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1
Content available Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system
Kużelewska Urszula
International Journal of Electronics and Telecommunications
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2024
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Vol. 70, No. 1
103--114
EN
The successful application of a multi-clusteringbased neighborhood approach to recommender systems has led to increased recommendation accuracy and the elimination of divergence related to differences in clustering methods traditionally used. The Multi-Clustering Collaborative Filtering algorithm was developed to achieve this, as described in the author’s previous papers. However, utilizing multiple clusters poses challenges regarding memory consumption and scalability. Not all partitionings are equally advantageous, making selecting clusters for the recommender system’s input crucial without compromising recommendation accuracy. This article presents a solution for selecting clustering schemes based on internal indices evaluation. This method can be employed for preparing input data in collaborative filtering recommender systems. The study’s results confirm the positive impact of scheme selection on the overall recommendation performance, as it typically improves after the selection process. Furthermore, a smaller number of clustering schemes used as input for the recommender system enhances scalability and reduces memory consumption. The findings are compared with baseline recommenders’ outcomes to validate the effectiveness of the proposed approach.
2
Content available remote Stereotype-Aaware Collaborative Filtering
Frisch Gabriel, Leger Jean-Benoist, Grandvalet Yves
Annals of Computer Science and Information Systems
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2021
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Vol. 25
69--79
EN
In collaborative filtering, recommendations are made using user feedback on a few products. In this paper, we show that even if sensitive attributes are not used to fit the models, a disparate impact may nevertheless affect recommendations. We propose a definition of fairness for the recommender system that expresses that the ranking of items should be independent of sensitive attribute. We design a co-clustering of users and items that processes exogenous sensitive attributes to remove their influence to return fair recommendations. We prove that our model ensures approximately fair recommendations provided that the classification of users approximately respects statistical parity.
3
Content available remote Context Clustering-based Recommender Systems
Kannout Eyad
Annals of Computer Science and Information Systems
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2020
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Vol. 21
85--91
EN
Recommender systems have gained lots of attention due to the rapid increase in the amount of data on the internet. Therefore, the demand for finding more advanced techniques to generate more useful recommendations becomes an urgent. The increasing need for generating more relevant recommendations led to the emergence of many novel recommendation systems, such as Context-aware Recommender System (CARS), which is based on incorporating the contextual information in recommendation systems. The goal of this paper is to propose new recommender systems that utilize the contextual information to find more relevant recommendations.
4
Content available Machine Learning as a method of adapting offers to the clients
Bielecki Jacek, Ceglarski Oskar, Skublewska-Paszkowska Maria
Journal of Computer Sciences Institute
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2019
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Vol. 13
267--271
EN
Recommendation systems are class of information filter applications whose main goal is to provide personalized recommendations. The main goal of the research was to compare two ways of creating personalized recommendations. The recommendation system was built on the basis of a content-based cognitive filtering method and on the basis of a collaborative filtering method based on user ratings. The conclusions of the research show the advantages and disadvantages of both methods.
PL
Systemy rekomendacji to aplikacje filtrujące dane, których głównym zadaniem jest dostarczanie spersonalizowanych rekomendacji produktów. Celem badań było dokonanie analizy i porównania dwóch metod uczenia maszynowego wykorzystywanych do generowania rekomendacji. System rekomendacji zbudowano na podstawie metody filtrowania kognitywnego opartej o treści oraz na podstawie metody filtrowania kolaboratywnego opartej o oceny użytkowników. Wnioski z przeprowadzonych badań pokazują wady i zalety obu metod.
5
Content available The recommendation algorithm for an online art gallery
Karwowski W., Sosnowska J., Rusek M.
Information Systems in Management
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2018
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Vol. 7, No. 2
108--119
EN
The paper discusses the need for recommendations and the basic recommendation systems and algorithms. In the second part the design and implementation of the recommender system for online art gallery (photos, drawings, and paintings) is presented. The designed customized recommendation algorithm is based on collaborative filtering technique using the similarity between objects, improved by information from user profile. At the end conclusions of performed algorithm are formulated.
