PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest

Latent user representations are widely adopted in the tech industry for powering personalized recommender systems. Most prior work infers a single high dimensional embedding to represent a user, which is a good starting point but falls short in delivering a full understanding of the user's interests. In this work, we introduce PinnerSage, an end-to-end recommender system that represents each user via multi-modal embeddings and leverages this rich representation of users to provides high quality personalized recommendations. PinnerSage achieves this by clustering users' actions into conceptually coherent clusters with the help of a hierarchical clustering method (Ward) and summarizes the clusters via representative pins (Medoids) for efficiency and interpretability. PinnerSage is deployed in production at Pinterest and we outline the several design decisions that makes it run seamlessly at a very large scale. We conduct several offline and online A/B experiments to show that our method significantly outperforms single embedding methods.Comment: 10 pages, 7 figure

Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation

With the uptake of algorithmic personalization in the news domain, news organizations increasingly trust automated systems with previously considered editorial responsibilities, e.g., prioritizing news to readers. In this paper we study an automated news recommender system in the context of a news organization's editorial values. We conduct and present two online studies with a news recommender system, which span one and a half months and involve over 1,200 users. In our first study we explore how our news recommender steers reading behavior in the context of editorial values such as serendipity, dynamism, diversity, and coverage. Next, we present an intervention study where we extend our news recommender to steer our readers to more dynamic reading behavior. We find that (i) our recommender system yields more diverse reading behavior and yields a higher coverage of articles compared to non-personalized editorial rankings, and (ii) we can successfully incorporate dynamism in our recommender system as a re-ranking method, effectively steering our readers to more dynamic articles without hurting our recommender system's accuracy.Comment: To appear in UMAP 202

Temporal models for mining, ranking and recommendation in the Web

Due to their first-hand, diverse and evolution-aware reflection of nearly all areas of life, heterogeneous temporal datasets i.e., the Web, collaborative knowledge bases and social networks have been emerged as gold-mines for content analytics of many sorts. In those collections, time plays an essential role in many crucial information retrieval and data mining tasks, such as from user intent understanding, document ranking to advanced recommendations. There are two semantically closed and important constituents when modeling along the time dimension, i.e., entity and event. Time is crucially served as the context for changes driven by happenings and phenomena (events) that related to people, organizations or places (so-called entities) in our social lives. Thus, determining what users expect, or in other words, resolving the uncertainty confounded by temporal changes is a compelling task to support consistent user satisfaction. In this thesis, we address the aforementioned issues and propose temporal models that capture the temporal dynamics of such entities and events to serve for the end tasks. Specifically, we make the following contributions in this thesis: (1) Query recommendation and document ranking in the Web - we address the issues for suggesting entity-centric queries and ranking effectiveness surrounding the happening time period of an associated event. In particular, we propose a multi-criteria optimization framework that facilitates the combination of multiple temporal models to smooth out the abrupt changes when transitioning between event phases for the former and a probabilistic approach for search result diversification of temporally ambiguous queries for the latter. (2) Entity relatedness in Wikipedia - we study the long-term dynamics of Wikipedia as a global memory place for high-impact events, specifically the reviving memories of past events. Additionally, we propose a neural network-based approach to measure the temporal relatedness of entities and events. The model engages different latent representations of an entity (i.e., from time, link-based graph and content) and use the collective attention from user navigation as the supervision. (3) Graph-based ranking and temporal anchor-text mining inWeb Archives - we tackle the problem of discovering important documents along the time-span ofWeb Archives, leveraging the link graph. Specifically, we combine the problems of relevance, temporal authority, diversity and time in a unified framework. The model accounts for the incomplete link structure and natural time lagging in Web Archives in mining the temporal authority. (4) Methods for enhancing predictive models at early-stage in social media and clinical domain - we investigate several methods to control model instability and enrich contexts of predictive models at the “cold-start” period. We demonstrate their effectiveness for the rumor detection and blood glucose prediction cases respectively. Overall, the findings presented in this thesis demonstrate the importance of tracking these temporal dynamics surround salient events and entities for IR applications. We show that determining such changes in time-based patterns and trends in prevalent temporal collections can better satisfy user expectations, and boost ranking and recommendation effectiveness over time