As information continues to grow at a very fast pace, our ability to access this information effectively does not, and we are often realize how harder is getting to locate an object quickly and easily. The so-called personalization technology is one of the best solutions to this information overload problem: by automatically learning the user profile, personalized information services have the potential to offer users a more proactive and intelligent form of information access that is designed to assist us in finding interesting objects. Recommender systems, which have emerged as a solution to minimize the problem of information overload, provide us with recommendations of content suited to our needs. In order to provide recommendations as close as possible to a user&rsquo / s taste, personalized recommender systems require accurate user models of characteristics, preferences and needs. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users, But it suffers from several issues like data sparsity and cold start. In one-class collaborative filtering, a special type of collaborative filtering methods that aims to deal with datasets that lack counter-examples, the challenge is even greater, since these datasets are even sparser. In this thesis, we present a series of experiments conducted on a real-life customer purchase database from a major Turkish E-Commerce site. The sparsity problem is handled by the use of content-based technique combined with TFIDF weights, memory based collaborative filtering combined with different similarity measures and also hybrids approaches, and also model based collaborative filtering with the use of Singular Value Decomposition (SVD). Our study showed that the binary similarity measure and SVD outperform conventional measures in this OCCF dataset.

Recently, it has become more and more difficult for the existing web based systems to locate or retrieve any kind of relevant information, due to the rapid growth of the World Wide Web (WWW) in terms of the information space and the amount of the users in that space. However, in today&#039 / s world, many systems and approaches make it possible for the users to be guided by the recommendations that they provide about new items such as articles, news, books, music, and movies. However, a lot of traditional recommender systems result in failure when the data to be used throughout the recommendation process is sparse. In another sense, when there exists an inadequate number of items or users in the system, unsuccessful recommendations are produced. Within this thesis work, ReMovender, a web based movie recommendation system, which uses a content boosted collaborative filtering approach, will be presented. ReMovender combines the local/global similarity and missing data prediction v techniques in order to handle the previously mentioned sparseness problem effectively. Besides, by putting the content information of the movies into consideration during the item similarity calculations, the goal of making more successful and realistic predictions is achieved.

The evolution of the Internet has brought us into a world that represents a huge amount of information items such as music, movies, books, web pages, etc. with varying quality. As a result of this huge universe of items, people get confused and the question &ldquo / Which one should I choose?&rdquo / arises in their minds. Recommendation Systems address the problem of getting confused about items to choose, and filter a specific type of information with a specific information filtering technique that attempts to present information items that are likely of interest to the user. A variety of information filtering techniques have been proposed for performing recommendations, including content-based and collaborative techniques which are the most commonly used approaches in recommendation systems. This thesis work introduces ReMovender, a content-based movie recommendation system which is empowered by collaborative missing data prediction. The distinctive point of this study lies in the methodology used to correlate the users in the system with one another and the usage of the content information of movies. ReMovender makes it possible for the users to rate movies in a scale from one to five. By using these ratings, it finds similarities among the users in a collaborative manner to predict the missing ratings data. As for the content-based part, a set of movie features are used in order to correlate the movies and produce recommendations for the users.

Situation awareness and collective intelligence are two technologies used in smart systems. The former renders those systems able to reason upon their abstract knowledge of what is going on. The latter enables them learning and deriving new information from a composition of experiences of their users. In this dissertation we present a doctoral research on an attempt to combine the two in order to obtain, in a collaborative fashion, situation-based rules that the whole community of entities would benefit of sharing. We introduce the KRAMER recommendation system, which we designed and implemented as a solution to the problem of not having decision support tools both situation-aware and collaborative. The system is independent from any domain of application in particular, in other words generic, and we apply its prototype implementation to context-enriched social communication scenario.

