With high quality of data, enterprises will add value of business. How-ever, poor data has resulted in waste of resource, low service efficiency and high costs in every area. In this paper, we firstly focus on basic issues of data quality, like where the quality problems come from and how to describe it. Then we study some cases of data quality management from a holistic enterprise perspective to the details of perspective, that is, hierarchical management architecture, frameworks, approaches and algorithm. Quality assessment of data is also an important theme, which is used to show whether data is good enough and to help people master credibility of data quality. We study some assessment algorithm and models. When problem occur, what tools should be used in a project is also included in the paper. Finally, we provide some outstanding research topics and unresolved issues for future.
Recent years have witnessed the growth of recommender systems, with the help of deep learning techniques. Recurrent Neural Networks (RNNs) play an increasingly vital role in various session-based recommender systems, since they use the user’s sequential history to build a comprehensive user profile, which helps improve the recommendation. However, a problem arises regarding how to be aware of the variation in the user’s contextual preference, especially the short-term intent in the near future, and make the best use of it to produce a precise recommendation at the start of a session. We propose a novel approach named Attention-based Short-term and Long-term Model (ASLM), to improve the next-item recommendation, by using an attention-based RNNs integrating both the user’s short-term intent and the long-term preference at the same time with a two-layer network. The experimental study on three real-world datasets and two sub-datasets demonstrates that, compared with other state-of-the-art methods, the proposed approach can significantly improve the next-item recommendation, especially at the start of sessions. As a result, our proposed approach is capable of coping with the cold-start problem at the beginning of each session.
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