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Finding patterns or clusters in streaming data is very important in the present information mining. The most critical issue is the huge amount of data versus the limited size of storage space. In the previous works, the essential information of huge data was represented by subsets of data, grid summarization, or spherical function. Those forms of data representation are not compact enough to capture...
The traditional recommender system makes the recommendations using the overall ratings toward items provided by the users. However, the multi-criteria recommender system suggests that considering the effects of criteria ratings to the overall rating is the key to provide more personalized recommendations. In this work, a novel multi-criteria recommendation technique is proposed. The prediction from...
Most of existing Business process management (BPM) technologies have their own graphical user interfaces (GUIs), whereas users in different organizations who involved in business processes are more likely to work with a different set of GUIs. Consequently, developers have to build the specific set of GUIs in enterprise application which is appropriate for each business process, and they use BPM API...
Recommender systems (RS) are software tools that provide personalized recommendations of relevant items to individual users. However, most of them do not take into account additional contextual information that may affect user preferences, such as place, time, or weather. Context-aware recommender systems (CARS) have been proposed to solve this problem by providing recommendations for users based...
Collaborative Filtering Recommender Systems are used to recommend items that may match each user preference on the basis of preferences of similar users in the system. Since different users have different patterns of preference, there is a problem when one user's preference is used to recommend another user's preference. A way of converting one user's preference pattern into another user's pattern...
Recommender systems are becoming very useful for competitive businesses. It is very important for recommender systems to extract user preferences accurately by utilizing logs that record user behavior. Furthermore, user behavior should be analyzed from multiple aspects, storing the results as multicriteria rating scores. If the rating information is sparse, then systems are forced to compensate. One...
This paper proposes a Bayesian model for multicriteria (MC) recommender systems, which are useful tools for delivering information to those who require it. Such systems usually handle a single overall rating score to capture user's preferences. Recently proposed MC recommender systems use multiple scores evaluated from various aspects to obtain a more elaborate user profile. Our proposed model maps...
Facial expression is significant for face-to-face communication since it is one of our body language that increases data information during the communication. In recent surveys, some of the existing methods extracting features from facial images as the regions of interest. Such regions cover eyes and nose, eyes with eyebrows, mouth, etc. Then global features are extracted from those regions afterwards...
This paper proposes an algorithm for recommender systems that uses both positive and negative latent user models. In recommending items to a user, recommender systems usually exploit item content information as well as the preferences of similar users. Various types of content information can be attached to items and these are useful for judging user preferences. For example, in movie recommendations,...
Currently, people prefer shopping interesting items from the Internet. Therefore, e-business had installed recommender systems to support users in selecting products as quick as possible. The responsibility of the recommender system is to propose interesting items to the target users in order to gain their interests. Unfortunately, the existing recommender systems have some imperfect function under...
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