Inferring Networks of Substitutable and Complementary Products

McAuley, Julian; Pandey, Rahul; Leskovec, Jure

doi:10.1145/2783258.2783381

Cited by 537 publications

(334 citation statements)

References 29 publications

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“…In this paper, we use the Amazon [7], [22] and Yelp 4 datasets. Amazon dataset originally contains 142.8 million product reviews ranging from May 1996 to July 2014 and Yelp dataset contains 4.7 million product reviews ranging from July 2004 to January 2017.…”

Section: Data Preparationmentioning

confidence: 99%

Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

Bai

Zhao

et al. 2018

IEEE Trans. Knowl. Data Eng.

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Abstract-Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this paper, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers' ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines.

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Section: Data Preparationmentioning

confidence: 99%

Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

Bai

Zhao

et al. 2018

IEEE Trans. Knowl. Data Eng.

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“…Four of these datasets are obtained from Amazon [8], [9], three from MovieLens [10], [11], and one from Netflix [12]. These eight datasets used in our experiments (a) contain reliable timestamps (most of the ratings within each dataset have been entered in real rating time and not in a batch mode), (b) are up to date (published between 1998 and 2016), (c) are widely used as benchmarking datasets in CF research and (d) vary with respect to type of dataset (movies, music, videogames and books) and size (from 2MB, up to 4.7GB).…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The proposed algorithm, as well as the two algorithms presented in [7], are based on the exploitation of timestamp information which is associated with ratings; hence in this work, we use the Amazon datasets [8], [9], the MovieLens datasets [10], [11] and the Netflix dataset [12], which include the ratings' timestamps. It is worth noting that the proposed algorithm can be combined with other techniques that have been proposed for either improving prediction accuracy in CF-based systems, including consideration of social network data (e.g.…”

Section: Introductionmentioning

confidence: 99%

Using Time Clusters for Following Users’ Shifts in Rating Practices

Margaris

Vassilakis

2018

J. Complex Syst. Inform. Model. Q.

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Abstract. Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user collaborative filtering, namely, the Pearson Correlation, which adjusts each individual user rating by the mean value of the ratings entered by the specific user, when computing similarities. However, a user's rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa. In that sense, the practice of using a single mean value for adjusting users' ratings is inadequate, since it fails to follow such shifts in users' rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in useruser collaborative, filtering implementations. The proposed algorithm incorporates the aforementioned concept and is able to follow more successfully shifts in users' rating practices. It has been evaluated using numerous datasets, and has been found to introduce significant gains in rating prediction accuracy, while outperforming the dynamic average computation approaches that are presented earlier.

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“…[30] in his Probabilistic polyadic factorization and its application to personalized recommendation demonstrated the effectiveness of directly modelling all the dimensions simultaneously in a unified framework. These among other works presupposes that, tensor decomposition models performed well in terms of prediction efficiency and effectiveness compared to the various matrix factorization algorithms, in particular application to massive data processing [31]- [33]. However, the numerous literature concerning the subject.…”

Section: Related Workmentioning

confidence: 99%