The popularity of e-commerce sites and applications that use recommendations and user modeling is increased recently. The development and contest in tourism calls attention of large-scale IT companies. These companies have started to work on recommendation systems and user modeling on tourism sector. Some of the clustering methodologies, neighboring methods and machine learning algorithms are commenced to use for making predictions about tourist&rsquo / s interests while he/she is traveling around the city. Recommendation ability is the most interesting thing for a tourist guide application. Recommender systems are composed of two main approaches, collaborative and content-based filtering. Collaborative filtering algorithms look for people that have similar interests and properties, while contentbased filtering methods pay attention to sole user&rsquo / s interests and properties to make recommendations. Both of the approaches have advantages and disadvantages, for that reason sometimes these two approaches are used together. Chosen method directly affects the recommendation quality, so advantages and disadvantages of both methods will be examined carefully. Recommendation of locations or services can be seen as a classification problem. Artificial intelligent systems like neural networks, genetic algorithms, particle swarm optimization algorithms, artificial immune systems are inspired from natural life and can be used as classifier systems. Artificial immune system, inspired from human immune system, has ability to classify huge numbers of different patterns. In this paper ESGuide, a tourist guide application that uses artificial immune system is examined. ESGuide application is a client-server application that helps tourists while they are traveling around the city. ESGuide has two components: Map agent and recommender agent. Map agent helps the tourist while he/she interacts with the city map. Tourist should rate the locations and items while traveling. Due to these ratings and client-server interaction, recommender agent tries to predict user interested places and items. Tourist has a chance to state if he/she likes the recommendation or not. If the tourist does not like the recommendation, new recommendation set is created and presented to the user.

Avui en dia, la música està esbiaixada cap al consum d'alguns artistes molt populars. Per exemple, el 2007 només l'1% de totes les cançons en format digital va representar el 80% de les vendes. De la mateixa manera, només 1.000 àlbums varen representar el 50% de totes les vendes, i el 80% de tots els àlbums venuts es varen comprar menys de 100 vegades. Es clar que hi ha una necessitat per tal d'ajudar a les persones a filtrar, descobrir, personalitzar i recomanar música, a partir de l'enorme quantitat de contingut musical disponible. Els algorismes de recomanació de música actuals intenten predir amb precisió el que els usuaris demanen escoltar. Tanmateix, molt sovint aquests algoritmes tendeixen a recomanar artistes famosos, o coneguts d'avantmà per l'usuari. Això fa que disminueixi l'eficàcia i utilitat de les recomanacions, ja que aquests algorismes es centren bàsicament en millorar la precisió de les recomanacions. És a dir, tracten de fer prediccions exactes sobre el que un usuari pugui escoltar o comprar, independentment de quant útils siguin les recomanacions generades. En aquesta tesi destaquem la importància que l'usuari valori les recomanacions rebudes. Per aquesta raó modelem la corba de popularitat dels artistes, per tal de poder recomanar música interessant i desconeguda per l'usuari. Les principals contribucions d'aquesta tesi són: (i) un nou enfocament basat en l'anàlisi de xarxes complexes i la popularitat dels productes, aplicada als sistemes de recomanació, (ii) una avaluació centrada en l'usuari, que mesura la importància i la desconeixença de les recomanacions, i (iii) dos prototips que implementen la idees derivades de la tasca teòrica. Els resultats obtinguts tenen una clara implicació per aquells sistemes de recomanació que ajuden a l'usuari a explorar i descobrir continguts que els pugui agradar. / Actualmente, el consumo de música está sesgada hacia algunos artistas muy populares. Por ejemplo, en el año 2007 sólo el 1% de todas las canciones en formato digital representaron el 80% de las ventas. De igual modo, únicamente 1.000 álbumes representaron el 50% de todas las ventas, y el 80% de todos los álbumes vendidos se compraron menos de 100 veces. Existe, pues, una necesidad de ayudar a los usuarios a filtrar, descubrir, personalizar y recomendar música a partir de la enorme cantidad de contenido musical existente. Los algoritmos de recomendación musical existentes intentan predecir con precisión lo que la gente quiere escuchar. Sin embargo, muy a menudo estos algoritmos tienden a recomendar o bien artistas famosos, o bien artistas ya conocidos de antemano por el usuario.Esto disminuye la eficacia y la utilidad de las recomendaciones, ya que estos algoritmos se centran en mejorar la precisión de las recomendaciones. Con lo cuál, tratan de predecir lo que un usuario pudiera escuchar o comprar, independientemente de lo útiles que sean las recomendaciones generadas. En este sentido, la tesis destaca la importancia de que el usuario valore las recomendaciones propuestas. Para ello, modelamos la curva de popularidad de los artistas con el fin de recomendar música interesante y, a la vez, desconocida para el usuario.Las principales contribuciones de esta tesis son: (i) un nuevo enfoque basado en el análisis de redes complejas y la popularidad de los productos, aplicada a los sistemas de recomendación,(ii) una evaluación centrada en el usuario que mide la calidad y la novedad de las recomendaciones, y (iii) dos prototipos que implementan las ideas derivadas de la labor teórica. Los resultados obtenidos tienen importantes implicaciones para los sistemas de recomendación que ayudan al usuario a explorar y descubrir contenidos que le puedan gustar. / Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail.Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations. In this Thesis we stress the importance of the user's perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution. The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user's relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.

