News Recommendation in Real-Time

Recommender systems support users facing information overload situations. Typically, such situations arise as users have to choose between an immense number of alternatives. Examples include deciding what songs to listen to, what movies to watch, and what news article to read. In this chapter, we outline the case of suggesting news articles. This task entails a number of challenges. First, news collections do not remain relevant unlike movies or songs. Users continue to request novel contents. Second, users avoid creating consistent profiles thus reject login procedures. Third, requests arrive in enormous streams. Having short consumption times, users quickly request the next article to read. Handling these challenges requires adaptations to existing recommendation strategies as well as developing novel ones.

[1]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[2]  Saul Vargas,et al.  Rank and relevance in novelty and diversity metrics for recommender systems , 2011, RecSys '11.

[3]  Fabio Aiolli,et al.  Efficient top-n recommendation for very large scale binary rated datasets , 2013, RecSys.

[4]  Sean M. McNee,et al.  Improving recommendation lists through topic diversification , 2005, WWW '05.

[5]  Michael J. Pazzani,et al.  Adaptive News Access , 2007, The Adaptive Web.

[6]  Frank Hopfgartner,et al.  Benchmarking News Recommendations in a Living Lab , 2014, CLEF.

[7]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .

[8]  Alejandro Bellogín,et al.  News@hand: A Semantic Web Approach to Recommending News , 2008, AH.

[9]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[10]  Kevin C. Almeroth,et al.  Workshop and challenge on news recommender systems , 2013, RecSys.

[11]  Guy Shani,et al.  Evaluating Recommendation Systems , 2011, Recommender Systems Handbook.

[12]  Alejandro Bellogín,et al.  Ontology-Based Personalised and Context-Aware Recommendations of News Items , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[13]  Pabitra Mitra,et al.  Aggregating preference graphs for collaborative rating prediction , 2010, RecSys '10.

[14]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[15]  Mária Bieliková,et al.  Content-Based News Recommendation , 2010, EC-Web.

[16]  Xavier Amatriain,et al.  Mining large streams of user data for personalized recommendations , 2013, SKDD.

[17]  Gediminas Adomavicius,et al.  Stability of Recommendation Algorithms , 2012, TOIS.

[18]  Robert A. Legenstein,et al.  Combining predictions for accurate recommender systems , 2010, KDD.

[19]  Aristides Gionis,et al.  From chatter to headlines: harnessing the real-time web for personalized news recommendation , 2012, WSDM '12.

[20]  Jose María Álvarez Rodríguez,et al.  Towards a journalist-based news recommendation system: The Wesomender approach , 2013, Expert Syst. Appl..

[21]  Licia Capra,et al.  Temporal collaborative filtering with adaptive neighbourhoods , 2009, SIGIR.

[22]  Andreas Lommatzsch,et al.  Real-Time News Recommendation Using Context-Aware Ensembles , 2014, ECIR.

[23]  Shunzhi Zhu,et al.  Personalized News Recommendation: A Review and an Experimental Investigation , 2011, Journal of Computer Science and Technology.

[24]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[25]  Steffen Rendle,et al.  Improving pairwise learning for item recommendation from implicit feedback , 2014, WSDM.

[26]  Seong-Bae Park,et al.  A location-based news article recommendation with explicit localized semantic analysis , 2013, SIGIR.

[27]  Mohammad Emtiyaz Khan,et al.  Scalable Collaborative Bayesian Preference Learning , 2014, AISTATS.

[28]  Boi Faltings,et al.  Personalized news recommendation with context trees , 2013, RecSys.

[29]  Li Chen,et al.  Trust-inspiring explanation interfaces for recommender systems , 2007, Knowl. Based Syst..

[30]  Zhaohui Zheng,et al.  Learning to model relatedness for news recommendation , 2011, WWW.

[31]  Gediminas Adomavicius,et al.  Context-aware recommender systems , 2008, RecSys '08.

[32]  Wei Chu,et al.  Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms , 2010, WSDM '11.

[33]  Antal van den Bosch,et al.  Comparing and evaluating information retrieval algorithms for news recommendation , 2007, RecSys '07.

[34]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[35]  Yang Guo,et al.  On top-k recommendation using social networks , 2012, RecSys.

[36]  Martha Larson,et al.  CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering , 2012, RecSys.

[37]  Anthony J. Hornof,et al.  High-cost banner blindness: Ads increase perceived workload, hinder visual search, and are forgotten , 2005, TCHI.

[38]  Gonzalo Navarro,et al.  Word-based self-indexes for natural language text , 2012, TOIS.

[39]  John Riedl,et al.  An Algorithmic Framework for Performing Collaborative Filtering , 1999, SIGIR Forum.

[40]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[41]  Yehuda Koren,et al.  Lessons from the Netflix prize challenge , 2007, SKDD.

[42]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[43]  Licia Capra,et al.  Temporal diversity in recommender systems , 2010, SIGIR.

[44]  Roberto Turrin,et al.  Performance of recommender algorithms on top-n recommendation tasks , 2010, RecSys '10.

[45]  Louis Wehenkel,et al.  Learning to Play K-armed Bandit Problems , 2012, ICAART.

[46]  Steffen Rendle,et al.  Learning recommender systems with adaptive regularization , 2012, WSDM '12.

[47]  Barry Smyth,et al.  Terms of a Feather: Content-Based News Recommendation and Discovery Using Twitter , 2011, ECIR.

[48]  Frank Hopfgartner,et al.  Overview of CLEF NewsREEL 2015: News Recommendation Evaluation Lab , 2015, CLEF.

[49]  Martha Larson,et al.  GAPfm: optimal top-n recommendations for graded relevance domains , 2013, CIKM.

[50]  Flavius Frasincar,et al.  Semantic news recommendation using wordnet and bing similarities , 2013, SAC '13.

[51]  Jiahui Liu,et al.  Personalized news recommendation based on click behavior , 2010, IUI '10.

[52]  Wanlei Zhou,et al.  Learning User Preference Patterns for Top-N Recommendations , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[53]  Qiang Yang,et al.  EigenRank: a ranking-oriented approach to collaborative filtering , 2008, SIGIR '08.

[54]  Domonkos Tikk,et al.  Scalable Collaborative Filtering Approaches for Large Recommender Systems , 2009, J. Mach. Learn. Res..

[55]  Tao Li,et al.  News recommendation via hypergraph learning: encapsulation of user behavior and news content , 2013, WSDM.

[56]  Wei Chu,et al.  A contextual-bandit approach to personalized news article recommendation , 2010, WWW '10.

[57]  Frank Hopfgartner,et al.  Workshop on benchmarking adaptive retrieval and recommender systems: BARS 2013 , 2013, SIGIR.

[58]  Frank Hopfgartner,et al.  Shedding light on a living lab: the CLEF NEWSREEL open recommendation platform , 2014, IIiX.

[59]  Bing Liu,et al.  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.

[60]  Qi Gao,et al.  Interweaving Trend and User Modeling for Personalized News Recommendation , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.