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Efficient Parallelization for Big Data Collaborative Recommendation Decisions
موثر بودن تقسیم بندی برای تصمیم گیری های هماهنگ با توصیه های بزرگ-2018 This paper proposes a novel SentimentBased Probabilistic Tensor Analysis technique {sentiPTF} to address the information overload problem
through information filtering. The proposed
framework first applies a Natural Language
Processing (NLP) technique to perform sentiment
analysis taking advantage of the huge sums of textual
data generated in from the social media are
predominantly left untouched. Although some
current studies do employ review texts, many of them
do not consider how sentiments in reviews influence
recommendation algorithm for prediction. Existing
works concentrate only on rating matrix which are
often sparse. There is therefore this big data text
analytics gap whose modeling is computationally
expensive. Probabilistic Tensor Factorization (PTF)
is a standard technique for such large scale
processing. From our experiments, our novel
machine learning sentiment-based probabilistic
tensor analysis (senti-PTF) is computationally less
expensive, scalable and addresses the scalability
problem and cold-start problems, for optimal
recommendation decision making as shown from our
error detection evaluation metrics.
Index Terms: data partitioning, ProbabilisticTensor Factorization, senti-PTF, rat-PTF |
مقاله انگلیسی |