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Recommendation of microblog users based on hierarchical interest profiles

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Abstract

Quite a number of recent works have concentrated on the task of recommending to Twitter users whom they should follow, among which, the WTF (Who To Follow) service provided by Twitter. Recommenders are based, either on the user’s network structure, or on some notion of topical similarity with other users, or on both. In this paper, we propose to accomplish the recommendation task in two steps: First, we profile users and classify them as belonging to a target community (depending e.g., on their political affiliation, preferred football team, favorite coffee shop, etc.). Then, we fine-tune recommendations for selected populations. We cast both problems of user classification and recommendation as one of itemset mining, where items are either users’ authoritative friends or semantic categories associated to friends, extracted from WiBi, the Wikipedia Bitaxonomy. In addition to evaluating our profiler and recommender on several populations, we also show that semantic categories allow for very fine-grained population studies, and make it possible to recommend not only whom to follow, but also topics of interest, users interested in the same topic, and more.

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Notes

  1. http://expandedramblings.com/index.php/march-2013-by-the-numbers-a-few-amazing-twitter-stats/#.U5gOgS8Q6X0.

  2. http://www.scientificamerican.com/article/twitter-to-release-all-tweets-to-scientists-a-trove-of-billions-of-tweets-will-be-a-research-boon-and-an-ethical-dilemma/.

  3. http://babelnet.org/, http://babelfy.org/.

  4. The interested reader is invited to read the WiBi paper (Flati et al. 2014) for additional details.

  5. hereafter, we omit the k index to simplify notation.

  6. FP-Growth implementation http://www.borgelt.net/fpgrowth.html.

  7. leave-one-out is the standard evaluation procedure in user recommendation literature.

  8. remember that at level zero, items are Twitter users accounts.

  9. http://babelnet.org/explore.jsp.

  10. this may seem obvious, however it is often the case that quantitative data contradict intuition.

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Authors and Affiliations

  1. Dip. Informatica, La Sapienza, via Salaria 113, Rome, Italy

    Stefano Faralli, Giovanni Stilo & Paola Velardi

Authors
  1. Stefano Faralli
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  2. Giovanni Stilo
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  3. Paola Velardi
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Corresponding author

Correspondence to Paola Velardi.

Appendix: FP-growth

Appendix: FP-growth

Fig. 13
figure 13

An FP-tree instance

Full size image
figure a
figure b
figure c

A frequent-pattern tree FP-tree is a compact structure that stores quantitative information about frequent patterns in a database. Han et al. define the FP-tree structure as follows (see Fig. 13 for an FP-tree instance example from Han et al. (2011)):

  • One root labeled as “null” with a set of item-prefix subtrees as children, and a frequent-item-header table (presented in the left side of Fig. 13);

  • Each node in the item-prefix subtree consists of three fields:

    • Item-name: registers which item is represented by the node;

    • Count: the number of transactions represented by the portion of the path reaching the node;

    • Node-link: links to the next node in the FP-tree carrying the same item-name, or null if there is none.

  • Each entry in the frequent-item-header table consists of two fields:

    • Item-name: as the same to the node;

    • Head of node-link: a pointer to the first node in the FP-tree carrying the item-name.

  • Additionally, the frequent-item-header table can have the count support for an item.

Algorithms 1 and 2 show how to create an FP-Tree structure and the Algorithm 3 shows the steps to obtain the complete set of frequent patterns starting from a given FP-Tree. Additional details can be found in Han and Pei (2000).

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Faralli, S., Stilo, G. & Velardi, P. Recommendation of microblog users based on hierarchical interest profiles. Soc. Netw. Anal. Min. 5, 25 (2015). https://doi.org/10.1007/s13278-015-0264-2

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