6
Content available remote Transformation of "user-object" matrix for the collaborative filtering
Savchuk T., Sakaliuk A., Wójcik W., Burlibay A.
Przegląd Elektrotechniczny
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2014
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R. 90, nr 1
55-59
EN
The paper is devoted to application of collaborative filtering that is one of the method of automatic data filtering in the Internet. The main disadvantage of the approach is the necessity of performing a large number of operations. The authors have presented a mean of overcoming this problem by reduction of the dimension of the input matrix. Experimental results show that it had led not only to reduction of computational time, but also increased the accuracy of recommendations obtained.
PL
Artykuł poświęcony jest filtrowaniu kolaboracyjnemu, które jest jedną z metod automatycznej filtracji danych w sieci Internet. Główną wadą wspomnianego podejścia jest konieczność wykonywania bardzo dużej liczby operacji. Autorzy przedstawili rozwiązanie tego problemu polegający na redukcji wymiarowości przetwarzanej macierzy. Rezultaty badań pokazują, że oprócz zmniejszenia czasu obliczeń, uzyskano poprawę dokładności uzyskiwanych rekomendacji.
7
Content available remote Holistic Entropy Reduction for Collaborative Filtering
Szwabe A., Misiorek P, Janasiewicz T, Walkowiak P.
Foundations of Computing and Decision Sciences
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2014
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Vol. 39, No. 3
209--229
EN
We propose a collaborative filtering (CF) method that uses behavioral data provided as propositions having the RDF-compliant form of (user X, likes, item Y ) triples. The method involves the application of a novel self-configuration technique for the generation of vector-space representations optimized from the information-theoretic perspective. The method, referred to as Holistic Probabilistic Modus Ponendo Ponens (HPMPP), enables reasoning about the likelihood of unknown facts. The proposed vector-space graph representation model is based on the probabilistic apparatus of quantum Information Retrieval and on the compatibility of all operators representing subjects, predicates, objects and facts. The dual graph-vector representation of the available propositional data enables the entropy-reducing transformation and supports the compositionality of mutually compatible representations. As shown in the experiments presented in the paper, the compositionality of the vector-space representations allows an HPMPP-based recommendation system to identify which of the unknown facts having the triple form (user X, likes, item Y ) are the most likely to be true in a way that is both effective and, in contrast to methods proposed so far, fully automatic.
8
Content available remote Utilizing User Access Sequence to Mitigate the Cold Start Problem in Collaborative Filtering Recommendation
Deng X.
Przegląd Elektrotechniczny
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2013
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R. 89, nr 1b
217--220
EN
Collaborative filtering (CF) is one of the most successful recommending techniques, but it suffers from the cold start problem which severely affected the quality of recommendation. To address this problem, we propose a novel hybrid approach, named UAS-CF, which incorporates user access sequence into traditional CF for improving the quality of recommendation. Experiments on three datasets were carried out to evaluate the performance of our method. Our results show that our approach outperforms other methods and improves recommendation quality effectively.
PL
W artykule zaprezentowano nowe podejście UAS-CF do obsługi poleceń, które włącza sekwencję dostępu użytkownika do klasycznego filtrowania uwspólnionego (ang. Collaborative Filtering), w celu polepszenia jakości rekomendacji. Badania eksperymentalne, przeprowadzone na trzech sekwencjach danych, wykazują wysoką jakość rekomendacji w porównaniu z innymi metodami.
9
Content available remote Collaborative filtering based on bi-relational data representation
Szwabe A., Misiorek P, Ciesielczyk M, Jedrzejek C
Foundations of Computing and Decision Sciences
|
2013
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Vol. 38, No. 1
67--83
EN
Widely-referenced approaches to collaborative filtering (CF) are based on the use of an input matrix that represents each user profile as a vector in a space of items and each item as a vector in a space of users. When the behavioral input data have the form of (userX, likes, itemY) and (userX, dislikes, itemY) triples one has to propose a representation of the user feedback data that is more suitable for the use of propositional data than the ordinary user-item ratings matrix. We propose to use an element-fact matrix, in which columns represent RDF-like behavioral data triples and rows represent users, items, and relations. By following such a triple-based approach to the bi-relational behavioral data representation we are able to improve the quality of collaborative filtering. One of the key findings of the research presented in this paper is that the proposed bi-relational behavioral data representation, while combined with reflective matrix processing, significantly outperforms state-of-the-art collaborative filtering methods based on the use of a ‘standard’ user-item matrix.