Σκοπός της παρούσας διπλωματικής διατριβής είναι η μελέτη και ανάπτυξη ενός νέου αλγοριθμικού πλαισίου Συνεργατικής Διήθησης(CF) για την παραγωγή συστάσεων. Η μέθοδος που προτείνουμε, βασίζεται στην εκμετάλλευση της ιεραρχικής διάρθρωσης του χώρου αντικειμένων και πατά διαισθητικά στην ιδιότητα της ``Σχεδόν Πλήρης Αναλυσιμότητας'' (NCD) η οποία είναι συνυφασμένη με τη δομή της πλειοψηφίας των ιεραρχικών συστημάτων. Η Συνεργατική Διήθηση αποτελεί ίσως την πιο πετυχημένη οικογένεια τεχνικών για την παραγωγή συστάσεων. Η μεγάλη απήχησή της στο διαδίκτυο αλλά και η ευρεία εφαρμογή της σε σημαντικά εμπορικά περιβάλλοντα, έχουν οδηγήσει στη σημαντική ανάπτυξη της θεωρίας την τελευταία δεκαετία, όπου μια ευρεία ποικιλία αλγορίθμων και μεθόδων έχουν προταθεί. Ωστόσο, παρά την πρωτοφανή τους επιτυχία οι CF μέθοδοι παρουσιάζουν κάποιους σημαντικούς περιορισμούς συμπεριλαμβανομένης της επεκτασιμότητας και της αραιότητας των δεδομένων. Τα προβλήματα αυτά επιδρούν αρνητικά στην ποιότητα των παραγόμενων συστάσεων και διακυβεύουν την εφαρμοσιμότητα πολλών CF αλγορίθμων σε ρεαλιστικά σενάρια. Χτίζοντας πάνω στη διαίσθηση πίσω από τον αλγόριθμο NCDawareRank - μίας γενικής μεθόδου υπολογισμού διανυσμάτων κατάταξης ιεραρχικά δομημένων γράφων - και της σχετικής με αυτόν έννοιας της NCD εγγύτητας, προβαίνουμε σε μία μοντελοποίηση του συστήματος με τρόπο που φωτίζει τα ενδημικά του χαρακτηριστικά και προτείνουμε έναν νέο αλγοριθμικό πλαίσιο συστάσεων, τον Αλγόριθμο 1. Στο επίκεντρο της προσέγγισής μας είναι η προσπάθεια να συνδυάσουμε τις άμεσες με τις NCD, ``γειτονιές'' των αντικειμένων ώστε να πετύχουμε μεγαλύτερης ακρίβειας χαρακτηρισμό των πραγματικών συσχετισμών μεταξύ των στοιχείων του χώρου αντικειμένων, με σκοπό την βελτίωση της ποιότητας των συστάσεων αλλά και την αντιμετώπιση της εγγενούς αραιότητας και των προβλημάτων που αυτή συνεπάγεται. Για να αξιολογήσουμε την απόδοση της μεθόδου μας υλοποιούμε και εφαρμόζουμε τον Αλγόριθμο 1 στο κλασικό movie recommendation πρόβλημα και παραθέτουμε μια σειρά από πειράματα χρησιμοποιώντας τo MovieLens Dataset. Τα πειράματά μας δείχνουν πως ο Αλγόριθμος 1 με την εκμετάλλευση της ιδέας της NCD εγγύτητας καταφέρνει να πετύχει λίστες συστάσεων υψηλότερης ποιότητας σε σύγκριση με τις άλλες state-of-the-art μεθόδους που έχουν προταθεί στη βιβλιογραφία, σε ευρέως χρησιμοποιούμενες μετρικές (micro- και macro-DOA), αποδεικνύοντας την ίδια στιγμή πως είναι λιγότερο επιρρεπής στα προβλήματα που σχετίζονται με την αραιότητα και έχοντας παράλληλα ανταγωνιστικό προφίλ πολυπλοκότητας και απαιτήσεις αποθήκευσης. / The purpose of this master's thesis is to study and develop a new algorithmic framework for collaborative filtering (CF) to generate recommendations. The method we propose is based on the exploitation of the hierarchical structure of the item space and intuitively ``stands'' on the property of Near Complete Decomposability (NCD) which is inherent in the structure of the majority of hierarchical systems. Collaborative Filtering is one of the most successful families of recommendations methods. The great impact of CF on Web applications, and its wide deployment in important commercial environments, have led to the significant development of the theory, with a wide variety of algorithms and methods being proposed. However, despite their unprecedented success, CF methods present some important limitations including scalability and data sparsity. These problems have a negative impact of the quality of the recommendations and jeopardize the applicability of many CF algorithms in realistic scenarios. Building on the intuition behind the NCDawareRank algorithm and its related concept of NCD proximity, we model our system in a way that illuminates its