Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests

A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users. Two models with disjoint and hybrid payoffs are considered to characterize the phenomenon that users' preferences towards different items vary differently over time. In the disjoint payoff model, the reward of playing an arm is determined by an arm-specific preference vector, which is piecewise-stationary with asynchronous and distinct changes across different arms. An efficient learning algorithm that is adaptive to abrupt reward changes is proposed and theoretical regret analysis is provided to show that a sublinear scaling of regret in the time length $T$ is achieved. The algorithm is further extended to a more general setting with hybrid payoffs where the reward of playing an arm is determined by both an arm-specific preference vector and a joint coefficient vector shared by all arms. Empirical experiments are conducted on real-world datasets to verify the advantages of the proposed learning algorithms against baseline ones in both settings.Comment: Accepted by AAAI 2

A Contextual-Bandit Approach to Personalized News Article Recommendation

Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation. In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks. The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.Comment: 10 pages, 5 figure

HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation

In real-world streaming recommender systems, user preferences often dynamically change over time (e.g., a user may have different preferences during weekdays and weekends). Existing bandit-based streaming recommendation models only consider time as a timestamp, without explicitly modeling the relationship between time variables and time-varying user preferences. This leads to recommendation models that cannot quickly adapt to dynamic scenarios. To address this issue, we propose a contextual bandit approach using hypernetwork, called HyperBandit, which takes time features as input and dynamically adjusts the recommendation model for time-varying user preferences. Specifically, HyperBandit maintains a neural network capable of generating the parameters for estimating time-varying rewards, taking into account the correlation between time features and user preferences. Using the estimated time-varying rewards, a bandit policy is employed to make online recommendations by learning the latent item contexts. To meet the real-time requirements in streaming recommendation scenarios, we have verified the existence of a low-rank structure in the parameter matrix and utilize low-rank factorization for efficient training. Theoretically, we demonstrate a sublinear regret upper bound against the best policy. Extensive experiments on real-world datasets show that the proposed HyperBandit consistently outperforms the state-of-the-art baselines in terms of accumulated rewards

Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience

In the realm of e-commerce, popular platforms utilize widgets to recommend advertisements and products to their users. However, the prevalence of mobile device usage on these platforms introduces a unique challenge due to the limited screen real estate available. Consequently, the positioning of relevant widgets becomes pivotal in capturing and maintaining customer engagement. Given the restricted screen size of mobile devices, widgets placed at the top of the interface are more prominently displayed and thus attract greater user attention. Conversely, widgets positioned further down the page require users to scroll, resulting in reduced visibility and subsequent lower impression rates. Therefore it becomes imperative to place relevant widgets on top. However, selecting relevant widgets to display is a challenging task as the widgets can be heterogeneous, widgets can be introduced or removed at any given time from the platform. In this work, we model the vertical widget reordering as a contextual multi-arm bandit problem with delayed batch feedback. The objective is to rank the vertical widgets in a personalized manner. We present a two-stage ranking framework that combines contextual bandits with a diversity layer to improve the overall ranking. We demonstrate its effectiveness through offline and online A/B results, conducted on proprietary data from Myntra, a major fashion e-commerce platform in India.Comment: Accepted in Proceedings of Fashionxrecys Workshop, 17th ACM Conference on Recommender Systems, 202