Memory effect of the online user preference

The mechanism of the online user preference evolution is of great significance for understanding the online user behaviors and improving the quality of online services. Since users are allowed to rate on objects in many online systems, ratings can well reflect the users' preference. With two benchmark datasets from online systems, we uncover the memory effect in users' selecting behavior which is the sequence of qualities of selected objects and the rating behavior which is the sequence of ratings delivered by each user. Furthermore, the memory duration is presented to describe the length of a memory, which exhibits the power-law distribution, i.e., the probability of the occurring of long-duration memory is much higher than that of the random case which follows the exponential distribution. We present a preference model in which a Markovian process is utilized to describe the users' selecting behavior, and the rating behavior depends on the selecting behavior. With only one parameter for each of the user's selecting and rating behavior, the preference model could regenerate any duration distribution ranging from the power-law form (strong memory) to the exponential form (weak memory).

[1]  Bernardo A. Huberman,et al.  How Random are Online Social Interactions? , 2012, Scientific reports.

[2]  Jianguo Liu,et al.  Prediction of collective opinion in consensus formation , 2014 .

[3]  Jürgen Kurths,et al.  Evidence for a bimodal distribution in human communication , 2010, Proceedings of the National Academy of Sciences.

[4]  Zi-Gang Huang,et al.  Emergence of scaling in human-interest , 2013 .

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

[6]  Lei Hou,et al.  Long-term memories in online users' selecting activities , 2014 .

[7]  Kwang-Il Goh,et al.  Burstiness and memory in complex systems , 2006 .

[8]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[9]  Beom Jun Kim,et al.  Role of activity in human dynamics , 2007, EPL (Europhysics Letters).

[10]  Nick Craswell,et al.  Random walks on the click graph , 2007, SIGIR.

[11]  Alexei Vázquez,et al.  Exact results for the Barabási model of human dynamics. , 2005, Physical review letters.

[12]  Albert-László Barabási,et al.  The origin of bursts and heavy tails in human dynamics , 2005, Nature.

[13]  H. Stanley,et al.  Cross-correlations between volume change and price change , 2009, Proceedings of the National Academy of Sciences.

[14]  Albert-László Barabási,et al.  Modeling bursts and heavy tails in human dynamics , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Zimo Yang,et al.  Anchoring bias in online voting , 2012, ArXiv.

[16]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[17]  Marta C. González,et al.  A universal model for mobility and migration patterns , 2011, Nature.

[18]  Tao Zhou,et al.  Modeling human dynamics with adaptive interest , 2007, 0711.0741.

[19]  Qiang Guo,et al.  Effect of the time window on the heat-conduction information filtering model , 2014 .

[20]  Yi-Cheng Zhang,et al.  Solving the apparent diversity-accuracy dilemma of recommender systems , 2008, Proceedings of the National Academy of Sciences.

[21]  Jukka-Pekka Onnela,et al.  Spontaneous emergence of social influence in online systems , 2009, Proceedings of the National Academy of Sciences.

[22]  Qiang Guo,et al.  Ceiling effect of online user interests for the movies , 2014 .

[23]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[24]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[25]  Lei Hou,et al.  EMPIRICAL ANALYSIS OF THE CLUSTERING COEFFICIENT IN THE USER-OBJECT BIPARTITE NETWORKS , 2013 .

[26]  A. Barabasi,et al.  Human dynamics: Darwin and Einstein correspondence patterns , 2005, Nature.

[27]  Zhi-Dan Zhao,et al.  Emergence of scaling in human-interest dynamics , 2013, Scientific Reports.

[28]  Zhao-Long Hu,et al.  Effect of the social influence on topological properties of user-object bipartite networks , 2013 .

[29]  A. Barabasi,et al.  Dynamics of information access on the web. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Adilson E. Motter,et al.  A Poissonian explanation for heavy tails in e-mail communication , 2008, Proceedings of the National Academy of Sciences.

[31]  Yang Li,et al.  INFORMATION FILTERING BASED ON USERS' NEGATIVE OPINIONS , 2013 .

[32]  Anna Chmiel,et al.  Scaling of human behavior during portal browsing. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  I. L'Heureux,et al.  Are Hurst exponents estimated from short or irregular time series meaningful , 2003 .

[34]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[35]  S. Havlin,et al.  Scaling laws of human interaction activity , 2009, Proceedings of the National Academy of Sciences.