Recommender System

1. MovieLens-1M: This dataset contains user ratings for movies from the MovieLens website. It consists of 6,040 users and 3,952 movies. Users rate movies on a 5-star scale, and for our analysis, we convert the ratings into binary signals (positive and negative feedback) using a threshold of 3.5.

With the development of recommender systems (RSs), several promising systems have emerged, such as context-aware RS, multi-criteria RS, and group RS. Multi-criteria recommender systems (MCRSs) are designed to provide personalized recommendations by considering user preferences in multiple attributes or criteria simultaneously. Unlike traditional RSs that typically focus on a single rating, these systems help users make more informed decisions by considering their diverse preferences and needs across various dimensions.

The dataset presents user evaluations for itinerary recommendations generated with three algorithms, PP, PP+TS and PP+TP.
Users evaluated recommendations according to five properties:

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

only traditional rating entries, but also enriched information, e.g., contexts, user preferences

<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.

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.

A Hybrid Approach to Service Recommendation Based on Network Representation Learning

A Hybrid Approach to Service Recommendation Based on Network Representation Learning

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.