Recommender System
With the development of recommender systems (RS), several promising systems
have emerged, such as context-aware RS, multi-criteria RS, and group RS. However, the
education domain may not benefit from these developments due to missing information, such
as contexts and multiple criteria, in educational data sets. In this paper, we announce and
release an open data set for educational recommender systems. This data set includes not
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<p>Finding software developers with expertise in specific technologies that align with industry domains is an increasingly critical requirement. However, due to the ever-changing nature of the technology industry, locating these professionals has become a significant challenge for companies and institutions. This research presents a comprehensive overview of studies exploring suitable recommendation systems that can assist companies in addressing this pressing need.
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This dataset contains 4669820 ratings from 1499238 users to 351109 movies on the imdb.com website. This data is collected from reviews (https://www.imdb.com/review/rw0000001/). Each row in this dataset is as follows:
userID, movieID, rating, review date
For example :
ur18238764, tt2177461, 9, 22 January 2019
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In this paper, we present a collaborative recommend system that recommends elective courses for students based on similarities of student’s grades obtained in the last semester. The proposed system employs data mining techniques to discover patterns between grades. Consequently, we have noticed that clustering students into similar groups by performing clustering. The data set is processed for clustering in such a way that it produces optimal number of clusters.
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A Hybrid Approach to Service Recommendation Based on Network Representation Learning
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A Hybrid Approach to Service Recommendation Based on Network Representation Learning
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This dataset includes the Channels Switch Sequences of 300 IPTV viewers in Guangzhou, P.R. China, in Augest, 2014. There are 4 columns in the file, which represent viewer ID, the current channel number, the next channel number, the date of the month, respectively. The first column, the ID code of a viewer, ranks in descent with the times the viewer watched tv channels. The more times a viewer watches tv channels, the bigger the ID is. In a day, the rows are time series and generated step by step as the real watching tv behavior.
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This dataset includes the Channels Switch Sequences of 300 IPTV viewers in Guangzhou, P.R. China, in Augest, 2014.
There are 4 columns in the file, which represent viewer ID, the current channel number, th next channel number, the date of the month, respectively.
The first column, the ID code of a viewter, ranks with the times the viewer watched tv channels. The more times a viewer watches tv channels, the bigger
the ID is. In a day, the rows are time series and generated step by step as the real watching tv behavior.
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