Recommender systems rely on large datasets of historical data and entail serious privacy risks. A server offering recommendations as a service to a client might leak more information than necessary regarding its recommendation model and training dataset. At the same time, the disclosure of the client’s preferences to the server is also a matter of concern. Providing recommendations while preser...

recommender systems (rs) provide personalized recommendation according to user need by analyzing behavior of users and gathering their information. one of the algorithms used in recommender systems is user-based collaborative filtering (cf) method. the idea is that if users have similar preferences in the past, they will probably have similar preferences in the future. the important part of col...

Process modeling facilitates understanding and restructuring of activities used to achieve business goals. However, manual process modeling is time-consuming and error-prone. In this paper, we describe a recommender system that suggests a list of correct and fitting process fragments for an edited business process model, which can be used to complete the process model being edited. The recommen...

Recommender systems support users in €nding relevant items in overloaded information spaces. Researchers and practitioners have proposed many di‚erent collaborative €ltering algorithms for different information scenarios, domains and contexts. One of the laŠer, are time-aware recommender methods that consider temporal dynamics in the users’ interests in certain items, topics, etc. While there i...

<span lang="EN-US">In this paper, we present a tourism recommender framework based on the cooperation of ontological knowledge base and supervised learning models. Specifically, new ontology, which not only captures domain but also specifies entities in numerical vector space, is presented. The recommendation making process enables machine models to work directly with from training step d...

Current music recommender systems only use basic information for recommending music to its listeners. These usually include artist, album, genre, tempo and other song information. Online recommender systems would include ratings and annotation tags by other people as well. We propose a recommender system that recommends music depending on how the listener wants to feel while listening to the mu...

In view of the existing user similarity calculation principle of recommendation algorithm is single, and recommender system accuracy is not well, we propose a novel social multi-attribute collaborative filtering algorithm (SoMu). We first define the user attraction similarity by users’ historical rated behaviors using graph theory, and secondly, define the user interaction similarity by users’ ...

Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a ...

Context-aware recommender systems extend traditional recommender systems by adapting their output to users’ specific contextual situations. Most of the existing approaches to context-aware recommendation involve directly incorporating context into standard recommendation algorithms (e.g., collaborative filtering, matrix factorization). In this paper, we highlight the importance of context simil...

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommende...