Long-term memories in online users' selecting activities

Xue, Peng; Hou, Lei; Stephen, Mutua; Yang, Huijie

doi:10.1016/j.physleta.2014.07.012

Cited by 21 publications

(12 citation statements)

References 19 publications

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“…This phenomenon also coincides with human behavior characteristics such as memory effect 14,15 and period patterns. 16 So similar to spatial features, we utilize hot-times defined in section ''TCB algorithm'' as reference anchors to specify their effects on current online activity on temporality.…”

Section: Temporalitysupporting

confidence: 77%

TCB: A feature transformation method based central behavior for user interest prediction on mobile big data

Zhou

Jiang

Chen

et al. 2016

International Journal of Distributed Sensor Networks

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Although traditional spatial-temporal features, such as gyration, probability, and the intervals between consecutive records, have contributed to model human dynamics, the importance of these basic spatial-temporal features in predicting mobile user interest is not fully investigated. Moreover, these typical features ignore the fact that human behaviors are highly predictable and centralized. Specifically, human mobility is constrained in a small area depicted by several hotspots, and users tend to access mobile Internet intensively on several particular timeslots, which are defined as hot-times in this article. Thus, this article proposes a feature transformation method based central behavior to construct informative feature sets. Transformation method based central behavior only requires small amount of records to extract hotspots/hot-times information for every user, and projects original records into a relative vector space, of which coordinates represent the effects suffered from corresponding centralities (hotspots/hot-times). Then, the new space is further enriched by statistical summaries related to hotspots/hot-times. Based on the state-of-the-art classification algorithms, the proposed transformation method based central behavior is validated on a large Usage Detail Records dataset generated in real physical world. Results show that features generated by transformation method based central behavior surpass traditional spatial-temporal features and preference in the terms of precision, recall, and f1-score.

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Section: Temporalitysupporting

confidence: 77%

TCB: A feature transformation method based central behavior for user interest prediction on mobile big data

Zhou

Jiang

Chen

et al. 2016

International Journal of Distributed Sensor Networks

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show abstract

“…Among these collective behaviors, the online user behavior got more and more attention 12 14 15 16 17 18 19 20 21 22 23 with the rapid development of Internet. In addition to the bursty nature of inter-event time, efforts also have been paid to investigate the behavior itself, such as the social influence of the decision making when installing online applications 12 13 and the anchoring bias of online rating 14 .…”

mentioning

confidence: 99%

Memory effect of the online user preference

Hou

Xue

Guo

et al. 2014

Sci Rep

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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).

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“…Firstly, while the consumers' historical behaviours are normally regarded as one of the most influential factor driving them further engage in the posting behaviour, we found that actually their recent behaviour, rather than the history as a whole, strongly influence the posting possibility. Consumer behaviour such as posting and rating always exhibit a strong memory effect (Pan et al, 2014;Hou et al, 2014) that they tend to behave in a certain pattern in a short period. As a consequence, the number of reviews posted during the past week is found to be the most influential one ( 2 = 4.609) among all the considered factors.…”

Section: Contributionsmentioning

confidence: 99%

Do reviews from friends and the crowd affect online consumer posting behaviour differently?

Xue

Hou

Liu

et al. 2018

Electronic Commerce Research and Applications

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User-generated reviews are valuable resources for consumers to gain information of products which has significant impact on their following decision-making. With the development of social network service, consumers are exposed to reviews coming from both friends and the crowds (non-friends). However, the impact of friends' and crowds' reviews on consumer posting behaviour has not been well differentiated. Using the online review information as well as the underlying social network from Yelp, this paper develops a multilevel mixed effect probit model to study the impact of consumer characteristics and reviews of different sources, i.e. friends or crowds, on the possibility of consumer further engaging in posting behaviour. Despite the common perception that the volume, valance and variance of reviews significantly impact the possibility of following posting behaviour, we show that such influence majorly comes from the friend reviews. The volume of friend reviews has much stronger impact on the target user's posting behaviour than that of the crowds. The valance and variance of the crowd reviews show no significant influence when ignoring the friend reviews, but negative influence when considering it. The friend reviews and crowd reviews are further divided as positive and negative ones, and only the positive friend reviews and negative crowd review are found significantly enhancing the posting possibility.

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Long-term memories in online users' selecting activities

Cited by 21 publications

References 19 publications

TCB: A feature transformation method based central behavior for user interest prediction on mobile big data

TCB: A feature transformation method based central behavior for user interest prediction on mobile big data

Memory effect of the online user preference

Do reviews from friends and the crowd affect online consumer posting behaviour differently?

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