Paper Info
Title
Cold-start news recommendation with domain-dependent browse graph
Abstract
Online social networks and mash-up services create opportunities to connect different web services otherwise isolated. Specifically in the case of news, users are very much exposed to news articles while performing other activities, such as social networking or web searching. Browsing behavior aimed at the consumption of news, especially in relation to the visits coming from other domains, has been mainly overlooked in previous work. To address that, we build a BrowseGraph out of the collective browsing traces extracted from a large viewlog of Yahoo News (0.5B entries), and we define the ReferrerGraph as its subgraph induced by the sessions with the same referrer domain. The structural and temporal properties of the graph show that browsing behavior in news is highly dependent on the referrer URL of the session, in terms of type of content consumed and time of consumption. We build on this observation and propose a news recommender that addresses the cold-start problem: given a user landing on a page of the site for the first time, we aim to predict the page she will visit next. We compare 24 flavors of recommenders belonging to the families of content-based, popularity-based, and browsing-based models. We show that the browsing-based recommender that takes into account the referrer URL is the best performing, achieving a prediction accuracy of 48% in conditions of heavy data sparsity.
Year
DOI
Venue
2014
10.1145/2645710.2645726
RecSys
Keywords
Field
DocType
miscellaneous,sessions,cold-start,browsing behavior,news recommendation,browsegraph,cold start
Recommender system,Graph,World Wide Web,Social network,Web searching,Computer science,Web service,Cold start (automotive)
Conference
Citations 
PageRank 
References 
9
0.58
23
Authors
4
Name
Order
Citations
PageRank
Michele Trevisiol11438.42
Luca Maria Aiello271344.77
Rossano Schifanella361935.44
Alejandro Jaimes41461104.52