Recommender systems (RSs) consist of predicting missing ratings based on the observed ones. This problem corresponds to matrix completion where users are its rows and items are its columns and it contains the observed ratings. An efficient RS is the one promoting the personal relevancy of its users which is not the case in the matrix completion process. It takes into account all the rates for prediction without including the users’ and items’ characteristics. Patterns are the key enablers of such solutions. In this work, we present a three-mode tensor representation with two aspects namely user–item interactions (observed ratings) and item–item relationship (detected similarities). To capture the similarities, the patterns are grouped in an equivalent manner using a bi-quantum clustering process (Quantum K-means). This step is adopted to consider only the relevant observed ratings in the prediction process and express item-to-item relationship using fidelity distance. Then, for each missing rating r that a user u might give to an item i in the future, a sub-tensor is created according to the detected patterns. This sub-tensor then is completed by minimizing its rank. This problem is NP-hard, hence a surrogate is used which is the nuclear norm. The effectiveness of the proposed approach is measured according to information retrieval evaluation criteria: precision, recall and [Formula: see text]-measure. The proposed approach improved the precision of the state-of-the-art methods by 20[Formula: see text].

Abstract Faster than classical algorithms, quantum algorithms benefit from the superposition property of quantum information to offer significant speedup to complex algorithms. Therefore, quantum computing can be used to help machine learning algorithms by boosting their performance and accelerate the processing of time‐consuming ones. Clustering algorithms are very complex unsupervised learning algorithms. Indeed, the similarity calculation (distance) between input vectors is a resource‐consuming step, especially when working with large datasets. In this article, we propose a new better‐performing recommender system that operates as a combination of an adapted quantum K‐means algorithm and the singular value decomposition (SVT) algorithm. We integrate the developed quantum clustering algorithm to a prediction process of the proposed recommender system using matrix completion. To the best of our knowledge, no system with such details was proposed in the literature. The system was applied on the MovieLens dataset without a dimensionality reduction step and evaluated according to measures of information retrieval systems. The results of the quantum K‐means algorithm show that the quantum version leads to a logarithmic reduction of the time complexity compared to the classical algorithm. The proposed system has proved to be better than the previous tested ones in terms of precision and recall.

Systems of data integration using ontologies aim to implement a collaborative environment between sources for sharing data and services to respond a user request for information. Their users’ requests are an exact expression of their needs. However, the multiplicity of data sources, their scalability and the increasing difficulty to control their descriptions and their contents are the reasons behind the implacability of this assumption today. The users now may not know the data sources they questioned, nor their description or content. Consequently, their queries reflect no more a need that must be satisfied but an intention that must be refined according to data sources available at the time of interrogation. In this work, we present a semantic-based approach to enrich user’ queries expressed in SPARQL Language by his preferences in order to adapt the returned results and make them more precise and more relevant. The proposed approach is applied on a movies management system based on the standard MovieLens dataset. The obtained results are compared to existing approaches according to precision and recall measures. Our approach improved the precision with 26% and the recall with 7% comparing to those of previous study using collaborative filtering.

The emergence of new applications that need to share information between different sources of information, such as e-learning, e-commerce, e-government, and electronic libraries requires interoperability between information systems. The information systems are designed and developed by different organizations to be autonomous and heterogeneous sources which lead to multiple structuring formats and different interpretations of the same data. So to simplify data access, the virtual integration of heterogeneous, autonomous and distributed data sources is required based on the couple mediator-wrapper and founded on ontologies that provide a consensual terminology. The mediator masks the heterogeneity and distributes the data sources, while the adapter accommodates queries to data sources formats. Information systems exchange data and services thanks to the rich context that the semantic web offers. It aims to provide data understandable by humans and machines that perform automatic processing by software modules following a semantic enrichment. This article is a survey of the state of art that compare the previous works according to several criteria.