The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems

@article{Kowald2017TheTF,
  title={The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems},
  author={Dominik Kowald and Simone Kopeinik and E. Lex},
  journal={Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization},
  year={2017}
}
Recommender systems have become important tools to support users in identifying relevant content in an overloaded information space. To ease the development of recommender systems, a number of recommender frameworks have been proposed that serve a wide range of application domains. Our TagRec framework is one of the few examples of an open-source framework tailored towards developing and evaluating tag-based recommender systems. In this paper, we present the current, updated state of TagRec… 

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References

SHOWING 1-10 OF 43 REFERENCES

TagRec: towards a toolkit for reproducible evaluation and development of tag-based recommender algorithms

TLDR
The purpose of TagRec is to provide the research community with a standardised framework that supports all steps of the development process and the evaluation of tag-based recommendation algorithms in a reproducible way, including methods for data pre-processing, data modeling and recommender evaluation.

An analysis of tag-recommender evaluation procedures

TLDR
It is shown, that a recommender's performance depends on the particular core and correlations between performances on different cores are explored.

Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study

TLDR
An open-source evaluation framework is used to compare a rich set of state-of-the-art algorithms in six unfiltered, open datasets via various metrics, measuring not only accuracy but also the diversity, novelty and computational costs of the approaches.

Recommending Items in Social Tagging Systems Using Tag and Time Informations

TLDR
The usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an eective item recommender that outperforms two alternative algorithms which also exploit time and tag- based information.

Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations

TLDR
It is concluded that recommender systems can be improved by incorporating related principles of human cognition by implementing an interplay between individual micro-level and collective macro-level processes in the form of a novel tag recommender algorithm.

A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy

TLDR
A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy, storing and classifying the tags used to label a set of available resources.

Tag Recommendations in Folksonomies

TLDR
This paper evaluates and compares two recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank, showing that both provide better results than non-personalized baseline methods.

Social ranking: uncovering relevant content using tag-based recommender systems

TLDR
This paper proposes Social Ranking, a method that exploits recommender system techniques to increase the efficiency of searches within Web 2.0, and proposes a mechanism to answer a user's query that ranks content based on the inferred semantic distance of the query to the tags associated to such content, weighted by the similarity of the querying user to the users who created those tags.

Which Algorithms Suit Which Learning Environments? A Comparative Study of Recommender Systems in TEL

TLDR
This work evaluates six state-of-the-art recommendation algorithms for tag and resource recommendations on six empirical datasets and demonstrates that the performance of the algorithms strongly depends on the properties and characteristics of the particular dataset.