navigating the uncertainties and complexities of group dynamics as they constructively and professionally engage in team dialogue and learn to focus on the goal of the team task.

Hyers-Ulam-Rassias Stability of Functional Differential Systems with Point and Distributed Delays

This paper investigates stability and asymptotic properties of the error with respect to its nominal version of a nonlinear time-varying perturbed functional differential system subject to point, finite-distributed, and Volterra-type distributed delays associated with linear dynamics together with a class of nonlinear delayed dynamics. The boundedness of the error and its asymptotic convergence to zero are investigated with the results being obtained based on the Hyers-Ulam-Rassias analysis.

incremental learning of triadic plsa for collaborative filtering

Beijing University of Technology (BJUT); Beijing Municipal Lab of Brain Informatics; Chinese Society of Radiology; National Natural Science Foundation of China (NSFC); State Administration of Foreign Experts Affairs

Mures : Un système de recommandation de musique

Pendant la dernière décennie nous avons vu une transformation incroyable du monde de la musique qui est passé des cassettes et disques compacts à la musique numérique en ligne. Avec l'explosion de la musique numérique, nous avons besoin de systèmes de recommandation de musique pour choisir les chansons susceptibles d’être appréciés à partir de ces énormes bases de données en ligne ou personnelles. Actuellement, la plupart des systèmes de recommandation de musique utilisent l’algorithme de filtrage collaboratif ou celui du filtrage à base de contenu. Dans ce mémoire, nous proposons un algorithme hybride et original qui combine le filtrage collaboratif avec le filtrage basé sur étiquetage, amélioré par la technique de filtrage basée sur le contexte d’utilisation afin de produire de meilleures recommandations. Notre approche suppose que les préférences de l'utilisateur changent selon le contexte d'utilisation. Par exemple, un utilisateur écoute un genre de musique en conduisant vers son travail, un autre type en voyageant avec la famille en vacances, un autre pendant une soirée romantique ou aux fêtes. De plus, si la sélection a été générée pour plus d'un utilisateur (voyage en famille, fête) le système proposera des chansons en fonction des préférences de tous ces utilisateurs. L'objectif principal de notre système est de recommander à l'utilisateur de la musique à partir de sa collection personnelle ou à partir de la collection du système, les nouveautés et les prochains concerts. Un autre objectif de notre système sera de collecter des données provenant de sources extérieures, en s'appuyant sur des techniques de crawling et sur les flux RSS pour offrir des informations reliées à la musique tels que: les nouveautés, les prochains concerts, les paroles et les artistes similaires. Nous essayerons d’unifier des ensembles de données disponibles gratuitement sur le Web tels que les habitudes d’écoute de Last.fm, la base de données de la musique de MusicBrainz et les étiquettes des MusicStrands afin d'obtenir des identificateurs uniques pour les chansons, les albums et les artistes.