endemic characteristics and we propose a new algorithmic framework for recommendations, called Algorithm 1. We focus on combining the direct with the NCD `` neighborhoods'' of items to achieve better characterization of the inter-item relations, in order to improve the quality of recommendations and alleviate sparsity related problems. To evaluate the merits of our method, we implement and apply Algorithm 1 in the classic movie recommendation problem, running a number of experiments on the standard MovieLens dataset. Our experiments show that Algorithm 1 manages to create recommendation lists with higher quality compared with other state-of-the-art methods proposed in the literature, in widely used metrics (micro- and macro- DOA), demonstrating at the same time that it is less prone to low density related problems being at the same time very efficient in both complexity and storage requirements.

Submitted by Haroudo Xavier Filho (haroudo.xavierfo@ufpe.br) on 2016-03-11T15:25:20Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) dissertação_gava_cin.pdf: 1596983 bytes, checksum: 23245c1b65fe3416d3baeeac5e118845 (MD5) / Made available in DSpace on 2016-03-11T15:25:20Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) dissertação_gava_cin.pdf: 1596983 bytes, checksum: 23245c1b65fe3416d3baeeac5e118845 (MD5) Previous issue date: 2015-08-06 / Sistemas de recomendação têm desempenhado um papel importante em diferentes contextos de aplicação (e.g recomendação de produtos, filmes, músicas, livros, dentre outros). Eles automaticamente sugerem a cada usuário itens que podem ser relevantes, evitando que o usuário tenha que analisar uma quantidade gigantesca de itens para realizar sua escolha. Filtragem colaborativa (FC) é a abordagem mais popular para a construção de sistemas de recomendação, embora sofra com problemas relacionados à esparsidade dos dados (e.g., usuários ou itens com poucas avaliações). Neste trabalho, investigamos a combinação de técnicas de FC, representada pela técnica de Fatoração de Matrizes, e técnicas de recomendação baseada em confiança (RBC) em redes sociais para aliviar o problema da esparsidade dos dados. Sistemas de RBC têm se mostrado de fato efetivos para aumentar a qualidade das recomendações, em especial para usuários com poucas avaliações realizadas (e.g., usuários novos). Entretanto, o desempenho relativo entre técnicas de FC e de RBC pode depender da quantidade de informação útil presente nas bases de dados. Na arquitetura proposta nesse trabalho, as predições geradas por técnicas de FC e de RBC são combinadas de forma ponderada através de medidas de esparsidade calculadas para usuários e itens. Para isso, definimos inicialmente um conjunto de medidas de esparsidade que serão calculadas sobre a matriz de avaliações usuários-itens e matriz de confiança usuários-usuários. Através de experimentos realizados utilizando a base de dados Epinions, observamos que a proposta de combinação trouxe uma melhoria nas taxas de erro e na cobertura em comparação com as técnicas isoladamente. / Recommender systems have played an important role in different application contexts (e.g recommendation of products, movies, music, books, among others). They automatically suggest each user items that may be relevant, preventing the user having to analyze a huge amount of items to make your choice. Collaborative filtering (CF) is the most popular approach for building recommendation systems, although suffering with sparsity of the data-related issues (eg, users or items with few evaluations). In this study, we investigated the combination of CF techniques represented by matrix factorization technique, and trust-based recommendation techniques (TBR) on social