Open AccessPosted Content
Diversifying Music Recommendations
TLDR
A submodular approach incorporates item relevance score within its optimization function, and produces a relevant and uniformly diverse set of recommendations to diversify Amazon Music recommendations.Abstract:
We compare submodular and Jaccard methods to diversify Amazon Music recommendations. Submodularity significantly improves recommendation quality and user engagement. Unlike the Jaccard method, our submodular approach incorporates item relevance score within its optimization function, and produces a relevant and uniformly diverse set.read more
Citations
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Proceedings ArticleDOI
Practical Diversified Recommendations on YouTube with Determinantal Point Processes
TL;DR: This work presents a statistical model of diversity based on determinantal point processes (DPPs), and empirical results show that this model, coupled with a re-ranking algorithm, yields substantial short- and long-term increases in user engagement.
Proceedings ArticleDOI
An Efficient Bandit Algorithm for Realtime Multivariate Optimization
TL;DR: In this paper, the authors focus on multivariate optimization of interactive web pages and apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in real-time.
Proceedings ArticleDOI
Adaptive, Personalized Diversity for Visual Discovery
Choon Hui Teo,Houssam Nassif,Daniel Hill,Sriram Srinivasan,Mitchell Goodman,Vijai Mohan,S. V. N. Vishwanathan +6 more
TL;DR: This work explores extensions in the direction of adaptive personalization and item diversification within Stream, a new form of visual browsing and discovery by Amazon, and presents the user with a diverse set of interesting items while adapting to user interactions.
Proceedings ArticleDOI
An Efficient Bandit Algorithm for Realtime Multivariate Optimization.
TL;DR: This work formulates an approach where the possible interactions between different components of the page are modeled explicitly and applies bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime.
Proceedings ArticleDOI
Adaptive, Personalized Diversity for Visual Discovery
Choon Hui Teo,Houssam Nassif,Daniel Hill,Sriram Srinavasan,Mitchell Goodman,Vijai Mohan,S. V. N. Vishwanathan +6 more
TL;DR: In this paper, the authors explore extensions in the direction of adaptive personalization and item diversification within Stream, a new form of visual browsing and discovery by Amazon, which presents the user with a diverse set of interesting items while adapting to user interactions.
References
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Journal Article
Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering.
TL;DR: This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.
Journal ArticleDOI
Amazon.com recommendations: item-to-item collaborative filtering
TL;DR: Item-to-item collaborative filtering (ITF) as mentioned in this paper is a popular recommendation algorithm for e-commerce Web sites that scales independently of the number of customers and number of items in the product catalog.
Journal ArticleDOI
An analysis of approximations for maximizing submodular set functions--I
TL;DR: It is shown that a “greedy” heuristic always produces a solution whose value is at least 1 −[(K − 1/K]K times the optimal value, which can be achieved for eachK and has a limiting value of (e − 1)/e, where e is the base of the natural logarithm.
Posted Content
An analysis of approximations for maximizing submodular set functions II
TL;DR: In this article, the authors considered the problem of finding a maximum weight independent set in a matroid, where the elements of the matroid are colored and the items of the independent set can have no more than K colors.