In recent years, social networks have become one of the best ways to access information. The ease with which users connect to each other and the opportunity provided by Twitter and other social tools in order to follow person activities are increasing the use of such platforms for gathering information. The amount of available digital data is the core of the new challenges we now face. Social recommender systems can suggest both relevant content and users with common social interests. Our approach relies on a signal-based model, which explicitly includes a time dimension in the representation of the user interests. Specifically, this model takes advantage of a signal processing technique, namely, the wavelet transform, for defining an efficient pattern-based similarity function among users. Experimental comparisons with other approaches show the benefits of the proposed approach.

Arru, G., Gurini, D.F., Gasparetti, F., Micarelli, A., & Sansonetti, G. (2013). Signal-based user recommendation on twitter. In WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web (pp.941-944) [10.1145/2487788.2488088].

Signal-based user recommendation on twitter

GASPARETTI, FABIO;MICARELLI, Alessandro;SANSONETTI, GIUSEPPE
2013

Abstract

In recent years, social networks have become one of the best ways to access information. The ease with which users connect to each other and the opportunity provided by Twitter and other social tools in order to follow person activities are increasing the use of such platforms for gathering information. The amount of available digital data is the core of the new challenges we now face. Social recommender systems can suggest both relevant content and users with common social interests. Our approach relies on a signal-based model, which explicitly includes a time dimension in the representation of the user interests. Specifically, this model takes advantage of a signal processing technique, namely, the wavelet transform, for defining an efficient pattern-based similarity function among users. Experimental comparisons with other approaches show the benefits of the proposed approach.
9781450320382
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/309365
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