networks to alleviate the problem of data sparseness. TBR systems have in fact proven to be effective to increase the quality of the recommendations, especially for users with few assessments already carried out (e.g., cold start users). However, the relative performance between CF and TBR techniques may depend on the amount of useful information contained in the databases. In the proposed architecture in this work, the predictions generated by CF and TBR techniques are weighted combined through sparsity measures calculated to users and items. To do this, first we define a set of sparsity measures that will be calculated on the matrix of ratings users-items and matrix of trust users-users. Through experiments using Epinions database, we note that the proposed combination brought an improvement in error rates and coverage compared to combined techniques.

Made available in DSpace on 2014-06-12T15:59:01Z (GMT). No. of bitstreams: 2 arquivo4812_1.pdf: 2843132 bytes, checksum: cf053779fad5d73c77a2b107542256b3 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2003 / Ricardo de Melo Queiroz, Sérgio; de Assis Tenório Carvalho, Francisco. Group recommendation strategies based on collaborative filtering. 2003. Dissertação (Mestrado). Programa de Pós-Graduação em Ciência da Computação, Universidade Federal de Pernambuco, Recife, 2003.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-02-21T16:47:42Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) dvsTeseBiblioteca.pdf: 6571192 bytes, checksum: eb7914e5ffef25b8f01ff92d9a60c164 (MD5) / Made available in DSpace on 2017-02-21T16:47:42Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) dvsTeseBiblioteca.pdf: 6571192 bytes, checksum: eb7914e5ffef25b8f01ff92d9a60c164 (MD5) Previous issue date: 2016-07-21 / FACEPE / Traditionally, single-domain recommender systems (SDRS) have achieved good results in recommending relevant items for users in order to solve the information overload problem. However, cross-domain recommender systems (CDRS) have emerged aiming to enhance SDRS by achieving some goals such as accuracy improvement, diversity, addressing new user and new item problems, among others. Instead of treating each domain independently, CDRS use knowledge acquired in a source domain (e.g. books) to improve the recommendation in a target domain (e.g. movies). Likewise SDRS research, collaborative filtering (CF) is considered the most popular and widely adopted approach in CDRS, because its implementation for any domain is relatively simple. In addition, its quality of recommendation is usually higher than that of content-based filtering (CBF) algorithms. In fact, the majority of the cross-domain collaborative filtering RS (CD-CFRS) can give better recommendations in comparison to single domain collaborative filtering recommender systems (SD-CFRS), leading to a higher users’ satisfaction and addressing cold-start, sparsity, and diversity problems. However, CD-CFRS may not necessarily be more accurate than SD-CFRS. On the other hand, context-aware recommender systems (CARS) deal with another relevant topic of research in the recommender systems area, aiming to improve the quality of recommendations too. Different contextual information (e.g., location, time, mood, etc.) can be leveraged in order to provide recommendations that are more suitable and accurate for a user depending on his/her context. In this way, we believe that the integration of techniques developed in isolation (cross-domain and contextaware) can be useful in a variety of situations, in which recommendations can be improved by information from different sources as well as they can be refined by considering specific contextual information. In this thesis, we