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In order to generate effective results, it is essential for a recommender system to model the information about the user interests (user profiles). A profile usually contains preferences that reflect the recommendation technique, so collaborative systems represent a user with the ratings given to items, while content-based approaches assign a score to semantic/text-based features of the evaluated...
In this paper, we address the problem of combining information in recommender systems (RSs) based on Dempster-Shafer theory (DST). We first discuss the characteristics of this problem, and then analyze six popular combination methods in the context of RSs. Based on the analysis, we propose two new mixed combination methods which can be considered as useful tools for fusing information in the systems...
This paper proposes a new method for combining information in recommender systems based on Dempster-Shafer theory. Within this method, focal elements whose probabilities are less than or equal to an infinitesimal threshold are considered as noise that may be caused by the process of fusing information, and then eliminated. Comparing with two baselines, known as 2-points focused and 2-probabilities...
Information overload is one of the most important problems in context of personalized document retrieval systems. In this paper we propose to use ontology-based user profile. Ontological structures are appropriate to represent relations between concepts in user profile. We present a method for determining user profile based on his current activities. Results obtained in experimental evaluation are...
New distribution channels like music streaming platforms paved way for making more and more diverse music available to users. Thus, music recommender systems got in the focus of research in academia as well as industry. Collaborative filtering-based recommender systems have been proven useful, but there is space left for improvements by adapting this general approach to better fit to the music recommendations...
While fans of classical music were found to be underrepresented on social media and music streaming platforms, they constitute an important target group for music recommender systems. We therefore focus on this group of listeners and investigate a wide range of recommendation approaches and variants for the task of music artist recommendation. Within the group of classical music listeners, we further...
Rating aggregation is critical to the quality control of recommendation systems and its effectiveness is a deep concern of all users. However, there are some problems in existing recommendation systems. For example, some of the raters from certain source are much more stringent than others, leading the phenomena that some entities with better quality are rejected. In this paper, we propose a novel...
Recommender systems (RS) have been popular for decades and many novel types of RS have been proposed and developed, such as context-aware recommender systems (CARS) which additionally take contexts (e.g., time, location, occasion, etc) into consideration to further assist users' decision makings. Meantime, the emergence of CARS also brings new recommendation opportunities, such as context suggestion...
Current research environments are witnessing high enormities of presentations occurring in different sessions at academic conferences. This situation makes it difficult for researchers (especially juniors) to attend the right presentation session(s) for effective collaboration. In this paper, we propose an innovative venue recommendation algorithm to enhance smart conference participation. Our proposed...
The plethora of talks and presentations taking place at academic conferences makes it difficult, especially for young researchers to attend the right talks or discuss with participants and potential collaborators with similar interests. Participants may not have a priori knowledge that allows them to select the right talks or informal interactions with other participants. In this paper we present...
In this paper, we investigate the possibilities of interpreting user behaviour in order to learn his/her preferences. UP Comp, a PHP component enabling use of user preferences for recommendation, is described. UP Comp is a standalone component that can be integrated into any PHP web with only basic knowledge of PHP, HTML and SQL. The methods of user behaviour interpretation are evaluated on a real...
To push the right information to the right person at the right time, classical work on recommendation systems focuses on optimizing the rating of recommended items. Recent research on context-awareness and knowledge exchange shows potentials of recommendation systems in engineering work. Requirements engineering can also profit from recommendation systems in several scenarios, including maintaining...
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