Laura Vanessa Cruz Quispe

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In this paper we propose a course recommendation system based on historical grades of students in college. Our model will be able to recommend available courses in sites such as: Coursera, Udacity, Edx, etc. To do so, proba-bilistic topic models are used as follows. On one hand, Latent Dirichlet Allocation (LDA) topic model infers topics from content given(More)
In recent years, the Web and social media are growing exponentially. We are provided with documents which have opinions expressed about several topics. This constitute a rich source for Natural Language Processing tasks, in particular, Sentiment Analysis. In this work, we aim at constructing a sentiment dictionary based on words obtained from web pages(More)
This paper describes some experiments carried out to measure sentiment, which we call emotional reaction, on blog headlines. We analyze a text corpus of titles from Facebook entries or posts linking to a website. These titles are basically headlines and we study them to understand the relationship between article headlines and the self-reported reactions of(More)
We present a probabilistic approach based on TrueSkill for Content-Based Recommendation Systems. On one hand, this proposal allow us to tackle the "cold start" problem because it relies on a content-based approach. On the other hand, it is valuable for handling high uncertainty since it solely depends on available items and ratings given by users. Thus,(More)
Automated recommendation systems have been increasingly adopted by companies that aim to draw people attention about products and services on Internet. In this sense, development of distributed model abstractions such as MapReduce and GraphLab has brought new possibilities for recommendation research tasks due to allow us to perform Big Data analysis. Thus,(More)
In this work a probabilistic approach based on TrueSkill for Preference Elicitation is presented. This approach allow us to tackle the "cold start" problem because relies on a content based recommendation system. In addition, it is valuable for handling high uncertainty due there is no dependency on the number of products and users. The only dependency is(More)
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