define a novel formulation of the recommendation problem, considering both the availability of information from different domains (source and target) and the use of contextual information. Based on this formulation, we propose the integration of cross-domain and context-aware approaches for a novel recommender system (CD-CARS). To evaluate the proposed CD-CARS, we performed experimental evaluations through two real datasets with three different contextual dimensions and three distinct domains. The results of these evaluations have showed that the use of context-aware techniques can be considered as a good approach in order to improve the cross-domain recommendation quality in comparison to traditional CD-CFRS. / Tradicionalmente, “sistemas de recomendação de domínio único” (SDRS) têm alcançado bons resultados na recomendação de itens relevantes para usuários, a fim de resolver o problema da sobrecarga de informação. Entretanto, “sistemas de recomendação de domínio cruzado” (CDRS) têm surgido visando melhorar os SDRS ao atingir alguns objetivos, tais como: “melhoria de precisão”, “melhor diversidade”, abordar os problemas de “novo usuário” e “novo item”, dentre outros. Ao invés de tratar cada domínio independentemente, CDRS usam conhecimento adquirido em um domínio fonte (e.g. livros) a fim de melhorar a recomendação em um domínio alvo (e.g. filmes). Assim como acontece na área de pesquisa sobre SDRS, a filtragem colaborativa (CF) é considerada a técnica mais popular e amplamente utilizada em CDRS, pois sua implementação para qualquer domínio é relativamente simples. Além disso, sua qualidade de recomendação é geralmente maior do que a dos algoritmos baseados em filtragem de conteúdo (CBF). De fato, a maioria dos “sistemas de recomendação de domínio cruzado” baseados em filtragem colaborativa (CD-CFRS) podem oferecer melhores recomendações em comparação a “sistemas de recomendação de domínio único” baseados em filtragem colaborativa (SD-CFRS), aumentando o nível de satisfação dos usuários e abordando problemas tais como: “início frio”, “esparsidade” e “diversidade”. Entretanto, os CD-CFRS podem não ser mais precisos do que os SD-CFRS. Por outro lado, “sistemas de recomendação sensíveis à contexto” (CARS) tratam de outro tópico relevante na área de pesquisa de sistemas de recomendação, também visando melhorar a qualidade das recomendações. Diferentes informações contextuais (e.g. localização, tempo, humor, etc.) podem ser utilizados a fim de prover recomendações que são mais adequadas e precisas para um usuário dependendo de seu contexto. Desta forma, nós acreditamos que a integração de técnicas desenvolvidas separadamente (de “domínio cruzado” e “sensíveis a contexto”) podem ser úteis em uma variedade de situações, nas quais as recomendações podem ser melhoradas a partir de informações obtidas em diferentes fontes além de refinadas considerando informações contextuais específicas. Nesta tese, nós definimos uma nova formulação do problema de recomendação, considerando tanto a disponibilidade de informações de diferentes domínios (fonte e alvo) quanto o uso de informações contextuais. Baseado nessa formulação, nós propomos a integração de abordagens de “domínio cruzado” e “sensíveis a contexto” para um novo sistema de recomendação (CD-CARS). Para avaliar o CD-CARS proposto, nós realizamos avaliações experimentais através de dois “conjuntos de dados” com três diferentes dimensões contextuais e três domínios distintos. Os resultados dessas avaliações mostraram que o uso de técnicas sensíveis a contexto pode ser considerado como uma boa abordagem a fim de melhorar a qualidade de recomendações de “domínio cruzado” em comparação às recomendações de CD-CFRS tradicionais.