Item recommendation on monotonic behavior chains

By Mengting Wan, Julian McAuley. ACM Conference on Recommender Systems, 2018

315 citations recorded by the source corpus.

'Explicit' and 'implicit' feedback in recommender systems have been studied for many years, as two relatively isolated areas. However many real-world systems involve a spectrum of both implicit and explicit signals, ranging from clicks and purchases, to ratings and reviews. A natural question is whether implicit signals (which are dense but noisy) might help to predict explicit signals (which are sparse but reliable), or vice versa. Thus in this paper, we propose an item recommendation framework which jointly models this full spectrum of interactions. Our main observation is that in many settings, feedback signals exhibit monotonic dependency structures, i.e., any signal necessarily implies the presence of a weaker (or more implicit) signal (a 'review' action implies a 'purchase' action, which implies a 'click' action, etc.). We refer to these structures as 'monotonic behavior chains,' for which we develop new algorithms that exploit these dependencies. Using several new and existing datasets that exhibit a variety of feedback types, we demonstrate the quantitative performance of our approaches. We also perform qualitative analysis to uncover the relationships between different stages of implicit vs. explicit signals.

Subfields: Information Systems, Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e291788bc51d7e8c51c6f4701dcd954a80dafe61

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Xue Liu
2. Peng Kang
3. Chen Ma
4. Wayne Xin Zhao
5. Ji-rong Wen

In Management Science and Operations Research

1. Xue Liu
2. Peng Kang
3. Chen Ma

In Statistical and Nonlinear Physics

1. Xue Liu
2. Peng Kang
3. Chen Ma

Citations in the corpus

• Detect and Perturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-based Decoding (2021, unknown): “GoodReads a book review dataset (Wan and McAuley, 2018) containing user reviews, star ratings, and genres.”
• Hierarchical Gating Networks for Sequential Recommendation (2019, methodology): “atasets The proposed model is evaluated on five real-world datasets from variousdomainswithdifferentsparsities:MovieLens-20M [6],AmazonBooks and Amazon-CDs [9], Goodreads-Children and GoodreadsComics [38]. MovieLens-20M is a user-movie dataset collected from the MovieLens website, where this dataset”