10
Content available Collaborative filtering recommender systems in music recommendation
Kużelewska U., Ducki R.
Advances in Computer Science Research
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2013
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Nr 10
67--79
EN
Nowadays, the primary place of information exchange is the internet. Its features, such as: availability, unlimited capacity and diversity of information influenced its unrivalled popularity, making the internet a powerful platform for storage, dissemination and retrieval of information. On the other hand, the internet data are highly dynamic and unstructured. As a result, the internet users face the problem of data overload. Recommender systems help the users to find the products, services or information they are looking for. The article presents a recommender system for music artist recommendation. It is composed of user-based as well as item-based procedures, which can be selected dynamically during a user’s session. This also includes different similarity measures. The following measures are used to assess the recommendations and adapt the appropriate procedure: RMSE, MAE, Precision and Recall. Finally, the generated recommendations and calculated similarities among artists are compared with the results from LastFM service.
PL
W obecnych czasach głównym miejscem wymiany informacji jest internet. Jego cechy, takie jak: wysoka dostępność, nieograniczona pojemność i różnorodność informacji wpłynęły na jego niezrównana popularność. W ten sposób internet stał się potężną platformą do przechowywania, rozpowszechniania i udostępniania informacji. Z drugiej strony, dane internetowe są bardzo dynamiczne i niestrukturalizowane. W rezultacie, użytkownicy internetu muszą radzić sobie z problemem przeładowania danych. Systemy rekomendujące służą pomocą użytkownikom w celu znalezienia poszukiwanych produktów, usług lub informacji. W artykule przedstawiono system rekomendujący artystów muzycznych. Składa się on z procedur typu user-based oraz item-based oraz różnych sposobów szacowania podobieństwa, które mogą˛ być zmieniane dynamicznie podczas sesji użytkownika. Do oceny list rekomendacji wykorzystano następujące miary: RMSE, MAE, Precision i Recall. Dodatkowo, wygenerowane rekomendacje i obliczone podobieństwa miedzy artystami są porównywane z wynikami z serwisu LastFM .
11
Pamięciowe algorytmy kooperatywnej filtracji
Cichosz P.
Elektronika : konstrukcje, technologie, zastosowania
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2009
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Vol. 50, nr 8
267-271
PL
Zadanie kooperatywnej filtracji, dla pewnego zbioru użytkowników i obiektów, polega na prognozowaniu ocen, jakie przyznaliby użytkownicy nieznanym sobie obiektom, na podstawie znanych ocen przyznanych przez tych użytkowników innym obiektom oraz przez innych użytkowników tym samym obiektom. Rozwiązanie tego zadania umożliwia tworzenie inteligentnych systemów rekomendujących (np. dla elektronicznego handlu) charakteryzujących się wysokim stopniem trafności i indywidualizacji ofert. Artykuł jest poświęcony algorytmom kooperatywnej filtracji opartym na zapamiętywaniu znanych ocen i wykorzystywaniu ich do identyfikacji "najbliższych sąsiadów", na podstawie których wyznaczana jest prognoza. Przedstawiony jest wspólny schemat tego typu algorytmów, jego najbardziej typowa konkretyzacja oraz pozostające do zbadania możliwości poprawy dokładności lub efektywności.
EN
The collaborative filtering task, for a given set of users and items, consists in predicting ratings that the users would assign to unknown items based on known ratings assigned by the same users to similar items or by similar users to the same items. Solutions to this task enable creating intelligent recommender systems (e.g., for e-commerce). The paper is devoted to collaborative filtering algorithms based on storing known ratings and using them to identify the nearest neighbors used to calculate the predictions. A common basic template of such algorithms is presented with its most typical instantiation and remaining possibilities of improving accuracy or efficiency.
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