- (Lee et al., 2014) ⇒ Pei Lee, Laks V.S. Lakshmanan, Mitul Tiwari, and Sam Shah. (2014). “Modeling Impression Discounting in Large-scale Recommender Systems.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623356
Subject Headings: Multi-Session Item Recommendation.
- Presentation slides: https://slideshare.net/mitultiwari/impression-kdd
- QUOTE: ... Impression discounting relies on learning a model for users based on their historical engagement (i.e. impressions) with recommendations over a certain period of time. You can think of it as a re-ranking plugin for a recommender, where it’s implemented either in the Recommendation Post-Processing or Online Modules components, or even in both. The model, when applied to a list of recommendations for a user will then penalize (i.e. discount) items that have not been engaged with and change the ordering of the items in the list. Items that have previously been impressed but not interacted with will be pushed down the list, allowing other items to bubble up. The hard part of this process is training the model so that it discounts items by the correct amount, neither being too harsh or lenient. ...
Recommender systems have become very important for many online activities, such as watching movies, shopping for products, and connecting with friends on social networks. User behavioral analysis and user feedback (both explicit and implicit) modeling are crucial for the improvement of any online recommender system. Widely adopted recommender systems at LinkedIn such as “People You May Know " and " Endorsements " are evolving by analyzing user behaviors on impressed recommendation items.
In this paper, we address modeling impression discounting of recommended items, that is, how to model user's no-action feedback on impressed recommended items. The main contributions of this paper include (1) large-scale analysis of impression data from LinkedIn and KDD Cup; (2) novel anti-noise regression techniques, and its application to learn four different impression discounting functions including linear decay, inverse decay, exponential decay, and quadratic decay; (3) applying these impression discounting functions to LinkedIn's “People You May Know " and " Endorsements “recommender systems.
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|2014 ModelingImpressionDiscountingin||Pei Lee|
Laks V.S. Lakshmanan
|Modeling Impression Discounting in Large-scale Recommender Systems||10.1145/2623330.2623356||2014|
|Author||Pei Lee +, Laks V.S. Lakshmanan +, Mitul Tiwari + and Sam Shah +|
|proceedings||Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +|
|title||Modeling Impression Discounting in Large-scale Recommender Systems +|