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It is a challenge to estimate expected benefits from recommender systems based on association rule mining. This paper aims to address this challenge and presents a study of buying preferences of a sample of retail customers. It reveals a monotonic, non-linear relationship between the expected profits (as a function of information loss) and minimum support threshold levels, when considering transactions...
Recommending users for a new social network user to follow is a topic of interest at present. The existing approaches rely on using various types of information about the new user to determine recommended users who have similar interests to the new user. However, this presents a problem when a new user joins a social network, who is yet to have any interaction on the social network. In this paper...
Top-k is a well-studied problem in the literature, due to its wide spectrum of applications, like information retrieval, database querying, Web search and data mining. In the big data era, the volume of the data and their velocity, call for efficient parallel solutions that overcome the restricted resources of a single machine. Our motivating application is recommenders, which typically deal with...
In this paper, we propose a new method for addressing post-purchase recommendations for a dynamic marketplace. The proposed method uses the transactional data as the primary data source to mine co-purchase relationships. The item listings from the transactional data are mapped to their static ‘cluster’ representation and a cluster-cluster directed graph is generated. Clusters have explicit definitions...
The Web today has gone far beyond a tool for simply posting and retrieving information, but a universal platform to accomplish various kinds of tasks in daily life. However, research and application of personalized recommendation are still mostly restricted to intra-site vertical recom-menders, such as video recommendation in YouTube, or product recommendation in Amazon. Usually, they treat users'...
An enduring issue in higher education is student retention to successful graduation. To further this goal, we develop a system for the task of predicting students' course grades for the next enrollment term in a traditional university setting. Each term, students enroll in a limited number of courses and earn grades in the range A-F for each course. Given historical grade data, our task is to predict...
There are users who generate significant amounts of domain knowledge in online forums or community question and answer (CQA) websites. Existing literature defines them as ‘experts.’ These users attain such statuses by providing multiple relevant answers to the question askers. Past works have focused on recommending relevant posts to these users. With the rise of web forums where certified experts...
Interactive database exploration is a key task in information mining. Relational databases have been long used as a critical infrastructure component to access and analyze large volumes of data in a variety of applications, including ad-hoc analytics over big data, large-scale data warehouses that support business-intelligence tools, and services for scientific-data exploration. To aid the users of...
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